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dissertation entitled

Pre-Training Context Effects: Training Assignment as
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presented by

Miguel Angel Quinones

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PRE-TRAINING CONTEXT EFFECTS: TRAINING

ASSIGNMENT AS FEEDBACK

By

Miguel Angel Quifiones

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Psychology

1993

ABSTRACT
Pius-TRAINING CONTEXT EFFECTS: TRAINING
ASSIGNMENT AS FEEDBACK
By

Miguel Angel Quiiiones

This study examined the extent to which the framing of training program
assignments affects training outcomes. A model was developed suggesting that training
assignment can provide feedback regarding past performance and result in different
attitudinal and motivational levels going into training. These differences in attitudes and
modvation were hypothesized to affect various affective as well as performance training
outcomes.

Undergraduate students (n = 163) participated in the study examing the
effectiveness of a training program designed to improve performance on a computer Air
Defense Task. Participants were randomly assigned to two differently framed training
programs (remedial vs advanced). An additional 54 undergraduates participated in the
study but did not receive any framing information. This group served as a control group in
various exploratory analyses. The participants in the experimental groups were told that
their assignment was made based on their level of performance during an initial ability
measure. Furthermore, the participants were told that the training programs were
specifically designed to improve the performance of high and low ability individuals,
depending on their training assignment.

The results showed that a number of individual characteristics moderated the
relationship between training assignments and pre-training characteristics. In addition,
motivation to learn was found to be positively related to various training performance

outcomes. Implications for training effectiveness research and practice are discussed.

ACKNOWLEDGMENTS

I must start out by saying that my years spent here at Michigan State University
(1988 - 1993) have been some of the best years of my life. I have met pe0ple and seen
places which I will never forget. First, my development as an Industrial/Organizational
Psychologist is due largely to the outStanding faculty here at MSU. Kevin Ford, my
committee chair, has been my closest and most important mentor and is primarily
responsible for my training (no pun intended). Kevin and I have developed an excellent
working relationship and I consider him a colleague and a true friend. Thanks a lot for
everything Kevin. Neal Schmitt, who was also on my Committee, has been invaluable for
my intellectual deve10pment. I can't even count the times I have barged into his office to
discuss a number of professional and personal issues. I think Neal is the model by which
all academics should be measured. His intelligence, work ethic, and laid back attitude
make him one of the best professors I will probably ever meet. Mike Lindell, also on my
committee, has shown me that cool people can be successful academics. Steve Kozlowski
was the first person that ever sat me down and showed me how to be a clear conceptual
thinker. Finally, Dan Ilgen has been a great friend and colleague. I will always treasure
our numerous discussions.

I would also like to thank John Hollenbeck who served on my committee and
everyone else at the Team Effectiveness Lab. Jen Hedlund, Jean Phillips, Doug Sego,
Chuck Capps, Dave Waldschmidt, Dale Tuttle (my friend indeed), Spence Tower, and
Linda Barrett were all there for me when I discovered that my data had been lost. This
project would never have been completed without them. I would also like to thank Dan and

John for their financial assistance during this project.

There also a number of individuals who have contributed immensely to my
development. My classmates Stephen Gilliland and Jeff Schneider were the best. We
stuck together through thick and thin and managed to get out of grad school in one piece.
Keith Hattrup has been, and continues to be, a good friend and colleague. Jose Cortina
and Ron Landis have managed to make MSU a fun place to be. Finally, I want to thank
my parents and my wife Sherry. They have always believed in me. My successes are their

SUCCCSSCS.

iv

TABLE OF CONTENTS

Page

LIST OF TABLES ............................................................................ viii
LIST OF FIGURES .......................................................................... xi
INTRODUCTION ............................................................................ 1
Framing .................................................................................... 5
Pre-Training Factors ...................................................................... 8
Context ................................................................................ 8
Training Assignment ............................................................ 8
Framing .................................................... 10

Training Assignment as Feedback ............................................. 11
Self-Efficacy .......................................................................... 12
Attributions ....................................................................... 16

Locus of Control ................................................................. 17
Self-Esteem ....................................................................... 18

Cone l u 5 ion ........................................................................ 20

Motivation ............................................................................. 21
Fairness Perceptions ................................................................. 23
Framework for the Current Research ................................................... 25
Pre-Training Context ................................................................. 26
Training Outcomes ................................................................... 27
Individual Differences ............................................................... 28

A Conceptual Model ................................................................. 29
Moderating Hypotheses ......................................................... 29

Structural Hypotheses ........................................................... 32

METHOD ..................................................................................... 40
Participants ................................................................................ 40
Design ...................................................................................... 4O
Task ..................................................................................... 40
Training Course ........................................................................... 43
Measures ................................................................................... 44
Locus of Control ..................................................................... 44
Self-Esteem ........................................................................... 44
Pre-Training Knowledge ............................................................ 46
Pre-Trainin g Performance ........................................................... 46
Perceived Performance .............................................................. 46
Expected Assignment ................................................................ 46
Attrrbutrons 46
Pre-Trainin g Self-Efficacy .......................................................... 47
Fairness Perceptions ................................................................. 47

V

Motivation to Learn .................................................................. 47

Post-Training Knowledge ........................................................... 47
Post-Trainin g Performance .......................................................... 48
POSt-Training Self-Efficacy ......................................................... 48
Learning ............................................................................... 48
Number of Trials ..................................................................... 48
Accuracy....". ............................ ’ .............................................. 4 8
Behavior ............................................................................... 49
Reactions .............................................................................. 49
Procedure .................................................................................. 49
Analytic Strategy .......................................................................... 51
RESULTS ...................................................................................... 54
Descriptive Data ........................................................................... 54
Moderating Hypotheses .................................................................. 54
Pre-Training Self-Efficacy .......................................................... 61
Fairness Perceptions ................................................................. 71
Summary .............................................................................. 82
Structural Hypotheses .................................................................... 82
Motivation to Learn .................................................................. 83
Performance Outcomes .............................................................. 83
Affective Outcomes .................................................................. 93
Summary .............................................................................. 95
Control Group Comparisons ............................................................ 97
Pre—Trainin g Characteristics ......................................................... 97
Performance Outcomes .............................................................. 97
Affective Outcomes .................................................................. 97
DISCUSSION ................................................................................. 102
Summary of Results ...................................................................... 102
Moderating Hypotheses ............................................................. 102
Structural Hypotheses ............................................................... 105
Control Group Comparisons ........................................................ 109
Implications and Directions for Future Research ...................................... 109
Frami n g ................................................................................ 1 10

Needs Assessment ................................................................... 113
Motivation to Learn .................................................................. 1 13
Fairness Perceptions ................................................................. 114

Study Limitations ......................................................................... 115
Conclusion ................................................................................. 116
LIST OF REFERENCES .................................................................... 117
APPENDIX A: Training Materials .................................... ‘. .................... 130
APPENDIX B: Locus of Control 1 Scale ................................................. 145
APPENDIX C: Rotter's (1966) Locus of Control Scale ................................. 146
APPENDIX D: Self-Esteem Scale .......................................................... 149
APPENDIX E: Pre-Training Knowledge Measure ....................................... 150
APPENDIX F: Pre-Training Performance Measure ...................................... 154

vi

APPENDIX G: Perceived Performance Scale .......... ' .................................. 164

APPENDIX H: Expected Training Assignment .......................................... 165
APPENDIX 1: Attributions Scale ........................................................... 166
APPENDIX J: Pre-Training Self-Efficacy Scale ......................................... 168
APPENDIX K: Fairness Perceptions Scale ............................................... 169
APPENDIX L: Motivation to Learn Scale ................................................. 171
APPENDIX M: Learning Measure ......................................................... 172
APPENDIX N: Training Reactions ........................................................ 175
APPENDIX 0: Participant Consent Forms ............................................... 176
APPENDIX P: General InstruCtions ....................................................... 178
APPENDIX Q: Participant Debriefing Forms ............................................. 184

LIST OF TABLES

Page
Table 1. Overview of Study Variables ..................................................... 45
Table 2. Means, SDS, and Reliabilities of Study Variables: Experimental Groups... 55

Table 3. Means, SDS, and Reliabilities of Study Variables: Control Groups ......... 57
Table 4. lntercorrelations Among Study Variables: Experimental Groups ............ 58
Table 5. lntercorrelations Among Study Variables: Control Groups ................... 60

Table 6. Regression Analyses Results of the Test of Moderation of Locus of

Control 1 on Assignment, Pre-Training Self-Efficacy Relationship ........ 62
Table 7. Regression Analyses Results of the Test of Moderation of Locus of

Control 2 on Assignment, Pre-Training Self-Efficacy Relationship ........ 63
Table 8. Regression Analyses Results of the Test of Moderation of Luck I

Attributions on Assignment, Pre-Training Self-Efficacy Relationship ..... 64
Table 9. Regression Analyses Results of the Test of Moderation of Effort

Attributions on Assignment, Pre-Training Self-Efficacy Relationship ..... 65
Table 10. Regression Analyses Results of the Test of Moderation of Task

Difficulty Attributions on Assignment, Pre-Training Self-Efficacy

Relationship ........................................................................ 67
Table 11. Regression Analyses Results of the Test of Moderation of Ability

Attributions on Assignment, Pre-Training Self-Efficacy Relationship ..... 69
Table 12. Regression Analyses Results of the Test of Moderation of Self-Esteem

on Assignment, Pre-Training Self-Efficacy Relationship .................... 73

viii

Table 13.

Table 14.

Table 15.

Table 16.

Table 17.

Table 18.

Table 19.

Table 20.

Table 21.

Table 22.

Table 23.

Table 24.

Table 25.

Regression Analyses Results of the Test of Moderation of Perceived
Performance on Assignment, Distributive Justice Relationship .............
Regression Analyses Results of the Test of Moderation of Perceived
Performance on Assignment, Procedural Justice Relationship ..............
Regression Analyses Results of the Test of Moderation of Expected
Assignment on Assignment, Disuibutive Justice Relationship ..............
Regression Analyses Results of the Test of Moderation of Expected
Assignment on Assignment, Procedural Justice Relationship ...............
Regression Results of the Effects of Distributive Justice, Procedural
Justice, and Pre-Training Self-Efficacy, on M0tivation to Learn ............
Hierarchical Regression Results of the Test of Mediation of

Mouvation to Learn on Pom-Training Knowledge ............................
Hierarchical Regression Results of the Test Of Mediation of

Motivation to Learn on Learning ................................................
Hierarchical Regression Results of the Test of Mediation of

Motivation to Learn on Behavior ................................................
Hierarchical Regression Results of the Test of Mediation of

M0tivation to Learn on Post—Trainin g Performance ...........................
Hierarchical Regression Results of the Test of Mediation of

Motivation to Learn on Accuracy ................................................
Hierarchical Regression Results of the Test of Mediation of

Motivation to Learn on Number of Trials ......................................
Regression Results of the Effects of Fairness Perceptions and
Performance Outcomes on Training Reactions ................................
Regression Results of the Effects of Fairness Perceptions and

Performance Outcomes on Post-Training Self-Efficacy ......................

74

76

78

80

85

86

87

88

90

91

92

94

96

Table 26. Regression Results of Control Group Comparisons:

Pre-Training Characteristics ..................................................... 99

Table 27. Regression Results of Control Group Comparisons:
Performance Outcomes ........................................................... 100

Table 28. Regression Results of Control Group Comparisons:
101

Affective Outcomes ...............................................................

LIST OF FIGURES

Page

Figure 1. A Conceptual Model .............................................................. 35
Figure 2. Hypothesized Relationship Between Training Assignment,Locus of

Control, and Pre-Training Self-Efficacy ...................................... 36
Figure 3. Hypothesized Relationship Between Training Assignment,

Self-Esteem, and Pre-Training Self-Efficacy ................................. 37
Figure 4. Hypothesized Relationship Between Training Assignment, Perceived

Performance, and Fairness Perceptions ....................................... 38
Figure 5. Hypothesized Relationship Between Training Assignment, Expected

Assignment, and Fairness Perceptions ........................................ 39
Figure 6. Relationship Between Assignment, Effort Attributions,

and Pre-Training Self-Efficacy ................................................. 66
Figure 7. Relationship Between Assignment, Task Difficulty Attributions,

and Pre-Training Self-Efficacy ................................................. 68
Figure 8. Relationship Between Assignment, Ability Atuibutions,

and Pre-Training Self-Efficacy ................................................. 70
Figure 9. Relationship Between Assignment, Perceived Performance,

and Distributive Justice .......................................................... 75
Figure 10. Relationship Between Assignment, Perceived Performance,

and Procedural Justice ........................................................... 77

Figure 11. Relationship Between Assignment, Expected Assignment,
and Distributive Justice .......................................................... 79
Figure 12. Relationship Between Assignment, Expected Assignment,

and Procedural Justice ........................................................... 81

xii

INTRODUCTION

Training has become a critical component of an organization's competitive edge
(Rosow & Zager, 1988). Changes in technology have forced companies to develop
training programs to keep up with these rapid advances. In fact, successful organizations
of the future will be what can be called "leaming organizations" or organizations that better
adapt to changes through their ability to learn. This is reflected in continuous learning
programs that are springing up in companies throughout the counu'y (Noe & Ford, 1992).

Changes in the demographic characteristics of the workforce are also Causing
organizations to reconsider their human resources strategies (GoldSIein & Gilliam, 1990).
A shrinking labor force is forcing companies to focus on worker retraining as a way of
maintaining a productive workforce. These trends toward increased technological
advances, global competition, and a changing workforce, highlight the need for research on
training effectiveness. This becomes even more important when one considers that of the
billions of dollars spent annually in training, only about ten percent of these expenditures
result in performance changes back on the job (Georgenson, 1982).

Past research on training effectiveness has tended to focus on training design
elements such as identical elements,.task repetition, and training delivery (see Connier &
Hagman, 1987; Goldstein, 1986). This led to a number of studies examining the impact of
various training designs on knowledge and skill acquisition. While providing valuable
information regarding training effeCtiveness, these studies have been relatively narrow in
scope and have usually examined simple psychomotor tasks. Recent reviews of the
training literature have lamented this apparent lack of theoretical development in the field

and have called for a paradigm shift which begins to look at training in a more integrated

1

and complex way (cf..Tannenbaum & Yukl, 1992). More recently a few theoretical
frameworks of training have been developed which take into account other factors in
addition to training design.

Baldwin & Ford (1988) pr0posed that training effectiveness is a function of trainee
characteristics, training program characteristics, and environmental variables. Individual
level variables such as trainee motivation (Noe & Schmitt, 1986), self-efficacy (Gist,
Stevens, & Bavetta, 1991), and ability (Ree & Earles, 1991) have been shown to be related
to training outcomes. Researchers have also noted the importance of environmental factors
such as workgroup climate (Ford, Quifiones, Sego, & Sorra, 1992; Rouillier & Goldstein,
1991) and supervisor support (Marx, 1982) on the transfer of training. These variables
represent what Goldstein (1986) referred to as "climate for transfer".

Noe (1986) highlighted the importance of pre-training trainee attitudes and attributes
in affecting training outcomes. Other pre-training factors such as trainee expectations have
also been investigated (Tannenbaum, Mathieu, Salas, &- Cannon-Bowers, 1991). In
addition to pre-u'aining individual characteristics, a number of contextual factors prior to
training have also proven to be important in the training process. For example, Baldwin,
Magjuka, & Loher (1991) showed that participation in the decision making process prior to
training can affect individual's attitudes and morivation going into training. These studies
demonstrate that training effectiveness is more than just a funCIion of training design.
Factors prior to, during, and after training can all have an impact on training effectiveness.
The present study focuses on pro-training characteristics and their impact on training
outcomes.

The way in which an organization labels or frames a training program is one factor
which can affect pre-training attitudes and motivation. Martocchio (1992) showed that
framing a computer u'aining program as an opportunity had an impact on uaining

outcomes. Trainees who believed that the training program will help enhance their career

were more likely to perform better in training. So clearly, organizations must pay more
attention to pre-training contextual factors which can serve to enhance or undermine any
benefits that they may hope to gain by implementing a new training program.

One pre-training contextual factor that has received little attention in the literature is
the labeling of the purpose for attending the mining program. Organizations often conduct
training to make up for deficiencies in the skills of individuals (Goldstein, 1992). This
type of training can be referred to as remedial training. On the other hand, training
assignments can sometimes indicate that an individual is performing well and is being
groomed for promotion or higher responsibility (Howard & Bray, 1988). In this case,
individuals may be assigned to what can be called advanced training. These labels can
frame or place a context around an individual's assignment to training and result in
differences in attitudes and motivation going into training.

Another factor which makes the study of labels attached to training programs more
critical is the increased attention being devoted to aptitude/treatment interactions (cf.
Ackennan, 1989; Kanfer & Ackerrnan, 1989). An aptitude/treatment interaction approach
recognizes that different ability individuals may learn better under certain types of training
program designs. However, assigning different ability individuals to different training
programs requires the categorization of both, u'ainees, as well as training programs. Even
if relatively neutral labels are used to classify trainees and training programs, these labels
are likely to become very value laden as individuals in an organization begin to recognize
which individuals are being assigned to what types of training. Furthermore, the very act
of categorization serves to cue up differences between individuals as well as training
programs. If aptitude/treatment interactions are to be implemented widely, more research is
necessary which investigates the potential effects of assigning different trainees to

differently labeled training programs.

Therefore, aside from the objective purpose of the training program, labeling a
training program is likely to have an impact on an individual's attitudes and motivation
going into the training program. The labeling of the training program may communicate
information about a trainee's performance, ability, motivation, expectations, and
attributions for their past performance. Because these labels carry with them such a high
level of evaluative baggage, it is important to understand their effects on trainees and
training outcomes (Noe, 1986).

Research on human decision making has shown that the way a problem is framed
(gain vs loss) has an impact on the decision strategy used (Kahnemann & Tversky, 1979).
In addition, research in the education literature suggests that children attend to the
expectations that adults have regarding their performance and achieve accordingly
(Rosenthal & Jacobson, 1968). This phenomenon has been called the pygmalion effect
(Ross & Nisbett, 1991). In addition, there is evidence to suggest that feedback, especially
negative feedback, can have motivational as well as affective consequences (Il gen, Fisher,
& Taylor, 1979; Martocchio & Webster, 1992). Therefore, the label assigned to a training
program can have unintended effects on training outcomes by communicating information
to the trainee which, in turn, affects their pre-training attitudes and motivation.

The present study expands our knowledge of the effeCIs of pre-training contextual
factors on training outcomes. The study examines the extent to which framing the purpose
of attending a training program affects training outcomes. Specifically, the effects of
assigning an individual to training because they have been performing poorly (remedial
training) as opposed to assigning them for superior performance (advanced training) are
examined. The mechanisms by which the framing of training assignment can affect
training outcomes are identified and used to develop a number of research hypotheses.

The study is organized in the following manner. First, an overview of the literature

on framing is presented. Second, a review and discussion of the literature investigating

pre-training factors is presented. Third, theories describing the mechanisms by which pre-
training context can affect trainee attitudes and motivation are reviewed Fourth, potential
individual differences which may moderate the relationship between pre-training context
and training outcomes are discussed. Finally, a conceptual model and hypotheses derived

from this model regarding the expected effects of uaining assignment are presented.

Framing

A great deal of attention has been dev0ted in the psychological literature to studying
the effects of framing. In its general form, framing refers to an individual's "schema", or
cognitive representation, of a situation or set of circumstances (Srull & Wyer, 1980).
Frames have been shown to affect the encoding, processing, and retrieval of information
regarding persons and objects in a number of situations. This effect also includes
information regarding the self (Fiske & Taylor, 1991). The present study makes the
argument that training programs can have different "frames" which communicate
information to the trainee about themselves such as their past performance level by
categorizing them and assigning to a particular training program. For this reason, a brief
review of the literature on framing effects is presented.

The Study of framing effects has been most thoroughly examined in the context of
human decision making. Researchers have found that humans often do not follow
normative rational models of decision making (Einhom & Hogarth, 1981). Kahneman &
Tversky (1979) developed what is known as prospect theory to explain these departures
from rationality. The theory basically states that the way in which a problem is framed, in
addition to objective characteristics of the decision, will have an impact on the final
outcome of the decision. Research results show that individuals are more willing to take

risks when a decision is framed as a loss as Opposed to a gain.

Prospect theory hypothesizes that individuals are influenced by contextual factors
when evaluating information. Two identical propositions involving similar payoff
structures are evaluated differently dependingon whether they are framed as involving
probabilities for a gain or a loss (Dutton & Jackson, 1987). In addition to affecting
decision making, frames can also affect the encoding and retrieval process.

Contemporary models of human cognition view human perception as being guided
by organizing mental categories or "schemas" (cf. Srull & Wyer, 1980). These schemas
act to filter both incoming and outgoing information from a person's memory storage
(Quir'iones, 1990). Schemas have been shown to affect an individual's view of others as
well as themselves. Framing is believed to aetivate schemas and make them accessible
during certain situations. One way in which schemas are activated by framing is through
the use of labels (Hattrup, 1990; Srull & Wyer, 1989). Labels are descriptive words or
statements which cue up a number of related attributes. For example, a label of "librarian"
can cue up characteristics such as shy, studious, or inhibited (Hattrup, 1990).

The accessibility of schemas through labeling has been used to explain the
persistence of stereotypes in person perception (Fiske & Taylor, 1991; Hattrup, 1990).
Labels attached to others help fill in the gaps about the kinds of characteristics that go along
with the label. Common person labels include race, occupation, as well as trait
descriptions such as honest or criminal. Schemas activated through labeling have been
shown to result in biased encoding and recall which help perpetuate stereotypes (Fiske &
Taylor, 1991). Research has shown that individuals tend to encode and recall information
which is consistent with the category label (cf. Hasrie, 1980). Thus, frames activated
through labels help individuals make sense of their world by providing a unified structure
with which to perceive incoming information.

Researchers have also noted that individuals also hold schemas about themselves,

or "self-schemas" (Markus & Sentis, 1982). These self-schemas guide information

processing about the self. Self-schemas can be made salient through labels given to
individuals by Others such as teachers, employers, etc. These labels can then serve to
influence an individual's subsequent behavior. The most famous illustration of this
phenomenon is the concept of the self-fulfilling prophesy (Merton, 1948; Snyder, 1984).
The basic notion of the self-fulfilling prophesy is that labels attached to individuals can
influence other's perceptions and actions toward them which, in turn, results in the
individual behaving in ways which are consistent with this expectation (Ross & Nisbett,
1991).

The classic study on self-fulfilling prophesy was conducted by Rosenthal &
Jacobson (1968). In their study, first- and second- grade children were randomly labeled as
high or low IQ. These labels were provided to their teachers at the beginning of the school
. year. Follow up measures showed that children labeled as high IQ performed significantly
higher than those labeled as low IQ. Subsequent investigations revealed that teachers
tended to treat the high IQ labeled children differently than the low IQ children (Rosenthal
& Rubin, 1978). Teachers provided more encouragement and feedback and expected more
from children whom they believed to be smarter (Harris & Rosenthal, 1985;
Meichenbaum, Bowers, & Ross, 1969; Rosenthal, 1976, 1985).

In addition to altering other's behaviors, labels can influence a person's own
behavior. In a study conducted by Miller, Brickman, & Bolen (1975), the effects of
labeling on children's littering behavior was investigated. One classroom was assigned to a
persuasion condition which received lectures on the virtues of cleanliness and the
importance of keeping the classroom clean. A second classroom was praised and
commended for already being clean and neat. This constituted the label manipulation.
Finally, a third Classroom served as a control group. The results showed that the kids who
were told that they were neat and clean did indeed keep their classroom the cleanest and this

behavioral change persisted long after the manipulation was withdrawn.

It is clear that labels can cue up frames or schemas about Others as well as the self.
Labels attached to individuals can result in behavior that is consistent with that label or, at
least, result in a reaction to this label. Labels attached to decision outcomes can influence
an individual's decision making process. Thus, labels, to the extent that they cue up
frames or schemas, are an important aspect of human perception and behavior. However,
little is known about the labeling effects on trainees. The present study takes the viewpoint
that labels or frames can place a context around the training program which can influence a
trainee as they enter the training program. These labels can cue up self-schemas which can
affect attitudes and motivations going into training. A review of these pre-training context

factors is now presented with a focus on pre-training framing effects.

Pre—Training Factors

Context

Context refers to the circumstances surrounding an event. Recent views of training
have recognized that training is an activity which is embodied within a larger organizational
reality. Thus training has come to be seen as a subsystem of the larger organizational
human resources system (Goldstein, 1992). This study takes the view that trainees do not
arrive at the training site tabula rasa. Circumstances surrounding the reasons and ways by
which they were assigned to training can frame or put a context around the training
program. The study of the effects of pre-training context factors on training is a relatively
new phenomenon. There are, however, a growing number of studies which suggest that
pie-training context is important in affecting training outcomes. A review of this body of
research is now presented.

Training Assignment Surprisingly, little research exists on the effects of training

assignment on trainee attitudes and motivation. Training research usually takes the

assignment process as a given. However, not all trainees arrive at the training program
under equal circumstances. For example, some may have chosen to go to training while
others may have been forced to attend. The extent to which individuals have a choice in
deciding which training program to attend is one aspect of training assignment that has
received some attention. Based on the participation literature, researchers have noted that
participating in the choice process prior to training should enhance trainee's satisfaction
with training and their motivation to attend and learn during the u'ainin g program. For
example, Hicks and Klimoski (1987) found that trainees who perceived that they had
freedom to attend the training program reported higher training satisfaction and performed
better during training.

Baldwin, Magjuka, & Loher (1991) agreed that participation prior to training is
important. However, they noted that organizational realities may deny trainees the
opportunity to attend the training program that they had chosen. Their study found that
individuals who were allowed to participate in the choice process but were subsequently
denied their choice had lower pre-trainin g motivation and learned less than trainees who
were not given the opportunity to participate in the choice process at all. The highest
motivation to learn and actual learning occurred among u'ainees who were given the
opportunity to participate in the choice process and actually received the training program
which they had chosen.

These two studies provide evidence suggeSIing that the circumstances, or context,
surrounding training assignments are important determinants of training performance.
Differences in m0tivation were observed depending on the circumstances surrounding the
assignment. It appears that by allowing individuals to participate in deciding which
programs to attend, they develop expectations about the activities they will be involved in
as well as the KSAs that will be covered during training (Tannenbaum ct al., 1991). If

these expecrations are not met, trainees react in a negative way.

10

Tannenbaum et a1. (1991) conducted a study to directly test the hypothesis that
trainee expectations prior to training can influence training outcomes. They hypothesized
that, to the extent that these expectations are fulfilled, training performance as well as
reactions to training will be enhanced. In a test of their hypotheses, Naval recruits
(n=1037) indicated their expectations about the KSAs that would be covered in an
upcoming training program. At the end of training, the recruits indicated the extent to
which their expectations had been fulfilled. This measure of training fulfillment was found
to be correlated with training motivation, academic and physical self-efficacy as well as
organizational commitment at the end of training. Individuals who felt that their
expectations had been met reported higher levels of confidence in their ability to perform
some of the trained KSAs. In addition, higher fulfillment resulted in individuals having a
more favorable impression of the organization.

mining. In the present study, it is argued that training programs can have different
labels attached to them which can communicate information to trainees. Labels can act as a
frame which trainees can use to make inferences regarding the training program and the
circumstances under which they were assigned. Their assignment can cue frames which
affect trainee perceptions of themselves and their ability to do well in training.

Martocchio (1992) hypothesized that social context can frame the training situation
by emphasizing different aspects of the training program such as training content,
difficulty, and usefulness. Specifically, the study hypothesized that framing training on
microcomputer usage as an opportunity can influence a trainee's view of their capacity to
learn the training material. Full time university clerical and administrative employees
(n=84) attended a microcomputer training program which was described as either an
opportunity to increase one's competence or as neutral (i.e. no context description).
Results indicate that context had a significant effect on learning during the training

program. Those in the opportunity frame showed higher learning than those in the control

11

condition. This effect was largely due to the fact that the opportunity framing decreased
computer anxiety and indirectly increased computer efficacy beliefs.

The studies cited above provide evidence that factors present prior to trainees
entering a training program can affect training outcomes. Trainee expectations regarding
the content of the uaining program as well as the amount of choice involved in selecting a
training program appear to be important variables which must be considered when
implementing training interventions. More important to the present study, the way in
which the training program was framed showed significant effects on training performance.
Context acts to focus a trainee's attention to training content, difficulty, or usefulness, all of
which can have an influence on their motivation and attitudes.

However, no attention has been paid to the potential effects of framing the
assignment of an individual to training. Framing a training program as remedial as
opposed to advanced should also serve to cue up different evaluations of the self.
Specifically, trainees are likely to make different assessments of their capability to learn
(efficacy beliefs) depending on how the training program is framed. This sort of framing
conveys feedback regarding an individuals prior level of performance depending on the
type of training to which they are assigned. Feedback has been shown to have motivational
as well as affective outcomes (Ilgen, Fisher, & Taylor, 1979).

Training Assignment as Feedback. Clearly organizations use training programs as
rewards for good performance or as a way of indicating poor performance. So,
assignment to different training programs which convey feedback about past performance
can provide a frame or context. Traditionally, training research has investigated feedback
in the context of trial learning (Holding, 1965; Komaki, Heinzman, & Lawson, 1980).
Individuals master a task by learning from their past mistakes. However, the effects of
feedback as it relates to training motivation and performance has been relatively

unexplored.

12

While feedback has been present in many training studies, the effects of that
feedback on trainee performance remains relatively unknown. Martocchio & Webster
(1992) hypothesized that positive feedback during training should enhance training
performance while negative feedback should inhibit performance. They argue that
feedback serves as a source of self-regulatory information which triggers the expenditure of
attentional resources as a trainee progresses through the training program. This hypothesis
was supported in their study. Individuals receiving negative feedback showed less learning
over time than those receiving positive feedback. In addition, they found that positive
feedback served to increase self-efficacy while negative feedback tended to lower self-
efficacy.

Martocchio and Webster (1992) proposed that social cognitive theory provides an
appropriate framework for investigating the effects of feedback in a training setting. The
present Study is concerned with the extent to which feedback conveyed by training
assignment affects training outcomes. Therefore, a framework which links assignment,
feedback, and training outcomes is necessary. The following section explores two
potential mechanisms by which framing or" the training assignment can affect training

outcomes. These include self—efficacy and motivation.

Self-Efficacy

Bandura's (1986; 1991) social cognitive theory serves as a framework for
understanding the effects of training assignment on uaining performance. It has been
argued that training assignment, depending on how it is framed, can convey feedback
regarding a trainee's past performance. Social cognitive theory suggests that feedback can
affect task performance through the process of self-regulation (Bandura, 1977). Feedback

helps individuals compare their past performance to a standard. This evaluation then

13

determines an individual's belief in their capability to perform successfully in the future, or
self-efficacy beliefs (Bandura, 1991).

Self-efficacy (Gist & Mitchell, 1992) refers to people's beliefs that they can attain a
given level of performance. It represents an individual's cognitive representation of ability,
motivational, and situational factors that will enhance or inhibit performance (Bandura,
1977; 1982; 1986). Self-efficacy, in other words, represents an individual's belief that
they have what it takes to perform under a given set of circumstances. In a training Or
learning situation, self-efficacy refers to an individual's belief that they can master the
trained material (Schunk, 1989).

Performance feedback has been shown to affect self-efficacy. In general, the
research suggests that negative feedback serves to lower self-efficacy (Bandura, 1986).
Individuals who have performed poorly in the past are less likely to believe that they will be
successful in the future. Positive feedback, on the other hand, serves to increase self-
efficacy. Bandura (1986) argues that success experiences are one way by which
individuals can gain a sense of confidence that they will be able to perform successfully in
the future. Because the framing of training assignment is believed to convey feedback, and
feedback has been shown to affect performance through self-efficacy (cf. Martocchio &
Webster, 1992), a closer look at self-efficacy and its role in training and learning is
warranted.

Self-efficacy beliefs have three distinct characteristics (Bandura, 1977). First, self-
efficacy beliefs vary in magnitude, or the level of performance that individuals believe they
can achieve. Second, individuals differ in the certainty , or strength, of these efficacy
beliefs. Finally, self-efficacy beliefs vary in the generality of behaviors that individuals
believe are within their personal control.

Self-efficacy beliefs are a component of self-regulation (Bandura, 1991). Self-

efficacy influences motivational and effort levels as individuals regulate their behavior to

14

match a given personal Standard (Bandura & Cervone, 1986). In fact, research has found a
relationship between self-efficacy and goal level as well as goal commitment (Locke,
Fredrick, Lee, & Bobko, 1984; Taylor, Locke, Lee, & Gist, 1984). In addition, self-
efficacy has been shown to be related to effort and persistence during task performance (cf.
Schunk, 1981). High self-efficacy individuals are more likely to continue to expend effort,
even when faced with initial failures. Low self-efficacy individuals tend to withdraw effort
and perform poorly.

Self-efficacy has been shown to be related to performance (Bandura, 1991; Barling
& Beattie, 1983), career choice (Lent, Brown, & Larkin, 1987), as well as learning
(Campbell & Hackett, 1986; Wood & Locke, 1987). In a uaining context, self-efficacy
has been found to be related to the acquisition of computer software skills (Gist,
Schwoerer, & Rosen, 1989; Martocchio & Webster, 1992), idea generation skills (Gist,
1989), as well as complex interpersonal skills (Gist, Stevens, & Bavetta, 1991). For
example, Gist et a1. (1989) found that a training format which increased self—efficacy
resulted in higher learning among managers attending an idea generation course. In another
study, Ford et al. (1992) found that individuals higher in self-efficacy were performing
more trained tasks four months after training.

The educational literature has also amassed a great deal of evidence suggesting that
self-efficacy is a key component in learning and classroom performance (Pinuich &
DeGroot, 1990; Schunk, 1981; 1982; 1984; 1989). Students who reported higher levels of
self-efficacy learned more and performed at higher levels on classroom exercises and tests
than their low self-efficacy counterparts. Furthermore, high self-efficacy students persisted
longer when faced with initial failure than low self-efficacy Students (Schunk, 1989).

In training studies, self-efficacy has been typically treated as an antecedent variable
which affects the outcome of interest. However, little attention has been paid to the factors

which influence an individual's self-efficacy judgements. Social cognitive theory

15

hypothesizes a number of mechanisms by which an individual arrives at a given self-
efficacy level (Gist & Mitchell, 1992). Taking these into account Should provide a clearer
picture of the dynamics involved in developing self-efficacy and translating these into .
higher learning, especially how training context can affect self-efficacy.

Self-efficacy involves situational as well as individual factors which result in
judgments regarding future behavior. Four general categories of determinants of self-
efficacy have been proposed. These include enactive mastery, vicarious experiences,
verbal persuasion, and physiological arousal (Bandura, 1982). Gist & Mitchell (1992)
developed a model in which they further articulate the mechanisms by which self-efficacy is
developed. Their model proposes three types of assessment processes involved in forming
self-efficacy judgments.

First, an individual analyzes the task requirements to determine the amount and type
of ability required for performing at various levels. When an individual has intensive
experience with the task, past performance outcomes tend to influence this assessment.
The second assessment involves the individual's inferences regarding the causes of this
past behavior. Two individuals who performed at the same level may make different
assessments of their future performance (i.e. self-efficacy) depending on their attributions
regarding the performance outcome. Finally, individuals assess themselves as well as the
setting to determine the availability of resources and constraints for performing at various
levels. These three assessments then combine to form a judgment regarding one's future
behavior, or self-efficacy.

Framing of the training program can influence self-efficacy beliefs through at least
two mechanisms. First, framing can influence self-efficacy by providing feedback
regarding past performance. Individuals who are sent to remedial training may infer that

they have performed poorly and may therefore lower their self-efficacy beliefs. A more

16

positive frame, advanced training, may suggest that performance has been superior and
therefore would result in higher levels of self-efficacy.

Second, framing can also cause trainees to search for explanations for their past
behavior. Explanations for past behaviors or outcomes are referred to as attributions. If
training assignment conveys feedback regarding past performance, the attributions that are
made regarding this past performance can deterrrrine an individual's belief regarding future
behavior. Therefore, training assignments are likely to trigger the attributional process.
The role of atuibutions in the development of self-efficacy is explored below.

Attriburions. Attributions are critical in determining self-efficacy (Gist & Mitchell,
1992). They determine how an individual uses feedback regarding past performance to
make judgements about future levels of performance. Three dimensions of attributions
have been identified. These include locus, Stability, and controllability (Weiner, 1985).
Locus refers to whether the causal facror lies within the individual (internal) perceiver or in
the environment (external). Stability refers to the extent to which the causal factor is (or is
perceived to be) subject to change. Finally, controllability is the extent to which the causal
factor is subject to the control of the individual. Causes which are expected to change tend
to have little effect on perceptions of future performance whereas those which are perceived
to be unlikely to change have a large impact on expectations of future performance
(Bandura, 1991). Other studies have also found that perceived controllability can have an
impact on an individual's self-efficacy and subsequent performance (Bandura & Wood,
1989).

Individuals who attribute past performance to factors within their control (e. g.
effort) are likely to deveIOp higher levels of self-efficacy as a result of positive feedback
because they perceive that their efforts will lead to specified performance levels (Weiner,
1985; Schunk, 1982). In contrast, those who attribute past behavior to situational factors

are more likely to develop lower levels of self-efficacy because of their belief that nothing

17

they can do will result in a given level of performance (Gist & Mitchell, 1992).
Furthermore, poor performance may not necessarily lead to lower self-efficacy levels if this
performance can be "explained away" by attributing it to unstable and controllable factors
(Gist & Mitchell, 1992). This suggests that framing can have an impact on self-efficacy to
the extent that the trainee feels that their performance is within their control.

Research has found that individuals vary in their attributional styles. Some
individuals have a tendency to attribute past performance to internal factors while others
tend to assume that performance was caused by external factors. This suggests that these
two types of individuals are likely to develop different levels of self-efficacy as a result of
training assignment because of the types of attributions they make. A closer look at this
individual difference factor is presented.

Locus of Control. Locus of control is a relatively stable personality characteristic
which is related to an individual's pr0pensity to attribute the cause of an outcome to internal
or external causes (see Rotter, 1966). Individuals with an internal orientation believe that
performance or outcomes are a result of their own behavior and efforts. Conversely,
externals believe that most of what happens to them is a result of external causes beyond
their control such as luck, fate, or the action of others. Thus, internals are more likely to be
proactive and engage in behaviors that will lead to desired outcomes because they perceive
the contingencies between their actions and outcomes.

In a review of the literature on locus of come] in organizational behavior, Spector
(1982) reported that numerous studies have found relationships between locus of control
and perceptions of job characteristics, job performance, motivation, expectancies, as well
as dealing with others. Noe (1986) suggests that in a training context, those with an
internal locus of control are more likely to react to performance feedback regarding their
skill strength and weaknesses. Internals may actually discount or question the accuracy of

negative feedback in making judgments about their own behavior (Stone, Gueutal, &

18

McIntosh, 1984). However, when feedback is given, internals are more likely to take
action in order to correct their lack of skill or knowledge (Phares, Ritchie, & Davis, 1968;
Weiss & Sherman, 1973).

In response to different framings of training assignments, internals are more likely
to be affected by being told that they are entering a remedial training program than an
advanced training program. Because they perceive the causes of their past behavior as
resulting from their own actions, internals will take this label or frame more personally as
reflecting an inadequacy on their part. They may then be more motivated toward
performing well in the training program. It is also possible that internals may also question
the adequacy of the training program for them which will also cause them to work hard
during training in order to prove that they in fact were not deficient.

By conuast, externals are more likely to attribute the cause of their past behavior to
external causes such as task difficulty (Rotter, 1966). Therefore, framing a training
program may have little effect on external trainees. Assignment to a remedial training
program may aetually decrease their self-efficacy and motivation because they may fail to
see how their actions can result in higher learning. Therefore, it is expected that locus of
control may moderate the relationship between u‘aining program label or frame and pre-
training self-efficacy.

Self-Esteem. A second individual difference factor which is likely to moderate the
relationship between training assignment and self-efficacy is trainee's level of self-esteem.
Training assignments can either violate or confirm an individual's level of self-esteem.
Therefore, it is likely that low self-esteem trainees will react differently than high self-
esteem u'ainees when assigned to either remedial or advanced training. This assignment
can boost their confidence level (advanced training) or crush an already low level of self-

confidence (remedial training).

19

There are, in fact, theories relating the consistency of feedback to an individual's
view of him or herself and the potential effects on subsequent behavior (see Hattrup,
1992). The theories basically state that individuals strive for control over their behavior.
Therefore, feedback is interpreted in relation to the self-concept. Long standing arguments
exist as to whether people seek to maintain consistency between feedback and self-concept
or they seek to improve or enhance their self-image (Dipboye, 1977). While the exact
motives which operate at any given time can be argued, recent theories of human behavior
have generally agreed that individuals do in fact compare self-relevant feedback with some
sort of internal standard in order to regulate their future behavior (e.g. Carver & Sheier,
1990).

In general, research shows that individuals prefer positive over negative feedback
irrespective of their self-image (McFarlin & Blascovich, 1981). However, when that
feedback is inconsistent with an individual's self-image, differences are found between
high and low self-esteem individuals in terms of their expected future performance. The
research suggests that people with a positive self-image who receive negative feedback
become very motivated to perform at higher levels in order to receive feedback which is
consistent with their positive self-image (Swann, 1990). In contrast, low self-image
individuals who receive negative feedback are not necessarily motivated to change their
behavior. Therefore, it appears that in order to maintain a sense of control over oneself,
individuals seek out or interpret feedback in a way that is consistent with their self-image
(Swann, 1987). When discrepancies occur, individuals are motivated to behave in ways
that restore consistency (Carver & Scheier, 1990).

High self-esteem individuals who are sent to remedial training may be motivated to
perform well and may actually develop high levels of self-efficacy. By contrast, low self-
irnage individuals who believe they are performin g poorly will havetheir low self-image

confirmed if sent to remedial training which will most likely result in low levels of self-

Z)

efficacy. Advanced training, on the other hand, implies that past performance has been
more than adequate. High self—esteem persons are likely to maintain their already high
levels of self-efficacy. Low self-image individuals assigned to advanced training, on the
other hand, may question or discount the accuracy of this information and, therefore,
maintain their initial low level of self-confidence (Noe, 1986). However, research has also
tended to Show that positive feedback, as implied in assignment to advanced training, is
likely to raise an individual's level of confidence, regardless of their level of self-esteem
(McFarlin & Blascovich, 1981). Therefore, low self-esteem individuals assigned to
advanced training may actually Show higher levels of self-efficacy than those assigned to
remedial training. Therefore, it is argued that self-esteem is likely to moderate the
relationship between training assignment and self-efficacy.

Conclusion. It has been argued that training assignment can affect self-efficacy.
Furthermore, there is substantial evidence to suggeSI that self-efficacy is positively related
to learning and training performance. When individuals have high levels of self-efficacy
they persist longer during task performance. In short, self-efficacy is related to the amount
of motivational energy that an individual expends toward task performance. Due to the
importance of self-efficacy for training effectiveness, the current study examines the extent
to which framing of uaining assignments affects pre-training self-efficacy. It is argued that
attributions, locus of control, and self-esteem, are likely to be related to how individual's
react to being assigned to differently framed training programs.

As stated earlier, self-efficacy is likely to affect training outcomes through its
relationship with motivation (cf. Bandura & Cervone, 1986). A closer look at the literature

on the effects of motivation on training outcomes is now presented.

21

I I . .

For a uaining program to be effective individuals must first master the trained
material and then transfer this knowledge to the work situation (Ford et al., 1992; Noe,
1986). Traditionally, research on training effeCIiveness which has focused on learning
outcomes has tended to rely on behavioristic/mechanistic views of human learning
(Kraiger, Ford, & Salas, 1993). This view led researchers to focus on stimulus-response
variables such as training design and delivery. As a consequence, individual differences
related to training outcomes were often ignored. Recently, however, a shift toward more
cognitive theories of training has occurred (cf. Cormier & Hagman, 1987).

A consequence of the cognitive perspective in training research is the recognition
that individuals differ in how much they will learn and how they will react to training. This
phenomenon is oftentimes referred to as an aptitude-treatment interaction (cf. Kanfer &
Ackerrnan, 1989). Thus, characteristics such as ability, motivation, and attitudes that
individuals bring to the uaining setting can interact with different training program designs
to determine training outcomes.

The concept of trainability captures the essence of this paradigm shift. Individual
differences in ability and motivation determine an individual's trainability, or their capacity
and willingness to learn the trained material (Noe, 1986; Robertson & Downs, 1989).
Individual differences in cognitive ability are believed to be crucial in determining the extent
to which an individual can master the uaining content (Ree & Earles, 1991). However,
individual differences in ability are usually believed to be relatively stable (Ackerman,
1987). Pre-uaining context factors are more likely to influence more fluid aspects of
individual characteristics, ll;r.llcly motivation and attitudes.

Noe (1986) proposed a model which suggests that more variable aspects such as
trainee motivation and attitudes can play an important role in determining training

effectiveness. He contends that even though individuals may have the ability to master the

22

uaining content they may fail to do so because of lack of motivation. In addition, Noe
proposes that individual difference variables such as locus of control, career involvement as
well as environmental characteristics can have an impact on a trainee's pre-training
motivation.

In a test of the Noe (1986) model, Noe & Schmitt (1986) investigated pre-training
factors in a uaining program designed to improve the administration and interpersonal skills
of educators. The results provided little support for the relationship between pie-training
motivation and training outcomes. In addition, factors which were hypothesized to
influence pre-training morivation were not found to be significantly related to pre-training
motivation. Even though the study failed to support the importance of pre-training
motivation as a component in training, severe methodological problems limit the ability to
generalize the findings. Specifically, the Study was composed of only 60 trainees. This
small sample provided little power to detecr significant paths in their path-analytic model.

Facteau et al. (1992) conducted a further ten of the Noe (1986) model. Managers
and supervisors (n=967) employed by a large southeastern state government served as the
participants in the study. The results of LISREL su-uctural equations analyses revealed that
motivation to learn was positively related to transfer of learned skills to the job. In
addition, a number of environmental variables (intrinsic incentives, compliance) as well as
career planning were related to morivation to learn.

A study by Baldwin, Magjuka, & Loher (1991) found that trainees with higher
levels of pie-training motivation showed higher learning during a performance appraisal
training program. Tannenbaum et a1. (1991) found that Naval Recruits reporting higher
levels of pre-training motivation had higher levels of academic and physical self-efficacy at
the end of training. Similarly, Mathieu, Tannenbaum, & Salas (1992) found that training

motivation was related to the amount of learning at the end of training.

23

The evidence suggests that pre-training motivation is a potential determinant of
training outcomes such as behavior and learning. The role of morivation in learning,
however, is quite complex. While motivation prior to learning may be beneficial in
focusing an individual's attention toward the training program, mOtivational interventions
during uaining may actually decrease learning (Kanfer & Ackerman, 1989). In general,
however, it appears that individuals who enter a training program with a willingness and
confidence to do well tend to do better in the training program. Thus, one of the ways in
which pre-training context can affect training outcomes is through its influence on
motivation.

In summary, the framing of the training program can provide feedback regarding a
trainee's past behavior. The attributions that are made based on this feedback can then
determine the extent to which self-efficacy is enhanced or diminished. Self-efficacy, in
turn, will affect the trainee's mOtivation level going into the training program. However,
while individuals use feedback to make judgments regarding future performance, they are
not passive recipients of this feedback. They hold prior beliefs about how well they are
doing which may or may n0t be consistent with the feedback provided by training
assignment. A possible outcome of this inconsistency is that trainees may perceive that the
system used to make training assignments is unfair and therefore leads to lower motivation

to learn.

Ea'unessicrseutiom

It has been argued that individuals are not passive recipients of training
assignments. Individuals are likely to hold prior beliefs regarding the type of training
assignment they expect. However, if these expectations are violated, it is likely that
u'ainees will question the fairness of these training assignments. There are no published

studies in the training literature which deal with perceptions of fairness of training

24

assignments. While it can be argued that fairness perceptions are a form of training
reactions as described by Kirkpauick (eg. 1967), fairness perceptions represent a Specific
affective response regarding assignments and not just overall reactions to the content of the
training program. Furthermore, fairness perceptions are likely to be formed prior to
attending the training program whereas training reactions are formed after the individual has
completed the uaining program.

Two dimensions of fairness perceptions have been distinctly outlined in the
literature. Distributive justice refers to perceptions regarding decision outcomes (e. g.
assignments) whereas procedural justice refers to the procedures used to make these
decisions (Greenberg, 1987; 1990). Procedures are judged to be fair when they (1) follow
consistent rules, (2) are based on accurate information, (3) employ safeguards against bias,
(4) allow for appeals to be heard, and (5) are based on prevailing moral and ethical
standards (Leventhal, 1980). In a training context, procedural justice refers to the
procedures used to assign individuals to different training programs. This might be a test
or the judgement of a supervisor. Distributive juSIice relates to actual assignments made.
While conceptually different, past research has tended to confound measures of procedural
and distributive fairness thus making it difficult to examine the effect of each on outcomes
of interest (Hattrup, 1992).

In a training context, trainees may question the fairness of the procedure used to
assign them to training as well as the outcome, or actual program to which they are
assigned. Past research has shown that perceptions of procedural justice are often
associated with perceptions of distributive justice (Folger & Greenberg; Greenberg, 1990;
Leventhal, 1980). However, as stated earlier, this overlap may be due to measurement
problems. There is research which suggests that individuals may accept an outcome which
they judge to be unfair if they perceive the procedure used to arrive at the outcome to be fair

(Leung & Li, 1990).

25

Fairness perceptions have been related to affecrive as Opposed to performance
outcomes. For example, researchers have nOted a relationship between fairness
perceptions and organizational commitment and satisfaction (Folger & 'Konovsky, 1989;
Fryxell & Gordon, 1989). It is for this reason that fairness perceptions are likely to be
related to affective reSponses to the training program as Opposed to actual training
performance. However, since these perceptions will be formed prior to training
attendance, it is possible that negative perceptions of fairness may result in lower pre-

training motivation and thus affect performance indirectly.
Framework for the Current Research

From the literature cited, it is clear that a number of factors present prior to an
individual entering a training program can have an impact on how well the trainee performs
during training. The present study examines the extent to which pre-training context
influences pre-training self-efficacy, attitudes, and motivation, and, in turn, training
outcomes. Specifically, it is argued that assigning an individual to a training program can
communicate information regarding past performance. The way in which the training is
framed, remedial as Opposed tooadvanced, can provide this feedback information.

While some studies have begun to investigate the role of pre-training context, no
attention has been paid to the information that assignment to a uaining program can
communicate to a trainee. Furthermore, research on the consequences of feedback in
training has nOt incorporated assignments as a form of feedback (cf. Martocchio &
Webster, 1992). Given the pOtential negative effects of assignment to differently framed
training programs it is argued that further research is needed to address this concern.

Noe (1986) as well as Baldwin & Ford (1988) have borh echoed the need to

understand the influence of pre-training factors on training outcomes. Noe (1986)

%

suggested that motivation is a critical pre-training characteristic which is potentially affected
by pre-trainin g context. Martocchio (1992) further suggests that pre-u'aining context can
affect an individual's level of self-efficacy going into training. Social cognitive theory
provides a framework for linking performance feedback with subsequent task performance
(Bandura, 1991). The current study extends this research by examining the extent to which
training assignment affeCIs an individual's pre-training self-efficacy and motivation. In
addition, individual differences which could potentially moderate the effects of context are

also examined.

Pre-Trainin ontext

The current study takes the perspecrive that context serves to focus an individual's
attention to their past performance as well as other aspects of the self such as how likely
they are to perform well during training. This framing is believed to set off a series of
cognitive processes such as attriburions which can ultimately affect a trainee's self-efficacy
and motivation prior to entering the training program. Research has consistently shown
that self-efficacy and motivation are important pre-training characteristics which are related
to uaining outcomes (cf. Baldwin, Magjuka, & Loher, 1991; Gist, Stevens & Bavetta,
1991). However, research on the effects of context, specifically framing, is lacking.

An unexplored consequence of context is the extent to which it conveys information
that is consistent with or violates a trainee's self-concept. Organizations may be unaware
of this potential side-effect of sending their workers to a training program. Threats to the
self-concept can have powerful effects on an individual's motivational as well as affective
states (cf. Dipboye, 1977; Swann, 1990). This study further expands the research on pre-

training context by examining the role of self-concept as it relates to training assignment.

W

Traditional training studies have focused on Kirkpatrick's (1959; 1960) training
criteria for evaluating training outcomes. These criteria include reactions, learning,
behavior, and results. While arguments have surfaced regarding the exact relationship
among these criteria (cf. Alliger & J anak, 1989), they are still considered appropriate for
evaluating the effectiveness of a training program. Kraiger, Ford, & Salas (1993) argue
that attitudinal as well as cognitive measures may also expand our understanding of the
effects of training. In addition, the level of confidence or self-efficacy that a trainee leaves
with has a large impact on how well they transfer the learned information back on the job
(Ford et al., 1992). Furthermore, affective responses to training can have an impact on
employee morale, commitment, and attitudes toward their work; all of which can affect
productivity.

The present study focuses on two general typesiof outcomes. These include
performance and affective training outcomes. Performance outcomes are those which are
related to an individual's ability to perform the particular task for which they were trained.
These outcomes include learning, behavior, and actual task performance. Research
suggests that pre-training motivation is likely to have a direct impact on these performance
outcomes (Noe, 1986).

The second type of training outcomes examined are affective outcomes. These
include training reactions and post-training self-efficacy. Reactions has oftentimes been the
only training outcome measured (Goldstein, 1992). However, little is known regarding the
relationship between pre-uaining characteristics and performance outcomes on training
reactions. In other words, is an individual's impression of the training program formed
prior to , or as a result of, training? In addition, current research has recognized that an
individual's perceptions of future performance, or self-efficacy, is an important training

outcome (Mathieu, Martineau, & Tannenbaum, 1993; Tannenbaum et al. 1991).

%

Individuals who leave training with higher levels of self-efficacy are more likely to Show

transfer from training to the job (Ford et al., 1992; Gist, Stevens, & Bavetta, 1991).

'ff n

Little research exists on the extent to which individuals react differentially to pre-
training context factors. The present study examines the extent to which locus of control
attributions, and self-concept moderate the effects of framing. Martocchio & Webster
(1992) found some evidence for the hypothesis that not all trainees react equally to
feedback given during training. The current study extends this research by examining the
moderating effects of individual differences in interpreting the pre-training environment.

Since it is believed that context serves to focus attention to feedback regarding past
performance as well as the likelihood of success in training, individual differences which
could potentially affect how this information is interpreted are investigated. Locus of
control has been shown to influence the attributions individuals make when they receive
performance feedback (Spector, 1982). These attributions play a role in determining an
individual's level of self-efficacy (Gist & Mitchell, 1992).

In addition, assignment to a training program can threaten or confirm one's self-
concept. High self-esteem individuals are more likely to maintain high levels of self-
efficacy, especially in the face of disconfuming evidence such as assignment to remedial
training. Low self-esteem individuals, on the other hand, may benefit from the positive
feedback implied by assignment to advanced training but be crushed when faced with self-
confirrrring information conveyed by assignment to remedial training.

The following section presents the conceptual model developed for the current
study. The model specifies the linkages among the constructs examined. Specific .

hypotheses derived from this model are discussed.

Wartime

Figure 1 presents the conceptual model developed for the present study. This
model adds to the training literature in a number of ways. First, the model clearly outlines
the mechanisms by which pre-training context leads to training outcomes. Secondly, the
model proposes a number of individual differences which are believed to moderate the
relationship between pie-training context and motivational and affective outcomes. Past
research on context effects has tended to ignore the role of individual differences. In
general, the proposed model incorporates motivational, affective, and individual difference
factors into a comprehensive model that reflects our understanding of the role of context in
affecting training effectiveness.

Two types of hypotheses can be derived from the proposed model. The first are
what can be called "Moderating Hyp0theses". These hypotheses State that individual
characteristics can affect the nature of the relationship between training assignments and
pre-training attitudes and motivation. Secondly, the model prOposes that pre-training
attitudes and motivation will affect various training outcomes. In other words, the model
proposes that, independent of the quality of the training program, an individual's attitudes
and motivation can have an influence on the effectiveness of the training program. Since
these hypotheses relate to the Structure of the relationships between pre-training
characteristics and training outcomes, these will be called "Structural Hypotheses“. Each
will be discussed in turn.

Moderating Hymtheses. The framing of training assignments are hypothesized to
affect pre-training characteristics by providing feedback regarding past performance. This
feedback is hypothesized to influence two mechanisms. These include attitudes toward the
training assignment and expectancies regarding future performance or self-efficacy. This
framework is consistent with the arguments developed earlier which suggest that training is

an activity which is embeded within a larger organizational reality. Circumstances

30

surrounding the assignment to training can set up a context which can influence how a
trainee approaches the training program. However, not all trainees will react in a similar
fashion to the same uaining assignment.

First, individual differences will moderate the relationship between training
assignments and pre-uaining self—efficacy. Particularly, an individual's atuibutions
regarding their level of past performance which led to their training assignment are likely to
result in different levels of self-efficacy. Those who attribute their past performance to
internal causes (effort, ability) or external stable causes (task difficulty) are likely to show
lower levels of self-efficacy when assigned to remedial training that those making similar
attributions who are assigned to advanced training (Bandura, 1977; Gist & Mitchell, 1992;
Weiner, 1985).

Locus of control is an individual difference variable which describes an individual's
propensity to make internal vs. external attributions (Rotter, 1966; Spector, 1982).
Therefore, locus of control, through its influence on atuibutions, is likely to moderate the
relationship between training assignments and pre-training self-efficacy. Past research has
shown than internal locus of control peOple tend to be more affected by feedback (Spector,
1982). Therefore training assignment may have stronger consequences for internal as
opposed to external locus of control individuals. Externals are less likely to see a
connection between their actions and observed outcomes and, therefore, show little
differences in self—efficacy as a result of training assignments. Stated explicitly:

H1: Locus of control will moderate the relationship between training assignment

and pre-training self-efficacy (see Figure 2). Internal locus of control individuals

will show higher levels of pre-training self-efficacy when assigned to advanced

training than to remedial training. Those with an external locus of control will be
relatively unaffected by their training assignment.

Training assignments are also likely to have an effect on pre-training self-efficacy to

the extent that it confirms or disconfirms the trainee's perceptions of him or herself. Those

31

with high levels of self esteem will be less affected by training assignments. These
individuals are more likely to believe that they perform well in any situation and therefore
maintain high levels of self-efficacy, even in light of an assignment to remedial training
(Carver & Scheier, 1990; Swann, 1990). However, low self-eSteem individuals may be
most affected by the framing of their training assignment. Assignment to advanced training
is likely to result in a boost in confidence by signaling to the individual that they can
actually perform this task well. However, assignment to remedial training will confirm an

already low self-image and result in low levels of self-efficacy. Stated explicitly:

H2: Self-esteem will moderate the relationship between training assignment and
self-efficacy (see Figure 3). Individuals with a positive self-esteem will maintain
relatively high levels of self-efficacy regardless of their training assignment. On the

Other hand, low self esteem individuals assigned to advanced training will report

higher levels of self-efficacy than those assigned to remedial training.

Secondly, training assignment is likely to affect training outcomes through an
individual's perceptions of fairness regarding their training assignment. However, not all
individuals will perceive the fairness equally. Their beliefs regarding the training
assignment which they should receive (as a function of their perceptions of past
performance) are likely to moderate the effects of training assignments on fairness
perceptions. Individuals who believe that their past performance has been adequate, and
believe they should be assigned to advanced training, are likely to have more negative
perceptions of the outcome (uaining assignment) and the process (Training Assignment
Test) than those assigned to advanced uaining. This will result in lower levels of
procedural and distributive justice perceptions.

On the contrary, those who believe their past performance has been poor, and

believe they should be assigned to remedial training, are likely to question the fairness of

the outcome and process when assigned to advanced training. However, when assigned to

32

remedial training, they are likely to have positive perceptions of both disuibutive and

procedural justice. Stated explicitly:

H3: Perceived performance will moderate the relationship between training
assignment and perceived fairness of assignment (see Figure 4). Individuals who
believe they are performing well and are assigned to remedial training will report
low levels of disuibutive and procedural justice as compared to those assigned to
advanced training. Similarly, those who believe they are performing poorly and are
assigned to advanced training will also report low levels of disuibutive and
procedural justice as compared with those assigned to remedial training.

H4: Expected assignment will moderate the relationship between training
assignment and perceived fairness (see Figure 5). Training assignments which are
different than expected assignments will result in lower levels of perceived fairness
than when they are consiStent with expeCted training assignments.

The Moderating Hypotheses presented above describe the mechanisms by which
individuals will react to different frames associated with training assignments. The
proposed model suggests that pre—training self-efficacy and fairness perceptions are the
most immediate result of training assignments. B0th of these variables are then
hypothesized to set off a causal chain of events which lead to differences in performance
and affective training outcomes. These hypotheses are discussed below.

W. The model hypothesizes that pre-training self-efficacy will
have an effect on trainin g outcomes through its influence on motivation to learn. Past
research on self—efficacy has found it to be related to the amount of effort an individual
expends on a particular task (Bandura, 1977; Bandura & Cervone, 1986; Gist & Mitchell,
1992). Those with higher levels of self-efficacy tend to Spend more energy and remain
engaged on a task longer than those low on self-efficacy.

In addition, fairness perceptions are also hypothesized to have an effect on
motivation to learn. This particular effect of fairness perceptions has not been explicitly
examined in the literature. However, the literature does suggest that negative fairness

perceptions are related to commitment (Folger & Konovsky, 1989; Fryxell & Gordon,

33

1989). Therefore, those reporting low levels of distributive and procedural justice are
hypothesized to report lower levels of morivation to learn. The following specific
hypotheses are made regarding the relationships between pre-training self-efficacy,
distributive, and procedural justice.
H5: Pie-training self-efficacy will be positively related to motivation to learn after
accounting for the effects of fairness perceptions.
H6: Perceptions of distributive justice will be positively related to motivation to
learn after accounting for the effects of pre-training self-efficacy and procedural
justice.
H7: Perceptions of procedural justice will be positively related to motivation to

learn after accounting for the effects of pre-training self-efficacy and distributive
justice.

An individual's level of morivation has been shown to be related to various training
outcomes. The proposed model hypothesizes that motivation to learn will have its most
direct impact on performance outcomes. These are outcomes which require the individual
to devote a substantial amount of attentional resources in order to benefit from uaining
(Kanfer & Ackerman, 1989). Specifically, motivation to learn is likely to lead to
differences in learning, performance, and behavioral outcomes of uaining.(Facteau et al.,
1992; Baldwin, Magjuka, & Loher, 1991; Tannenbaum et al., 1991). Therefore, the
following hypotheses can be made regarding the effects of motivation to learn on

performance outcomes of training.

H8: Motivation to learn will be positively related to actual learning.

H9: Motivation to learn will be related to behavioral measures of training
effectiveness.

H10: Motivation to learn will be positively related to post-training performance.

34

It is expected that these relationships will hold true after accounting for differences
in pre-training self-efficacy, and fairness perceptions. In other words, the model proposes
that motivation to learn is a mediating variable, therefore, its effeCt must be shown to hold
true after accounting for the effects of other prior causes (James & Brett, 1984; James,
Mulaik, & Brett, 1982). Thus, hypotheses 8 - 10 must be tested in conjunction with a test
of mediation of motivation to learn. This test is discussed in more detail in the Analytic
Strategy section.

Finally, affective outcomes of training are hypothesized to be a result of both
performance outcomes, and fairness perceptions. In Other words, an individual's reaction
to training, and their resulting level of self-efficacy, will be a function of both, how well
he/she performed as a result of training, and perceptions of fairness regarding training
assignments. This suggests that training reacrions are more than just a function of training
design. How an individual felt about the training program prior to attendance will also play
a part in their post-training reactions and self-efficacy. Thus the following hypotheses

regarding affective outcomes are proposed.

H11: Fairness perceptions (distributive and procedural justice) will be positively
related to uaining reactions.

H12: Learning will be positively related to training reactions.

H13: Behavioral outcomes of training will be positively related to training
reactions.

H14: Post-training performance will be positively related to training reactions.

H15: Fairness perceptions (disuibutive and procedural justice) will be positively
related to post-training self-efficacy.

H16: Learning will be positively related to post-training self-efficacy.

H17: Behavioral outcomes of training will be positively related to post-training
self-efficacy.

H18: Post-training performance will be positively related to post—training self-
efficacy.

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METHOD

E . .
The participants for this study were 217 undergraduate psychology students at
Michigan State University. The participants took part in the experiment as part of a course

requirement. The participants were 64% female and 36% male.

Design.

The current study employed a one-way experimental design containing three levels
of a between-subjects variable. The manipulated variable involves the label given to the
training program to which the participants were assigned (remedial, advanced, no label).
The no label condition represents a control condition used as a reference for interpreting the
results of the two labeled training conditions. A tom] of 163 individuals were assigned to
the two experimental groups. The conuol group consiSIed of 54 individuals. A more
detailed description of this manipulation is described in the Procedure section. The

remaining variables involved measured as opposed to manipulated factors.

Tail:

The present study suggests that learning a new task is a function of attitudinal and
motivational levels prior to entering a training program. The focus of the study is on the
process involving uaining assignment and subsequent attitudes and motivational levels.
Therefore, the choice of task was driven by concerns in capturing this process over any

concerns to train peOple for "real world" skills. In order to capture the process in question

40

41

the task must have several properties. First, the task muSt be novel in order to avoid any
confounds of prior experience. Even though randomization would help decrease this
confound, prior experience could also serve to place a ceiling on performance and thus not
allow for meaningful training effects. Second, the task must be complex enough to allow
for changes in performance as a function of learning. In addition, the complexity of the
task must allow for individual differences in performance. Third and most important, the
task must be cognitively complex enough to require substantial attentional resources. The
task should require individuals to be motivated enough to process the information and learn
the task.

Given the above requirements, the task chosen for this Study involved a multiple-
cue probability learning task (cf. Stevenson, Busemeyer, & N aylor, 1990). This type of
task requires that the participant make a decision based on the values of a number of cues
(e. g. cost, manufacturer, etc). The conect decision is usually determined by the researcher
by a combination of the cues and their values. The participant is asked to look at the values
of all cues and make a decision based on their hypothesis about how the cues combine to
determine the conect response (e.g. Brunswik, 1956). They are then given feedback as to
the accuracy of their decision. This information then allows the participant to learn the
correct decision from the cue values over a number of trials.

This type of task contains the necessary characteristics to examine the process by
which training assignment leads to differences in training performance. Because the cues
and cue values can be manipulated, the task is usually novel. Individual performance is
determined by the extent to which the participant learns the prOper combination of cue
values which lead to a particular decision. Therefore, the participant must be cognitively
engaged in the task. Any differences in attitudes and motivation which lead participants to

pay less attention should result in differences in task performance. In addition, rules

[i

42

relating to cue combination and strategies for arriving at the correct response can be taught
thus allowing for the design of a training program.

The performance task used in this study was a computer Naval Air Defense
simulation (Hollenbeck, Sego, Ilgen, & Major, 1991). This task requires individuals to
command a US Naval vessel and make decisions regarding a series of "targets" presented
on an IBM compatible microcomputer. The individual is asked to decide on a defensive
posture (Ignore, Monitor, Warn, Lock-On, Defend) depending on 9 target characteristics or
attributes (speed, altitude, size, angle, IFF, direction, corridor status, radar type, range).
Each trial requires the individual to make a decisions within an allotted amount of time.
However, if a decision is made sooner, another target can be assessed. In this way both
the number of targets as well as well as the accuracy of decisions can be assessed when
evaluating performance on the task.

The level of threat associated with a particular target depends on the values of the
nine attributes. Four interactions among eight of the nine attributes exist which help
deterrrrine the level of threat. The interaction indicates a threat if both of the attributes are
threatening. If any of them is non-threatening the interaction becomes non-threatening.
This added complexity requires the participants to not only know the level of threat for each
attribute but also the interactions among the attributes (see Hollenbeck, Sego, Ilgen, &
Major, 1991, for a more detailed explanation).

There are five possible outcomes possible during each trial of the simulation. These
are: hit, near miss, miss, incident, and disaster. The particular outcome depends on the
distance between the participant's decision and the correct decision. If the participant's
decision is correct, they receive a "hit" for that trial. If the participant's decision is one
level away from the correct decision (i.e. participant chose "ignore" when they should have

"monito ") they receive a "near miss". A two level distance results in a "miss". Three

43

and four levels away from the correct decision results in an "incident" and a "disaster",
respectively.

Point values are awarded for each outcome. A "hit" is worth 2 points, a "near
miss" 1 point, a "miss" 0 points, an ”incident" -1 points, and a "disaster" -2 points. A final
score was computed by adding the total number of points received by the participant during

each of the trials.

T 'nin ur

The training course consisted of four sections. First, the participants reviewed the
materials presented in the first experimental session which outlined the general instructions
of the task. Second, the participants were instructed on the "inflection points" or the
specific level when an attribute changes from non-threatening to somewhat threatening and
from somewhat threatening to very threatening. Knowing these points helps one determine
the levels of threat. Thirdly, they received instruction on strategies involving the
measurement of the target attributes. Some of the attributes interact so the level of threat is
determined by the combination of two target atuibutes. For example, a fast target is not
threatening. However, a fast target headed toward the Carrier vessel group is very
threatening. Given these interactions, the appropriate measurement strategy is to measure
the characteristics that interact in their respective order. Finally, the participants received
training on various "hot keys" which make the use of the keyboard to access the program
considerably more efficient.

The participants read each section of the trainin g manual (see Appendix A) and
received oral instructions relating to the materials presented. After the last section, the
participants were given a practice target to allow them to get comfortable with the keyboard
and to get initial experience pracricing the skills learned. The entire training course lasted

around 45 minutes.

Matures

The following measures were collected as part of this investigation. The measures
are listed in the order in which they were collected. Where appropriate, scale reliabilities
reported are for the experimental groups only (n=163). Participants in the control group
responded to a few relevant measures. Experimental and control group scale statistics will
be described more fully in the "Results" section. Table 1 presents an overview of the
measures collected from the experimental and control groups.

megs of Control. Locus of control was measured using a revised version of
Rotter's (1966) 29-item scale developed by Andrisani and Nestle (1976). This is an 11-
item Likert scale and was shown to be reliable by Noe and Schmitt (1986). The observed
reliability for this scale was or = .65 for 96 of the participants. The scale was coded so that
high scores represent an internal orientation. A copy of this scale can be found in
Appendix B.

Because of the low reliability of the ll-item locus of control scale, the original scale
developed by Rotter (1966) was used for the last 67 participants (see Appendix C). This
scale contains 23 forced-choice items with 6 filler items. Two items were deleted resulting
in a 21 item scale. The observed reliability of this scale was or = .71. Because two
different scales were used to measure locus of control, the analyses for this variable were
conducted twice; once for each scale.

Self-Esteem. Self-esteem was assessed using Rosenberg's (1965) 10-item scale.
This scale measures an individual's overall sense of self-worth and confidence. Past
research has demonstrated this scale to be reliable in a number of settings (e. g. Pelham &
Swann, 1989; Hattrup, 1992). The observed scale reliability was or = .82. A copy of this

measure can be found in Appendix D.

SESSIQNI

Self-Esteem

Locus of Control

Pre-Training Knowledge

Pre-Training Performance
E l N 2

Perceived Performance

Expected Assignment

Attributions

Distributive Justice

Procedural Justice

Pre-Training Self-Efficacy

Motivation to Learn

Post-Trainin g Knowledge

Post-Training Performance

Learning

# Trials

Behavior

Accuracy

Post-Training Self-Efficacy

Reactions

45

Table 1

Overview of Study Variables

EXPERIMENTAL

><><><><

><><><><><><><><><><><><><><><

ENTRQL
X
X
X
X

><><><><><><><><><><

46

fieflrammglgnowledge. Knowledge regarding threat levels and combination
rules for the nine target attributes gained during the initial pre-trainin g inn-oduction session,
and prior to participants receiving training, was assessed by a 20-item knowledge test. All
items employed a multiple-choice (5 options) format. This measure was adapted from
Major (1992). The split-half reliability (odd-even) was r = .70.. A copy of this measure
can be found in Appendix E.

W. A paper-and-pencil simulation of the Air Defense Task
was used to assess the participants application of their initial level of knowledge about the
Air Defense Task (see Appendix F). The participants were presented with all 9 target
attributes and asked to indicate the appropriate defensive posture. The targets varied in
difficulty from very easy targets (clearly threatening or non-threatening) to very difficult
(very complex interactions). The participants assessed a tom] of 10 targets. The sum of the
points received for each of the 10 targets was used as the measure of performance. The
possible range for this measure is -20 to +20. The observed split-half reliability (odd-even)
was r = .40.

Bereeived Performance. Participants responded to a 5-item scale measuring the
participant's perceived level of performance on the Training Assignmenr Tesr. Participants
indicated their level of agreement with each item using a 5-point Likert scale. Appendix G
presents the perceived performance scale. The scale reliability was or = .90.

W. Participants were asked to indicate which training program
they expect to be assigned to (remedial vs. deveIOpmental) given their perceived level of
performance on the TAT. This sin gle-item measure can be found in Appendix H.

mm. Four major types of atuibutions described by Weiner (1985) were
measured. These include luck, effort, task difficulty, and ability. The participants were
asked to indicate the extent to which luck, effort, task difficulty, and ability caused their

level of performance on the TAT. The first 93 participants responded to one item for each

47

of the four dimensions. For the last 69 participants, four additional items per dimension
were added (see Appendix I). The scale reliabilities based on these 69 participants were or
= .77, 0t =.86, or = .71, or = .72 for luck, effort, task difficulty, and ability attributions,
respectively.

W. A 10 item scale adapted from Hattrup (1992) was used
to measure an individual‘s expectations regarding their future level of performance on the
Air Defense Task. Participants responded to 10 items using a 5-point Likert scale. The
observed scale reliability was or = .89. This measure can be found in Appendix J.

Egimess Pereeptiens. A 20-item scale was used to assess the participants
perceptions of fairness regarding the training assignment. Two dimensions of fairness
perceptions, distributive (10 items) and procedural (10 items) jusrice, were assessed by the
instrument (Hattrup, 1992). The participants indicated their level of agreement with each
item on a 5-point Likert scale. Item and factor analyses resulted in the elimination of 1 item
from each of the 2 measures. The scale reliabilities for the resulting 9—item measures were
a = .92 and or = .83 for distributive and procedural justice, respectively. This scale can be
found in Appendix K.

Meg'vetion to guy. A 10-item scale was developed to assess participant's
motivation to learn the material presented in the training program. These items are similar
to those used by Noe & Schmitt (1986). Participants indicated their level of agreement
with each item using a 5-point Likert scale. The scale reliability was or = .93.The
motivation to learn scale can be found in Appendix L.

Tiger-Training Knewledge. Knowledge regarding scale threat values and
interactions at the end of training was assessed by the same 20-item mg
Kemledge measure. By using the same measure, pre- and post-training scores can be

compared. The split-half reliability (odd-even) reliability was r = .73.

48

Bear-jl'raining Perfermance. After receiving training, the participants responded to

the same 10-target paper-and-pencil simulation of the Air Defense Task as on the be
Training Performance measure. Performance scores were computed in a similar fashion.
The split-half reliability (Odd-even) was r = .56.

Beer-Training Self-Effieaey. The participants' expectations regarding their future
performance level on the Air Defense Task was measured by a 10-item scale Similar to the
pie-training self-efficacy measure. The scale reliability was or = .88.

I;ar_ni_ng. The extent to which participants learned the uaining material was
assessed via a l3-item knowledge test. While comparing pre- and post-training knowledge
can also serve as a way of assessing learning, this measure covers the unique material
developed for the training program beyond the material presented in the introductory
session (see Sections 2-4 of the Training Manual in Appendix A). The split-half reliability
(odd-even) was r = .82. A copy of this learning measure can be found in Appendix M.

Number of Trials. The number of trials completed by the participant in the 40
nrinute time period allowed was collected by the computer simulation. There were a total of
35 targets in the simulation target set. Therefore, scores on this measure could range from
0 to 35. Because of a computer malfunction during data collection, this measure was
collected on only 67 of the 163 experimental, and 17 of the 53 control group participants.

Aeeuracy. The accuracy of the participants' responses during the computer
simulation was assessed using the point system described above. For each individual trial,
scores could range from +2 to -2 depending on the dIStance between the participant's
response and the correct response. Accuracy scores were computed by summing the
number points across trials and dividing by the number of trials. The resulting measure
also had a possible range of +2 to -2. This measure was only available for a subset of the

participants as indicated above.

49

Behevier. Participants can choose to measure as many or few target atuibutes as
they wish. However, the overall target threat level is determined by 4 interactions among 8
target attributes. This was a point which was covered during the training program. If the
individual applies the training to the task, they should measure all nine attributes. A
behavioral measure of uaining outcome was developed by counting the number of
interactions actually measured by the participant prior to rendering a judgment. This
information was collected by the computer program and listed for each of the targets 1
assessed by the participant.

Scores on this measure were computed by adding the number of interactions
measured for each trial (out of 4 possible) and summing across all uials. Because the
number of trials varied across participants, an average was computed by dividing the total
interactions by the number of trials completed. Possible scores could range from 0—4. This
measure was only available for a subset of the participants as indicated above

Rem. A 10—item scale was deveIOped to measure participants' attitudes
regarding the quality and usefulness of the training program. Participants indicated their
level of agreement with each item using a 5-point Likert scale. Item and factor analyses
resulted in the elimination of 1 item. The resulting scale reliability was or = ..80. This

scale can be found in Appendix N.

Procedure

The participants took part in two experimental sessions. During the first session
approximately 30 participants reported to a large classroom. Upon arrival to the
experimental site, the participants were asked to read and Sign a brief consent form
informing them of the voluntary nature of their participation and the sorts of activities that
they would be performing during borh experimental sessions (see Appendix 0). At this

time the participants completed the gens of Control and Self-Esteem measures.

50

The participants were then given a brief oral introduction to the Air Defense Task.
An instruction manual developed by Major (1992) summarizing the task as well as the
descriptions of the various target characteristics was then provided to the participants (see
Appendix P). The experimenter then informed the participants that the study was being
conducted in order to examine the effectiveness of two specially designed uaining
programs. They were told that their performance on a Training Assignmenr Tee; (TAT)
will determine their training assignment. This test contained the Pre-Training Knewledge
and Pre-Training Performance measures.

The participants were told that paSt research had shown that some individuals tend
to perform extremely well on this test while others have tended to do fairly poorly.
However, two training programs have been specifically designed for each of these two
types of individuals. The participants were told that if they perform worse than average on

the test they will be assigned to remedial training. This training program has been

 

specifically designed to help those who are obviously deficient in the basic skills involved
in performing the Air Defense Task. If they perform above average, they were to be
assigned to advaneed training. The control group was simply told that they were to fill out
a knowledge test and sign up for a subsequent training session.

After they had ample time to review these materials (15 minutes), the participants
were then allowed to ask any questions they might have regarding the information
presented to them. They were then administered the Training Aseigamnr Tee; . After they
completed the test they were asked to hand in all materials and sign up for a subsequent
session in which they were to receive their training assignment and allowed to work on the
Air Defense Task. The second session was conducted the following week.

For the second session, participants reported to the computer site in groups of 2-9
individuals. Upon their arrival they were reminded that the results of the m

Assignmenr files; which they took at the previous session were being used to make training

51

assignments. They then filled out the Pereeived Perfdrmanee as well as the M
Assignmem scales. The experimenter then read off the names of the individuals assigned
to remedial training (for half of the sessions the advanced group was assigned first). Both
groups were then escorted to their respective rooms. Participants in the control group were
simply escorted to the training room.

Upon arrival to the training room, the participants completed the Perceived
Fairies; Pre-Tmining Self-Efficacy, Ambutiens, and Merivaden 10 gm measures. The
control group completed the -Trainin lf-Effic c and Modvatien re Lear_n measures
only. At this point the training course was presented. The training manuals were labeled
"Advanced" or "Remedial" depending on the assignment. Even though the courses were
labeled differently, the content of the course was exacrly the same.

At the end of training, the participants completed the Post-Training Knewledge and
Peer-Training Perferrnanee measures. They were then given 40 minutes to work on the
computer simulation. They were inStructed that they could record a judgment at any time
during a trial. However, they musr render a judgment within 180 seconds. They were to
examine as many targets, as accurately as possible.

At this point the experiment was finished and the participants were thoroughly
debriefed (see Appendix Q). Special attention was paid to ensure that the participants
understood that their training assignment was randomly determined and that it in no way
reflected their level of performance on the Training Ageigrrmenr Test They were then
instructed not to discuss this experiment with anyone else until all necessary participants

had been obtained.

Analxticfittategx
The analytic strategy for this study followed the logic of the causal direction
hypothesized by the model in Figure 1. The moderating hypotheses were tested first,

52

followed by the structural hypotheses. Individuals in the experimental groups were used
for these analyses (n = 163). Moderation was tested using multiple regression (Cohen &
Cohen, 1981). A cross-product term is formed by multiplying the two independent
variables together. The dependent variable of interest is then regressed on the two
independent variables as well as the cross-product term. A significant regression weight
for the cross-product term indicates the presence of an interaction.

In order to determine the direction of the interaction, the regression equation was
recast into a simple-SIOpe equation (see Aiken & West, 1991). This procedure allows one
to plot the relationship between one of the independent variables and the dependent variable
as a function of values of the second independent variable.

The second set of analyses involved tesrs of the structural hyporheses concerning
the effects of self-efficacy, procedural justice, and disuibutive justice, on mOtivation to
learn. Multiple regression was used to ten this set of hypotheses (Cohen & Cohen, 1981).
Motivation to learn was regressed on self—efficacy, procedural jUStice, and distributive
justice in order to determine if these variables had a non-zero regression weight.

The third set of analyses involved tests of the mediation hypotheses implied by the
causal order in the model. The model hypothesizes that motivation to learn mediates the
relationship between pre-training self-efficacy and training outcomes. A test of mediation
was conducted to test this hypothesis (James & Brett, 1984). Evidence for mediation
exists when motivation to learn adds prediction of the dependent variable above and beyond
that accounted for by pre-training self-efficacy, distributive, and procedural justice. In
addition, pre-training self-efficacy, distributive, and procedural justice should not add any
significant increase in prediction above that of motivation to learn. This strategy allows for
the examination of both, the mediated relationship hypothesized by the model, as well as

the specific relationships between motivation to learn and performance outcomes.

53

The final set of analyses tested the hypothesized relationships between performance
outcomes, fairness perceptions, and affective outcomes. To test this set of hypotheses,
training reactions were regressed on performance outcomes and fairness perceptions. The
extent to which these predictors have significant regression weights indicates that they have
a unique effect on training reactions. A similar analysis was conducted with post-training
self-efficacy.

In addition to the tests of the hypotheses outlined above, exploratory analyses were
conducted to compare the effects of labeling against an unlabeled, or control, group. These
comparisons were carried out separately for each of the pre-trainin g characteristics (except
fairness perceptions), performance, and affective outcomes. Even though the effects of
framing were hypothesized to be moderated by. a number of individual characteristics, these
analyses allowed for the exploration of any overall effects that framing has as compared to
an unframed group.

These analyses were conducted using multiple regression techniques. Group
membership was dummy coded using two variables (see Cohen & Cohen, 1983). A
variable called "Advanced" was coded as 1 for the advanced group and 0 for the remedial
and control groups. A variable called "Remedial" was coded as l for the remedial group
and 0 for the advanced and control group. Each of the dependent variables of interest was
then regressed on these two dummy variables. The regression weights associated with
these two variables serve as a test of the difference between the respective group, and the
control group. Negative weights reflect lower means whereas positive weights reflect

higher means than the control group (see Cohen & Cohen, 1983).

RESULTS

E . . I!
Table 2 presents the means, standard deviations, and scale reliabilities for the

experimental groups. Table 3 presents the control group descriptive data. In general, the
scales showed sufficient reliabilities to proceed with the data analyses with the exception of
both pre- and post-training performance. The scales also showed sufficient range and there
appeared to be no problems associated with floor or ceiling effects. An exception is the
self-esteem scale. In general, the participants reported high levels of self-esteem (mean =
4.11 on a 5-point scale). The intercorrelations among the variables can be found in Table 4

for the experimental groups, and in Table 5 for the control group.

r i h se
The model suggests that the framing of training assignments influence training
outcomes through their effects on pre-training charaCteriStics. Given the causal flow of the
model, the factors which are related to these pre-training characteristics were first
examined. The first set of hypotheses pertained to the moderating effects of locus of
control, self-esteem, perceived performance, and expected assignment on pre-training self-

efficacy and fairness perceptions.

54

55
Table 2

Means, SDS, and Reliabilities of Study Variables: Experimental Groups

VARIABLE # ITEMS MEAN SD RELIABILITY

 

 

Self-Esteem 10 4. 1 l .53 .82
Locus of Control 1 a 10 3.48 .47 .65
Locus of Control 2 b 21 10.38 3.80 .71
Perceived Performance 5 2.68 .82 .90
Expected Assignment C l .74 .44 .---
Luck Attributions a 5 2.44 .78 .77
Effort Attributions a 5 3.47 .90 .86
Task Difficulty Attributions a 5 3.15 .77 .71
Ability Attributions a 5 2.60 .84 .72
Pie-Training Self-Efficacy 10 3.49 .66 .89
Distributive Justice 9 3.51 .69 .92
Procedural Justice 9 3.26 .50 .83
Motivation to Learn 10 3. 8 1 .64 .93
Pre-Training Knowledge 20 11.44 3.00 .70 ‘1
Post-Training Knowledge 20 13.17 2.90 .73 d
Pre—Training Performance 10 11.10 3.02 .40 ‘1
Post-Training Performance 10 11.55 2.97 .56 d
H.015?- n = 163.

a n = 95. b n = 68. C Coded: 0 = Advanced, 1 = Remedial. d Split-half reliability

estimate (odd-even). All others are coefficient alpha.

56

 

Table 2 (cont'd)

VARIABLE # ITEMS MEAN SD RELIABILITY
Post-Training Self-Efficacy 10 3.79 .53 .88
Learning 13 9.84 2.59 .82 d

# Targets b --- 26.46 5.42 .---
Accuracy b 1.29 .27 .---
Behavior b "' 2.78 1.02 .""
Reactions 9 3.74 .50 .80

 

lime. n = 163.
a n = 95. b n = 68. C Coded: 0 = Advanced, 1 = Remedial. d Split-half reliability

estimate (odd-even). All others are coefficient alpha.

57
Table 3

Means, SDS, and Reliabilities of Study Variables: Control Group

 

 

VARIABLE # ITEMS MEAN SD RELIABILITY
Self-Esteem 10 4.15 .58 .86
Locus of Control 1 a 10 3.40 .48 .65
Locus of Control 2 b 21 10.38 3.80 .71
Pre-Training Self-Efficacy 10 3.68 .56 .88
Motivation to learn 10 4.09 .53 .92
Pre-Training Knowledge 20 11.43 3.06 .71 C
Post-Training Knowledge 20 13.26 2.93 .74 C
Pre-Training Performance 10 11.74 2.65 .44 C
Post-Training Performance 10 11.40 2.93 .48 C
Post-Training Self-Efficacy 10 4.07 .61 .91
Learning 13 9.60 2.57 .68 C
# Targets --- 24.88 5.69 .---
Accuracy --- 1.30 .22 .---
Behavior --- 2.69 .91 .---
Reaction 9 3.97 .59 .83
Mme. n = 54

a n = 36. b n = 18. C Split-half reliability estimate (odd-even). All others are coefficient

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61

Ere-Tm’ning Self-Efficacy. Hypothesis 1 stated that locus of control will moderate
the relationship between assignment and pre-training self—efficacy. Table 6 presents the
results of the tests of this relationship for the 11-item scale. Table 7 presents the results for
the 21-item scale. The results of b0th tests indicate a non-significant interaction term,
suggesting that the hypothesized relationship was not observed. Even though this
relationship was not found, tests of moderation were conduCIed for each of the four
dimensions of attributions (luck, effort, task difficulty, and ability). It is possible that these
more proximal measures may have a stronger effect on the assignment, pre-training self-
efficacy relationship.

Table 8 presents the test of moderation for luck attributions. As can be seen, the
results indicate a non-significant interaction between assignment and luck attributions.
Table 9 presents the results for effort attributions. The results indicate the presence of a
moderating relationship. The interaction was p10tted using the simple-slope technique and
is presented in Figure 6. As the figure shows, of the individuals who attributed their
performance on the TAT to the amount of effort they expended, those assigned to advanced
training showed higher levels of pre-training self-efficacy than those assigned to remedial
training. In contrast, of those who did not feel that effort played a large role in their
performance, those assigned to advanced training showed lower levels of pre-training self-
effrcacy than those assigned to remedial training.

Table 10 presents the results of the test of moderation for task difficulty
attributions. The results indicate a significant interaction between assignment and task
difficulty attributions. Figure 7 is a plot of the results of this interaction. The observed
pattern was similar to that observed for effort attributions. Finally, Table 11 presents the
results of the test of moderation for ability attributions. The results indicate a significant
interaction between assignment and ability attributions. As can be seen in Figure 8, the

pattern of the interaction was the same as that observed for effort and task difficulty.

Table 6

Regression Analyses Results of the Test of Moderation of Locus of Control 1 on

Assignment, Pre-Training Self—Efficacy Relationship

 

VARIABLE B SE B 0 t
Assignment a (A) -.02 .12 -.02 -.16
Locus of Control b (LOC) .10 .10 .17 1.04
A x LOC .02 .12 .03 .15
Constant 3.55 .09 41.38”
R2 = .05

F (3,89) = 1.15, p >05

 

** p < .01
a Coded: O = Advanced, 1: Remedial

b Standardized

63
Table 7

Regression Analyses Results of the Test of Moderation of Locus of Control 2 on

Assignment, Pre-Training Self-Efficacy Relationship

 

VARIABLE 8 SE B B t
Assignment a (A) -.29 .18 -. 19 -1.57
Locus of Control b (LOC) -.07 .12 -.09 -.55

A x LOC .00 .19 .00 .02
Constant 3.57 .13 27.46," *
R2 = .05

F(3,65)=1.11,p>.05

 

**p< .01

a Coded: 0 = Advanced, 1: Remedial

b Standardized

64
Table 8

Regression Analyses Results of the Test of Moderation of Luck Attributions on

Assignment, Pre-Training Self-Efficacy Relationship

 

VARIABLE B SE B 0 t
Assignment 3 (A) -.22 . 10 -. 17 -2.29*
Luck Attributions b (L) -.31 .07 -.47 -4.78**
AxL 12 10 12 123
Constant 3.61 .07 52.31“
R2 = .17

F (3,159) = 10.88, p < .001

 

*p<.05,**p<.01

a Coded: 0 = Advanced, 1: Remedial

b Standardized

65
TABLE 9

Regression Analyses Results of the Test of Moderation of Effort Attributions on

Assignment, Pre-Training Self-Efficacy Relationship

 

VARIABLE B SE B [3 t
Assignment a (A) -.15 .10 -.11 -1.47
Effort Attributions b (E) .28 .07 .42 3.86”
A xE -.39 .10 -.42 -3.89**
Constant 3.53 - .08 42.22“
R2 = .11

F (3,159) = 6.61, p < .001

 

** p < .01
a Coded: 0 = Advanced, 1: Remedial

b Standardized

 

 

 

 

 

 

 

 

 

4.0
1

>3 3

3 3.8 '4

2
h 1
m l

I
v...
73 3.6 " ,
U2 Illa-II LOW Effort An!-
:3 —.— High Effort Am.
‘3 3.4 .. ’
C; +

" l
['7

g 3.2 -
0- t

i
3.0

Advanced Remedial

Training Assignment

Figure 6. Relationship Between Assignment, Effort Attributions, and Pre-Training Self-

Efficacy

67
Table 10

Regression Analyses Results of the Test of Moderation of Task Difficulty Attributions

on Assignment, Pre—Training Self-Efficacy Relationship

 

VARIABLE B SE B 0 t
Assignment a (A) -.15 .10 -.11 -1.49
Task Difficulty b (TD) .14 .08 .22 1.80

A xTD -.30 .10 -.35 -2.89**
Constant 3.58 .07 49.02”
R2 = .06

F (3,159) = 3.68, p < .05

 

** p < .01
a Coded: 0 = Advanced, 1= Remedial

b Standardized

 

 

 

 

 

 

 

3.8
q

>. 3.7 ..
8
.2 ‘
t 3.6 -
m 1
5'-
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g 3.4 .. —.— High Task Diff. Attr.
.E .
E.
[T 3.3 '1
o d
l-
9- 32 .

3.1

 

Advanced Remedial

Training Assignment

Figure 7. Relationship Between Assignment, Task Difficulty Attributions, and Pre-

Training Self-Efficacy .

Q

Table 11

Regression Analyses Results of the Test of Moderation of Ability Attributions

on Assignment, Pre-Training Self-Efficacy Relationship

 

VARIABLE B SE B B t
Assignment a (A) -.16 .10 -.12 -1.57
Ability Attributions b (B) .15 .07 .23 208*
A x B -.35 .10 -.36 -3.35**
Constant 3.52 .07 47.31”
R2 = .08

F (3,159) = 4.54, p < .01

 

*p<.05,**p<.01

a Coded: 0 = Advanced, 1: Remedial

b Standardized

70

 

 

 

 

 

 

 

 

3.8
q
>3
9 d
N
g 3.6 ..
m at
é
0 . .
m - "A‘" Low Abtltty Anr.
.- 1
.E
a 'l
h
‘1' 3.2 -
E
3.0

Advanced Remedial

Training Assignment

Figure 8. Relationship Between Assignment, Ability Attributions, and Pie-Training Self-

Efficacy.

71

In summary, the hypothesized interaction between training assignment and locus of
control was not observed. However, when the interaction between assignment and
attributions were examined, a pattern similar to that hypothesized for locus of control was
observed. Assigning individuals to remedial training as opposed to advanced training,
resulted in lower pre-training self-efficacy when performance on the TAT was attributed to
effort, task difficulty, and inborn ability. However, when these attributions were low,
assigning individuals to remedial training resulted in higher levels of pre-training self-
efflcacy than those assigned to advanced training.

Hypothesis 2 predicted that self-esteem will moderate the relationship between
assignment and pre-training self-efficacy. Table 12 presents the results of the test of
moderation. As can be seen from the table, the hypothesized interaction was not observed.
Self—esteem was positively related to pre-training self-efficacy, however, this relationship
did not differ across assignment conditions.

Eaimess Perceptions. Hyp0thesis 3 stated that perceived performance will
moderate the relationship between training assignment and fairness perceptions. Both
distributive and procedural justice dimensions of perceived fairness were measured. The
zero-order correlation between these two dimensions was r = .50, p < .01. This
correlation was not sufficiently large to justify combining the two measures into a single
measure of fairness. Therefore, moderator analyses were conducted for each of the two
dimensions separately.

Table 13 presents the results of the test of moderation using distributive justice as
the dependent variable. The results show a significant interaction between training
assignment and perceived performance. Simple slope analyses are plotted in Figure 9.
Individuals who perceived their performance on the TAT to be high, and were assigned to
remedial training, reported lower levels of disuibutive justice than those assigned to

advanced training. Conversely, those who perceived their performance on the TAT to be

72

low, and were assigned to remedial training, reported higher levels of distributive justice
than those assigned to advanced training.

Table 14 presents the results of the test of moderation using procedural justice as
the dependent variable. The results also indicate a significant interaction between perceived
performance and training assignment. As Figure 10 shows, the interaction was in the same
direction as that observed for disuibutive justice.

Hypothesis 4 predicted that expected assignment would moderate the relationship
between actual training assignment and perceived fairness. As with hypothesis 3, this test
was conducted for distributive and procedural justice separately. Table 15 presents the
results of the test of moderation with distributive justice as the dependent variable. The
results indicate a significant interaCtion between expecred assignment and actual training
assignment. As Figure 11 shows, those expecting to be assigned to advanced training, and
who were assigned to remedial training, reported lower levels of distributive justice than
those assigned to advanced training. Conversely, those expecting remedial training and
who were assigned to remedial training reported higher levels of distributive justice than
those assigned to advanced training.

Table 16 presents the results of the mm of moderation of expected assignment using
procedural justice as the dependent variable. The results revealed a significant interaction
between expected assignment and actual training assignment. Figure 12 presents the
graphic interpretation of this interaction. The figure shows a similar relationship as that

observed for distributive justice.

73
Table 12

Regression Analyses Results of the Test of Moderation of Self-Esteem on Assignment,

Pre-Training Self-Efficacy Relationship

 

VARIABLE B SE B B t
Assignment a (A) -.13 .10 -. 10 -1.24
Self-Esteem b (SE) .20 .07 .31 2.94**
AxsE -.16 .10 -.17 -1.59
Constant 3.55 .07 49.09”
R2 = .06

F (3,158) = 2.10, p <05

 

** p < .01
a Coded: 0 = Advanced, 1: Remedial

b Standardized

74
Table 13

Regression Analyses Results of the Test of Moderation of Perceived Performance on

Assignment, Distributive Justice Relationship

 

VARIABLE B SE B l3 t
Assignment a (A) .34 .09 .25 3.90"
perceived Performance b (P) .39 .06 .56 6.20M
Ax P -.76 .09 -.80 -8.78**
Constant 3.34 .06 54.17"
R2 = .37

F (3,159) = 30.76. p < .001

 

** p < .01
a Coded: 0 = Advanced, 1: Remedial

b Standardized

75

 

 

 

 

 

 

 

4.5
0 4.0‘
U
'5
W
a d
“'3
2 35‘ onto" Lochrc.Pcrf.
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..‘E 1 7
S-
d
.‘L’
a 3.0- .o'
O
o...
. A
2.5

Advanced Remedial

Training Assignment

Figure 9. Relationship Between Assignment, Perceived Performance, and Distributive

Justice.

76
Table 14

Regression Analyses Results of the Test of Moderation of Perceived Performance on

Assignment, Procedural Justice Relationship

 

VARIABLE B SE B B t
Assignment a (A) .00 .08 .00 .01
Perceived Performance b (P) .19 .06 .38 3.50“
A x P -.27 .08 -.38 -3.50**
Constant 3.26 .05 59.65M
R2 = .08

F (3,159) = 4.75, p < .01

 

** p < .01
a Coded: 0 = Advanced, 1: Remedial

b Standardized

 

 

 

 

 

 

 

3.6
q
3.5J
U
.2 3.4 -
“ I
u: t
5
"'° 3.3 -1
E ‘ unfiu- Low Pcrc. Pcrf.
% 3.2 _, —.— High Perc. Pcrf.
0
g 1
E
O- 3.1 -1
3.0-4
l
2.9

 

Advanced Remedial

Training Assignment

Figure 10. Relationship Between Assignment, Perceived Performance, and Procedural

Justice.

Regression Analyses Results of the Test of Moderation of Expected Assignment on

.. Assignment, Distributive Justice Relationship

78
Table 15

 

VARIABLE B SE B 0 t
Assignment 8 (A) -.71 .18 -.52 -3.92**
Expected Assignment 3 (E) -.60 .15 -.39 -4.08**
AxB 1.41 .21 1.00 6.73**
Constant 3.78 . 13 29.98”
R2 = .26

F (3,159) = 19.81, p < .001

 

** p<.01

a Coded: 0 = Advanced, 1: Remedial

 

 

 

 

4.0
at
1
3.8 -
O
.2 1
g '1
H 3.6 -
‘2‘ ‘ —.— Advanced Expected
.5 . oufi-u Remedial Expected
:2 3.4 ~ ,
l-
; 'l
'5 .
3.2 -
3.0

 

 

 

Advanced Remedial

Training Assignment

Figure 11. Relationship Between Assignment, ExpeCted Assignment, and Distributive

Justice.

a)
Table 16

Regression Analyses Results of the Test of Moderation of ExpeCIed Assignment on

Assignment, Procedural Justice Relationship

 

VARIABLE B SE B [3 t
Assignment 8 (A) -.42 .15 -.41 -2.77**
Expected Assignment 3 (E) -.30 .12 -.26 -2.43*
AxE .57 .18 .55 3.23“
Constant 3.47 .1 1 33.03”
R2 = .06

F (3,159) = 3.49, p < .05

 

*p<.05,**p<.01

a Coded: O = Advanced, 1: Remedial

81

 

 

 

 

 

 

3.5
3.4 "
0
.2
H d
v:
=
"’ 3.3 - _
E -—.-— Advanced Expected
1
§ noon-ll Remedial Expected ,
u 3.2 -
O
h
m '1
3.1 ‘
3.0

 

 

Advanced Remedial

Training Assignment

Figure 12. Relationship Between Assignment, Expected Assignment, and Procedural

Justice.

82

In summary, the results supported the hypotheses by showing that the participants'
beliefs regarding their my performance and their expectations about future assignments
served to moderate the relationship between training assignments and fairness perceptions.
When these beliefs and expectations were consistent with actual assignments, individuals
reported higher levels of distributive and procedural fairness than when they were counter
to actual assignments.

ngmm. The moderator analyses, taken as a whole, provide evidence supporting
the hypothesized relationships bemeen training assignments and pre-training
characteristics. Specifically, the results show that individuals who attributed their
performance on the TAT to effort, task difficulty, and ability, reported higher levels of pre-
training self-efficacy when assigned to advanced training as opposed to remedial training.
For those not attributing past performance to these factors, the opposite effect was found.
For this latter group, assignment to remedial training resulted in higher levels of pre-
tr'aining self-efficacy than assignment to advanced training

In addition, evidence was found for the hypOthesized effects of training assignment
on fairness perceptions. Both, perceptions of past performance, as well as expectations
regarding training assignments, were found to moderate the relationship between training
assignments and fairness perceptions. When perceptions and expectations were consistent
with actual assignment (e.g.,. expected remedial, received remedial), individuals reported
more positive levels of distributive and procedural juStice than when these factors were

inconsistent with actual assignments.

W

The next set of hypotheses deal with the effects of the differences in pro-training
self-efficacy and fairness perceptions discussed in the previous section. These differences

were hypothesized to affect motivation to learn and affeCtive outcomes directly, as well as

83

performance outcomes, indirectly. Pre-training self-efficacy was hypothesized to affect
training outcomes through its influence on motivation to learn. The tests of these
hypotheses are discussed below.

W. Hypothesis 5 predicted that pre-training self-efficacy will be
positively related to motivation to learn. Similar relationships were predicted between
distributive justice, procedural justice, and motivation to learn by Hypotheses 6 and 7,
respectively. The goal of this set of analyses was to determine the independent effects of
these variables on motivation to learn. Therefore, motivation to learn was regressed on
pre-training self-efficacy, distributive justice, and procedural justice, simultaneously. The
results of this test can be found in Table 17. The results show that pre-training self-
efficacy was positively related to morivation to learn ([3 = .29, p < .01), giving support to
Hypothesis 5. The results also show non-significant effects for disuibutive and procedural
justice, suggesting a lack of support for Hypotheses 6 and 7. All variables accounted for
13% of the variance in motivation to learn.

Eggofinance Qutcomes. HypOthesis 8 predicred that motivation to learn will be
positively related to actual learning. The model also suggests that the effects of motivation
to learn are above and beyond those of pre-training self-efficacy, distributive, and
procedural justice. Therefore, Hypothesis 8 was tested in conjunction with a test of
mediation (James & Brett, 1984). Two measures of learning were used to test this
hypothesis.

First, pre-training knowledge scores were covaried out of post—training knowledge
scores prior to conducting the tesr of mediation (Arvey & Cole, 1989). The results of this
test can be found in Table 18. The results show that motivation to learn was positively
related to post-training knowledge ([3 = .18, p < .01). In addition, the results show that
motivation to learn added a significant increase in explained variance above pre-training

self-efficacy, distributive, and procedural justice (AR2 = .03, p < .01). Finally, pre-

84

training self-efficacy, distributive, and procedural justice did not add prediction above the
effects of motivation to learn. All variables accounted for 43% of the variance of post-
training knowledge scores. These results give support to Hypothesis 8 as well as the
mediated relationship suggested by the conceptual model.

The second measure of learning used was the 13-item Learning measure. Table 19
presents the results of the test of Hypothesis with this learning measure.The results show
that motivation to learn was positively related to learning ([3 = .35, p < .01). In addition,
the results show that motivation to learn added a significant increase in explained variance
above pre-training self-efficacy, distributive, and procedural justice (AR2 = .11, p < .01).
Finally, pre-training self-efficacy, distributive, and procedural justice did not add prediction
above the effects of motivation to learn. All variables accounted for 12% of the variance in
learning scores. These results also give support to Hypothesis 8 as well as the mediated
relationship suggested by the conceptual model.

Hypothesis 9 predicted that motivation to learn will be positively related to
behavioral outcomes of training. The "Behavior" measure described in the Measures
section served as the dependent variable for these analyses. As with Hypothesis 8, this
hypothesis was also tested in conjunction with a test of mediation. Table 20 presents the
test of this hypothesis. The results show that morivation to learn was positively related to
behavioral outcomes of training ([3 = .28, p < .05). Individuals reporting higher levels of
motivation to learn were more likely to measure more interactions while performing the Air
Defense Task than those reporting low levels of motivation to learn. However, the test of
mediation was not supported. Differences in degrees of freedom, rather than a true
relationship, appeared to account for the significance of motivation to learn over pre-

training charactersistics All variables accounted for 15% of the variance in behavior scores.

86
Table 17

Regression Results of the Effects of Distributive Justice, Procedural Justice,

and Pre-T raining Self-Efficacy, on Motivation to Learn

 

VARIABLE B SE B B t
Distributive Justice . 14 .08 .15 1.71
Procedural Justice . 16 . l 1 . 12 1.44
Pre-Training Self-Efficacy .28 .07 .29 3.86“
Constant 1.86 .41 4.53“
R2 = .13

F (3,159) = 8.06, p < .001

 

** p< .01

86
Table 18

Hierarchical Regression Results of Test of Mediation of Motivation to Learn

on Post-Trainin g Knowledge

 

 

VARIABLE [3 8 AR2 R2
EQUATION 1

STEP 1:

Pre-Training Knowledge .5 8 * * 39* * 39* *
STEP 2:

Pre-Training Self-Efficacy -.09 .01 .40**
Distributive Justice -.1 1

Procedural Justice .05

STEP 3:

Motivation to Learn .18** .03** .43**
EQUATION 2

STEP 1:

Pre-Training Knowledge .58** .39” .39**
STEP 2:

Motivation to Learn .18” .02* .41M
STEP 3:

Pro-Training Self-Efficacy -.09 .01 .43**
Distributive Justice —.1 1

Procedural Justice .05

n = 162

*p<.05, **p<.01

a These are standardized regression weights from the final equation, after all variables have

been entered.

Table 19

Hierarchical Regression Results of the Test of Mediation of

Motivation to Learn on Learning

 

VARIABLE a AR2 R2
EQUATION 1

STEP 1:

Pre-Training Self-Efficacy -.15 .01 .01
Distributive Justice -.1 1

Procedural Justice .06

STEP 2:

Motivation to Learn .35” .11** .12”
EQUATION 2

STEP 1:

Motivation to Learn 35* * .09* * .09**
STEP 2:

Pre-Training Self-Efficacy -.15 .03 .12* *
Distributive Justice -.1 1

Procedural Justice .06

 

n=162
*p<.05, **p<.01

88

Table 20

Hierarchical Regression Results of the Test of Mediation of

Motivation to Learn on Behavior

 

VARIABLE [3 AR2 R2
EQUATION 1

STEP 1:

Pre-Training Self-Efficacy -.07 .09 .09
Distributive Justice -.02

Procedural Justice .24

STEP 2:

M0tivation to Learn 28* .O6* .15*
EQUATION 2

STEP 1:

Motivation to Learn .28* .09* .09*
STEP 2:

Pie-Training Self-Efficacy -.07 .06 .15*
Distributive Justice -.02

Procedural Justice .24

 

n=68
*p<.05

89

Hypothesis 10 predicted that motivation to learn will be positively related to post-
training task performance. The mediating effects of motivation to learn were also tested in
conjunction with this hyp0thesis. Three measures of performance were used as dependent
variables in these analyses. First, pre-training performance scores were covaried out of
post-training performance scores in order to assess changes in performance scores as a
result of training. Table 21 presents the results of these analyses. The results failed to
support Hypothesis 10. Motivation to learn was not related to changes in performance
scores.

Secondly, accuracy scores were used as the dependent variable in the test of
Hypothesis 10. These scores represent an individual's average number of points (-2 to +2)
received across different trials of the Air Defense Task. Table 22 presents the results of
this test. The results show a marginal poshive relationship between motivation to learn and
accuracy (B = .24, p < .10). In addition, the mediation hypothesis was not supported.
The results do show a direct, positive relationship between procedural justice and accuracy
(B = .30, p < .05). The amount of variance in accuracy prediCted was only marginally
significant. Thus, the results failed to provide support for Hypothesis 10.

Thirdly, the number of trials completed in the 40 minute interval was used as the
dependent variable in the test of Hypothesis 10. The results are reported in Table 23. The
results failed to support Hypothesis 10. There was no relationship between motivation to
learn and the number of trials completed. The variables as a whole, failed to account for

any significant amount of variance in number of trails.

90
Table 21

Hierarchical Regression Results of the Test of Mediation of

Motivation to Learn on Posr-Training Performance

 

 

VARIABLE B ARZ R2
EQUATION 1

STEP 1:

Pre-Training Performance .31” .12” .12M
STEP 2:

Pre-Training Self-Efficacy .09 .01 .13**
Distributive Justice .04

Procedural Justice .04

STEP 3:

Motivation to Learn . 1 1 .00 . 13* *
EQUATION 2

STEP 1:

Pre-Training Performance .31” .12M .12”
STEP 2:

Motivation to Learn .03 .00 .12**
STEP 3:

Pre-Training Self-Efficacy .09 .01 .13**
Distributive Justice .04

Procedural Justice .04

n = 162

*p<.05, **p<.01

91
Table 22

Hierarchical regression Results of the Test of Mediation of

Motivation to Learn on Accuracy

 

 

VARIABLE B ARZ R2
EQUATION 1

STEP 1:

Pre-Training Self-Efficacy .07 .13** .13**
Distributive Justice -.08 .

Procedural Justice .30’" *

STEP 2:

Motivation to Learn .24* .04* .17”
EQUATION 2

STEP 1:

Motivation to Learn 24* .10M .10”
STEP 2:

Pre-Training Self-Efficacy .07 .07 .17”
Distributive Jusrice -.O8

Procedural Justice .30**

n = 68

*p<.10, **p<.05

92
Table 23

Hierarchical Regression Results of the Test of Mediation of

Motivation to Learn on Number of Trials

 

VARIABLE [3 AR2 R2
EQUATION 1

STEP 1:

Pie-Training Self-Efficacy .07 .00 .00
Distributive Justice -.03

Procedural Justice .00

STEP 2:

Motivation to Learn -. 16 .02 .02
EQUATION 2

STEP 1:

Motivation to Learn -. 16 .02 .02
STEP 2:

Pre-Training Self-Efficacy .07 .00 .02
Distributive Jusrice -.O3

Ptocedural Justice .00

 

n=68

93

In summary, the results indicate that pre-training mmivation was related to a
number of performance outcomes. First, those reporting high levels of mOtivation to learn
actually learned more of the training content. Secondly, those who reported higher levels
of motivation to learn actually applied more of the training content (measured more
interactions) than those reporting low levels of motivation to learn. Thirdly, the hypotheses
regarding performance (post-training performance and accuracy) were not supported. Only
marginal effects were found for the effects of motivation to learn on these two measures of
performance. Finally, the hypothesized role of motivation to learn as a mediator of the
effects of pre-training self-efficacy and fairness perceptions on learning and behavior was
also supported.

Affegg've Qutggmes. The final set of hypotheses dealt with the relationship between
fairness perceptions, performance outcomes, and affective outcomes of training. First,
Hypotheses 11 through 14 predicted that fairness perceptions and performance outcomes
will be positively related to training reactions. The correlations in Table 2 suggest support
for Hypotheses 11 & 12. Higher fairness perceptions were related to more positive
training reactions (r = .17 and r = .24, p < .05 for procedural and distributive fairness
respectively). In order to detemrine if the performance outcomes had an independent effect
on training reactions, all performance outcomes were included in the same regression
equation. Table 24 presents the results of this test. As the results indicate, there were not

independent effects between fairness perceptions, performance outcomes, and reactions.

Regression Results of the Effects of Fairness Perceptions and

Performance Outcomes on Training Reactions

94
Table 24

 

VARIABLE B SE B B t
FAIRNESS PERCEPTION S

Distributive Justice .05 .1 1 .07 .44
Procedural Justice .06 . 18 .06 .33
PERFORMANCE OUTCOMES

Learning .02 .03 . 12 .67
Post-Training Knowledge .00 .03 .01 .07
Post-Training Performance .00 .03 -.01 -.05
Number of Trials .00 .01 -.O3 -.23
Behavior .01 .09 .02 .10
Accuracy .20 .32 .10 .63
Constant 2.87 .66 4.33”
R2 = .06

F (8,59) = .50, p > .05

 

n=68
**p<.001

95

Hypotheses 15 through 18 predicted a positive relationship between fairness
perceptions, performance outcomes, and post-training self-efficacy. Table 25 presents the
results of the test of these hypotheses. The results indicate that, after partialing out pre~
training self-efficacy scores, accuracy was positively related to post-training self-efficacy
(B = .31, p < .05); thus supporting Hypothesis 18. However, none of the other
hypotheses were supported. All variables accounted for 51% of the variance in post-
training self-efficacy.

Summary. Taken as a whole, the results of the tests of the Structural Hypotheses
give partial support for the hypothesized role of pre-training characteristics on training
outcomes. Specifically, the results indicate a positive relationship between pre-training
self-efficacy and motivation to learn. Motivation to learn was then shown to be positively
related to learning and behavioral outcomes of training. Furthermore, accuracy was shown
to be positively related to pOSt-trainin g sell-efficacy, after accounting for differences in pre-
training self-efficacy.

Fairness perceptions were also found to be positively related to training reactions.
Those who perceived their training assignments to be fair reported more positive attitudes
toward the training program as a whole. In general pre-training self-efficacy and
motivation to learn were shown to be the most important variables relating training
assignments to training outcomes.

The tests described above provide a test of the process by which the framing of
training assignments affect training outcomes. However, another set of analyses were
conducted in order to determine the effects of framing as compared to a control group
receiving no framing. In this way, the general effects of framing as a whole on key

process and outcome variables can be determined. These analyses are described below.

Regression Results of the Effects of Fairness Perceptions and

%
Table 25

Performance Outcomes on Post-Training Self-Efficacy

 

 

VARIABLE B SE B B t
Pre-Training Self-Efficacy .46 .08 .58 6.06**
FAIRNESS PERCEPTION S

Distributive Justice . 14 .09 .17 1.49
Pr0cedural Justice -.07 . 15 -.06 -.44
PERFORMANCE OUTCOMES

Learning .02 .03 .11 .89
Post-Training Knowledge -.05 .03 -.24 -1.73
Pom-Training Performance .03 .03 .11 1.06
Number of Trials .00 .01 .01 .10
Behavior .01 .07 .02 .19
Accuracy .69 .27 .31 2.50”“
Constant 1.05 .60 1.74
R2 = .51

F (9,58) = 6.59, p < .001

 

n=68

*p<.05,**p<.01

W

The dummy coding strategy described in the Analytic Strategy section was used to
examine the overall effects of framing compared against the control grOup. Each dependent
variable of interest was regressed on the dummy coded variables in separate analyses.
However, these analyses were grouped in three main categories for ease of presentation.

Ere-Training Characteristics. First, the effects of framing on pre-training self-
efficacy and m0tivation to learn, were examined. Fairness perceptions were not collected
from the control group given the fact that these were irrelevant variables. Table 26 presents
a summary of the results if these tests. The results show that b0th experimental groups had
significantly lower levels of motivation to learn than the control group (B = -.26, p < .01
for the advanced group, and B = -.19, p < .05 for the remedial group). Framing effects
accounted for 4% of the variance in motivation to learn scores. Framing was not related to
pre-training self-efficacy.

Pgrformance Outcomes. Secondly, the effects of framing on performance
outcomes were examined. Table 27 presents the results of these tests. The results indicate
that the experimental groups did not differ significantly from the control group. However,
the two experimental groups differed from each other in terms of accuracy. Individuals
assigned to remedial training were significanty more accurate in performing the Air Defense
Task than those assigned to advanced training ( r= .37, p < .05). This result suggests a
more direct effect of framing on training outcomes than that investigated in this study.

Affegtive Outcomes. Finally, the effects of framing on affective outcomes were
examined. The results are presented in Table 28. The results indicate that both
experimental groups had significantly lower levels of post-training self-efficacy and more
negative training reactions than the control group. These results suggest that, while
affecting performance outcomes indirectly through self-efficacy and motivation to learn,

framing has a direct impact on affective outcomes of training. The results also suggest that

98

framing, regardless of whether it is positive or negative, results in lower levels of affective

outcome S .

99
Table 26

Regression Results of Control Group Comparisons: Pre-Trainin g Characteristics

 

 

Pre-Training Characteristics Advanced a Remedial a R 2
Pro-Training Self-Efficacy -.09 -.20* .03
Motivation to Learn -.26** -.19* .04*
n = 213

*p<.05,**p<.01

a These numbers represent beta-weights of dummy coded variables comparing the

experimental groups to the control group.

100
Table 27

Regression Results of Control Group Comparisons: Performance Outcomes

 

 

Performance Outcome Advanced a Remedial a R 2
Learning b .00 .09 .01
Post-Training Knowledge b9 -.02 -.03 .00
Post-Training Performance b,d .10 .00 .01
Number of Trials C .19 .09 .02
AccuracyC -.20 .18 .12M
Behavior9 -.15 .23 .12**
** p < .01

a These numbers represent beta-weights of dummy coded variables comparing the
experimental groups to the control group. b n = 213. C n = 85. 9 After covarying pre-

training knowledge and performance.

101
Table 28

Regression Results of Control Group Comparisons: Affective Outcomes

 

 

Affective Outcome Advanced a Remedial a R 2
Post-Training Self-Efficacy b -.18* -.16* .02*
Reacrions -.18* -.23** .03*
n = 213

*p<.05,**p<.01

a These numbers represent beta-weights of dummy coded variables comparing the
experimental groups to the control group. b After covarying out the effects of pre-training

self-efficacy.

DISCUSSION

The purpose of the present study was to expand research on training effectiveness
by examining the extent to which pre-training context factors affect uaining outcomes. In
particular, the present study examined the framing of training assignments (assigned,
remedial) on training outcomes. A conceptual model was developed which suggests that
framing affects training outcomes through pre-training characteristics, namely, fairness
perceptions and pre-training self-efficacy. These were hypothesized to affect training
outcomes through motivation to learn. Finally, a number of individual characteristics were
hypothesized to moderate the relationship between training assignments and pre-trainin g
characteristics.

The discussion of this study is organized in the following manner. First, a
summary of the study results is presented. Second, implications of the study results along
with a discussion of future research are presented. Finally, limitations of the study are

presented.

W
W. The first set of hypotheses examined suggested that a

number of individual characteristics would moderate the relationship between training
assignment and pre-training characteristics. Hypothesis I predicted that locus of control
would moderate the relationship between assignment and pre-training self-efficacy. Two
tests of this hypothesis were conducted, one for each measure of locus of control. Neither

test found support for Hypothesis 1. However, when the actual attributions which

102

 

103

individuals made regarding their training assignment were used in the analyses, a pattern
similar to that hypothesized was observed. For individuals who attributed their past
performance to effort, task difficulty, and ability, assignment to advanced training resulted
in higher levels of pre-training self-efficacy than assignment to remedial training.
However, for those not making these attributions, the opposite pattern was observed.
Assignment to remedial training resulted in higher levels of pre-training self-efficacy than
assignment to advanced training.

There are a number of possible reasons for the failure to find the hypothesized
effects of locus of control. First, the some what low scale reliabilities as well as the smaller
sample sizes associated with these measures may have decreased statistical power, thus
making it more difficult to find a significant interaction. Secondly, the hypothesized
mechanism by which locus of control will moderate this relationship was not observed. It
was hypOthesized that locus of control will be consistently related to an individual's
propensity to make internal vs external attributions. However, the correlations between
locus of control and attributions did n0t support this mechanism (see Table 4). Therefore it
is not surprising that when the actual attributions were used, the hypothesized relationships
were observed. This brings into question the construct validity of the locus of control
measures.

However, the results using the attribution measures give support to the main thesis
presented in this study. Framing of training assignments can communicate feedback
ragarding past performance and affect an individual's level of efficacy going into training.
However, consistent with attribution theory (Weiner, 1985) and social learning theory (eg.
Bandura, 1977), this feedback did not have a direct relationship with efficacy expectations.
Those who attributed their past performance to seemingly internal (effort) or uncontrollable

(task difficulty, ability) causes, were most affected by the framing of training assignments.

The implications of this and other findings are discussed in more detail later.

104

Hyp0thesis 2 predicted that self-esteem would moderate the relationship between
assignment and pre-training self-efficacy. Those high in self-esteem were expected to
maintain a high level of efficacy, regardless of training assignment. Those low in self-
esteem, on the other hand, were expected to have higher levels of self-efficacy when
assigned to advanced training than when assigned to remedial training. The hypothesized
interaction was not observed. Instead, a direct relationship between self-esteem and pre-
training self-efficacy was observed. Those high in self-esteem reported higher levels of
self-efficacy than those low in self-eSteem, regardless of training assignment.

It is possible that the nature of the training assignment manipulation did not serve to
enhance the efficacy of low self-esteem individuals assigned to advanced training, as
expected. Given the novelty of the task, perhaps assignment to advanced training was not
a sufficiently relevant piece of positive feedback to overcome a low self-image. In
addition, most of the panicipants reported a relatively high level of self-esteem (mean =
4.11, SD = .53 on a 5-point scale). The sample may n0t have contained enough truly low
self-esteem individuals to find the effect hypothesized.

Hyp0thesis 3 predicted that an individual‘s perceived performance on the TAT
would moderate the relationship between training assignment and fairness perceptions. Of
those perceiving high levels of pasr performance, assignment to advanced training would

result in higher fairness perceptions than assignment to remedial training. The results
supported the hypothesized relationship for both distributive, and procedural justice
measures of fairness. Similar results were hypothesized, and found, for expected
assignment (Hypothesis 4).

It is n0t surprising that similar results were found for perceived performance and
expected assignment. Those who thought they performed well were more likely to be the

ones expecting to be assigned to advanced training. In fact, the observed correlation

between perceived performance and expected assignment was r = -.60, p < .01. Taken as

 

105

a whole, the results support the hypothesis that framing can confirm or disconfirm an
individual's expectations regarding their training needs. This confirmation process results
in differences in fairness perceptions regarding training assignments.

W. The next set of hyporheses dealt with the process by which
pre-training characteristics resulting from training assignments lead to differences in
training outcomes. Hypotheses 5 through 7 predicted that differences in fairness
perceptions and pro-training self-efficacy would be positively related to differences in
motivation to learn. Only self-efficacy was shown to be positively related to motivation to
learn, supporting Hyporhesis 5. Distributive and procedural justice were not found to be
related to motivation to learn. However, this lack of support of Hypotheses 6 & 7 may
have been a result of statistical anifacts.

As table 4 demonstrates, distributive and procedural justice both, had positive
correlations with motivation to learn (r = .19 and _r_ = .20, both 9 < .05, respectively). In
addition, they were not highly correlated with self-efficacy. However, they were
sufficiently correlated with each other to create a multicollinearity problem (Cohen &
Cohen, 1983; Kennedy, 1992). This high intercorrelation has the effect of increasing the
standard errors of the regression coefficients. One remedy to this problem might be to
combine the distributive and procedural justice measures into one measure of fairness
perceptions. When this is done, fairness (or = .91) is shown to have a positive effect on
motivation to learn (B = .23, p < .01).

However, even though the high observed reliability of the combined fairness
measure suggests the presence of one construct, factor analyses reveal two distinct factors.
Cortina (1993) suggested that it is possible to find high internal consistency estimates of
reliability for a set of multi-factorial items, especially when a large number of items are

used. Therefore, it appears that while being distinct, yet correlated, constructs, the

variance shared by distributive and procedural justice (r2 = .25) is the same variance that

106

they share with modvation to learn. The best estimate of this shared relationship with
motivation to learn is the regression weight reported earlier using the combined fairness
scale (B = .23, p < .01).

These ancillary analyses provide support for Hyporheses 6 and 7. An individual's
perceptions of fairness regarding their training assignment are positively related to their
morivation to learn during the training program. These findings give further support to the
general thesis presented in this study regarding the potentially negative effects of the
framing of training assignments. Low levels of self-efficacy and fairness perceptions are
likely to lead to low level of motivation to learn. The next set of hypotheses examined the
effects of motivation to learn on training outcomes.

Hypotheses 8, 9, and 10 predicted that motivation to learn would be positively
related to performance outcomes. Hypotheses 8, predicting a positive relationship between
motivation to learn and learning, was supported using two different measures of learning.
The first test represented a stronger test of this hypothesis because it covaried out
differences in pre-training knowledge (Arvey & Cole, 1989; Goldstein, 1993). The
second test employed a post-training measure only. However, both tests supported the
hypothesis by showing that motivation to learn was positively related to actual learning.
This relationship was observed even after controlling for differences in pre-training self-
efficacy and fairness perceptions. These findings also support the general framework of
the model which hypothesizes that motivation to learn actually mediates the relationship
between pre-training self-efficacy, fairness perceptions, and training outcomes.

Hypothesis 9 predicted that motivation to learn will be positively related to
behavior. This hypothesis was also supported. Individuals reporting higher levels of
modvation to learn actually applied more of the training content while performing the Air

Defense Task than those reporring lower levels of morivation to learn. In particular, they

107

were more likely to measure more interaCtions before making a judgment, as was discussed
in the training course.

Hypothesis 10 predicted that motivation to learn would be positively related to post-
training performance. Three measures of performance were used to test this hypothesis.
These included post-training performance, accuracy, and number of trials completed. The
results indicate only a marginal effect for motivation to learn on accuracy. The results also
revealed a direct effect for procedural justice on accuracy. Those reporting higher levels of
procedural justice were more accurate in performing the Air Defense Task. The low
reliabilities of both pre- and post-trainin g performance may have been responsible for the
non-significant results of that particular test of Hypothesis 10. Furthermore, the number of
trials measure may have not been a sensitive enough measure of training effects.
Individuals were not explicitly taught how to complete trials as quickly as possible. In fact,
if the training was applied, individuals may actually take longer to complete a trial. Since
the focus on training was on quality, it is perhaps not surprising that number of trials, a
quantitative measure of performance, was not affected by differences in motivation to learn.

In general, the results show that motivation to learn was positively related to
learning and behavioral outcomes of training. In addition, motivation to learn mediated the
effects of pie-training self-efficacy and fairness perceptions on these outcomes. The final
set of hypotheses dealt with the relationship between fairness perceptions, performance
outcomes, and affective outcomes of training.

Hypotheses 11 through 14 predicted that fairness perceptions and performance
outcomes would be positively related to training reactions. The zero-order correlations
showed that those who perceived their training assignments as fair were more likely to
report positive attitudes toward the training program as a whole. However, a test of the
independent effects of fairness and performance outcomes failed to provide support to these

hypotheses.

108

There are several possible reasons for these results. First, the smaller sample size
resulting from listwise deletion, along with the relatively large number of predictors, may
have resulted in low levels of statistical power (Cohen, 1988). However, when pairwise
deletion is used, similar results are observed. Second, it is simply possible that no
relationship exists between performance outcomes and training reactions (Alliger & Janak,
1989). In fact, there is research which suggests that reactions are more a results of pre-
training characteristics and may actually lead to differences in performance outcomes
(Mathieu, Tannenbaum, & Salas, 1992). Factors related to training reactions will be
discussed again later in this semion.

HypOtheses 15 through 18 predicted that fairness perceptions and performance
outcomes would be positively related to post-training self-efficacy. The results supported
Hypothesis 18 which prediCted that post-training performance (accuracy) would be related
to post-training self-efficacy. These results are consistent with past research on self-
efficacy and training effectiveness which show that an individual's past performance is an
important component of efficacy judgments (Bandura, 1977; Gist & Mitchell, 1992;
Tannenbaum, et al., 1991). Accuracy was the only performance measure which provided
the trainee with immediate feedback regarding performance. The presence of this feedback
probably accounted for the significant relationship between accuracy and post-training self-
efficacy. The lack of relationship between other performance outcomes and post-training
self-efficacy could have been due to the lack of feedback from those measures.

The analyses just presented provide evidence for the hypothesized effects of
framing of training assignments on training outcomes. How a training program is framed
(advanced vs remedial) results in differences in pro-training self-efficacy and fairness
perceptions. These differences affeCI m0tivation to learn which then affects uaining
outcomes. In addition to these analyses, a series of exploratory analyses were also

conducted. Rather than investigating the effects of different frames, the exploratory

109

analyses determined the effects on framing, positive or negative, against an unframed, or
control, group. In this way, any overall effects of framing in key variables can be
determined.

QQDEQI Group Comparisons. The results of these tests indicate that both
experimental groups had lower overall levels of pre-training motivation than the control
group. In addition, experimental groups reported lower levels of training reactions
compared to the control group. In addition, experimental groups showed less gains in self-
efficacy as a result of training than control groups after taking into account differences in
pre-training self-efficacy. No differences in performance outcomes were found between
experimental and control groups. These results suggest that framing training assignments
based on differences in performance levels tends to have the largest impact on how an
individual perceives the training experience and the resulting level of efficacy developed
from this experience. However, as evidenced by the results of the experimental groups,
there was evidence for a direct effect between framing and accuracy. Those assigned to
remedial training were more accurate in perfomring the Air Defense Task than those
assigned to advanced training. The implications of these findings on training research and

practice are now discussed.

Impligasigns and Directions for Future Research

The results of this study are consistent with mounting evidence in the training

 

literature which suggests that training effectiveness is more than just a function of training
design (Baldwin, Magjuka, & Loher, 1991; Mathieu, Tannenbaum, & Salas, 1992: Noe,
1986; Quifiones, Ford, Sego, & Smith, under review). Pre-training context and individual
characteristics can influence training outcomes beyond any training design effects. The
results of this study have implications for research and practice in anumber of areas related

to training effectiveness.

' 110

Framing. First, this study suggeSts that training assignments are not value free.
Clearly, the intent of training programs is to develop or improve a specific set of skills
(Goldstein, 1993). However, the way in which the need for training is framed can affect
an individual's pre-training characteristics. These findings are consistent with recent
research on framing effects and training (Martocchio & Webster, 1992). This is an
important finding given the recent trends toward a continuous learning strategy (Noe &
Ford, 1992). Organizations must take into account the way in which different training
programs are perceived by organizational members. Future research could examine the
way in which training programs acquire different frames over time.

The results observed also suggest there are a number of ways in which
organizations can communicate expectations to individuals. In this case, the framing of
training assignments led some individuals to believe that they were high performers
whereas others thought they were low performers. Research on the "Galatea Effect" has
shown that interventions or situations which boost an individual's expectations regarding
future performance usually lead to actual increases in performance (cf. Eden & Kinnar,
1991).

In addition, supervisors or coworkers may develop expectations about others'
performance based on training assignments. Individuals may actually expect less from
someone who has been assigned to a "remedial" training program. By contrast, a certain
degree of "halo" may deveIOp around someone assigned to specialized or "advanced"
training. Research has shown that expectations about others can result in differences in
performance on the part of these Other individuals. This is the "Pygmalion" effect
discussed earlier (cf. Eden & Shani, 1982). Future research could examine the extent to
which training assignments "stigmatize" individuals and affect others‘ behavior toward

them.

111

The results of this study are also consistent with the work by Dweck regarding
learning versus mastery orientations (Dweck, 1986). Making training assignments
contingent on past performance may set up a mastery orientation and decrease an
individual's overall reactions to the learning experience. In addition, morivation to learn
may be decreased. Negative reactions could undermine the philosophical underpinnings of
a continuous learning strategy. Future research could examine the extent to which framing
affects attitudes toward training in general, and the organization's training programs, in
particular.

The present study also has implications for research on aptitude/treatment
interactions. Not only must researchers take care of any potential side effects of
categorizing programs and individuals, but they must also begin to examine individual
characteristics related to training effeCtiveness. Research must go beyond looking at
general cognitive ability as the most important determinant of differences in reactions to
training programs (e. g. Fleishman & Mumford, 1989). Clearly attributions and self
perceptions of training needs can influence training effectiveness. Research could examine
other individual difference variables such as personality characteristics which could be
related to how different training programs are perceived.

Since attributions can affeCI reactions to training assignments, perhaps attributional
training can help ensure that individuals do not make attributions which are likely to lower
their perceptions of efficacy (FOSterling, 1985). Research is needed on the effectiveness of
attributional interventions in eliminating any negative effects of training assignments on
efficacy perceptions.

The control group comparisons, however, do suggest that assigning individuals to
remedial training resulted in higher levels of accuracy. This suggesrs that the negative
feedback implied in remedial assignments actually led to higher levels of performance.

Thus, even though the prOposed mechanisms examined in this study were generally

112

supported, this direct mechanism was also found. These findings are similar to those
obtained by Podsakoff & Farh (1989). In that study, individuals who were given negative
feedback performed at higher levels than those receiving positive feedabck. However, the
study also found a positive relationship between self-efficacy and performance. In
addition, similarly to the present study, no direct relationship was found between feedback
sign and self-efficacy.

These findings suggest that two parallel prOcesses are responsible for creating
higher levels of performance. First, self-efficacy enhancement resulted in higher
motivation to learn and actual learning, which was then related to performance. On the
other hand, giving negative feedback resulted in significantly better performance. This
brings into question whether one should be concerned about negative framing or whether
negative frarrrings might actually be beneficial.

The current study suggesrs that, at least in the short term, individuals may actually
do better if the are assigned to a remedial program. However, this conclusion must be
tempered by the fact that this was a novel task which participants were only performing
once and in which most individuals expeCted to be assigned to remedial training (74% of
participants expected to be assigned to remedial training). There is a body of literature
which suggests that repeated negative feedback can lead to many negative outcomes (Ilgen,
Fisher, & Taylor, 1979). The concept of learned helplessness is based on the principle
that, when confronted with continued failure, an organism will just give up (Seligman,
1975). Research suggest that continued persitence is necessary for task perforrnacne (Gist
& Mitchell, 1992).

The educational literature also suggests that continued negative feedback is
detrimental to prolonged motivation and learning (Rosenthal] & Jacobson, 1968; Schunk,
1982). It appears that attributions may be a key component in this process. In a novel

task, individuals may be more likely to take negative feedback as evidence of their lack of

113

experience with the task. However, with more familiar tasks, individuals may tend to
discount negative feedback and lower their m0tivation going into training. Therefore,
future research is needed which examines the effects of negative feedback due to training
assignments over a longer period of time and with tasks which are familiar to the
participant. This research is needed before firm recomendations can be made ragarding the
most effective way to increase training perforrnacne through framing. In the meantime, the
large body of research evidence suggests that one needs to build up an individual's level of
confidence while at the same time providing a certain level of challenge. It may be best to
challenge pe0ple through more explicit ways than by telling them they need to go to
remedial trainin g. The long term consequences of such repeated negative feedback may be
too great.

Needs Assessment. The results of this Study suggest that individuals can have
preferences regarding training program assignments based on self-perceptions of
performance which in turn affect training effecriveness. The needs assessment phase of a
training intervention can set up expectations regarding the type of program that is likely to
be developed. Noe (1986) has suggesred that individual reactions to skill assessment are
likely to affect their pre-training characteristics. Care must be taken to ensure that
inaccurate expectations regarding training program design and assignments are not being
formed.

W. The results of the present study are consistent with past
findings showing the importance of motivation to learn on training effectiveness (Mathieu,
Tannenbaum, & Salas, 1992; Noe, 1986; Ryman & Biersner, 1975). Individuals
motivated to learn tend to master more of the training content and apply this learning to task
performance. However, research has also shown that interventions designed to increase
motivation may actually decrease learning (Kanfer & Ackerman, 1989). The results of this

study suggest that, rather than focusing motivational interventions on individual trainees,

114

interventions should focus on contextual factors which tend to lower motivation to learn.
Giving individuals the chance to choose their own training programs and ensuring that
these preferences are honored could go a longway towards increasing trainee motivation
(Baldwin, Magjuka, & Loher, 1991; Hicks & Klimoski, 1987; Martocchio & Webster,
1992).

Furthermore, research could examine other factors which can inhibit or enhance
motivation to learn. For example, recent research suggests that perceptions of situational
constraints can lower motivation to learn (Mathieu, Tannenbaum, & Salas, 1992). Other
factors such as supervisory attitudes toward training or a "climate for training" are likely to
affect motivation to learn. Given the recent move towards continuous improvement as a
general business strategy, research on the effects on these variables on motivation to learn
is sorely needed.

Fairness Perceptions. This study represents the first attempt at determining the role
of fairness perceptions on training effectiveness. To the extent that individuals do not have
free access to unlimited types of training programs, perceptions regarding the process used
to determine training assignments, as well as actual training assignments themselves, are
likely to represent a potential threat to a successful training intervention. Research is
needed in order to determine methods of distributing training which are perceived as fair by
individuals. The results of this study clearly show that training assignments which are
consistent with an individual's perceptions of training needs lead to higher fairness
perceptions.

Future research is also needed to determine the long-term effects of poor fairness
perceptions (cf. Gilliland, in press). For example, research on organizational justice has
shown that fairness perceptions are related to organizational commitment (Folger &
Konovsky, 1989; Fryxell & Gordon, 1989). This lack of commitment is likely to lead to

fewer attempts at transferring trained material back on the job (Ford et al., 1992). In

115

addition to designing good training programs, research must determine fair ways of

determining who is to receive this training program.

The present study has several potential limitations which must be taken into account
when interpreting the results and designing future studies. First, the use of college
students may limit the generalizability of the effects. Future studies should attempt to get a
diverse group of participants who are at different stages of development. It is possible that
older workers, who know their own skills well, may react differently when given feedback
in the form of training assignments.

Secondly, future studies should study framing effects in training programs
designed to teach more "generic" skills such as literacy or math courses for adults. In
addition, studies could examine more technical courses such as computer training. The
task used in this study was chosen in order to limit any effeCts of previous task experience.
Having tested the hypothesized relationships, studies could now determine how experience
affects the observed relationships. Perhaps the effecr may be stronger for tasks for which
an individual has had a lot of experience. It is more likely that under high experience
situations, individuals have a pretty strong idea about their level of skill. Therefore,
fairness perceptions may be very sensitive to framing in this situation.

Thirdly, the training course investigated may have been shorter than those used in
organizations. Courses lasting a half day or a few days may be more common (Goldstein,
1993). For longer courses, sustained modvation may be more critical. Therefore, while
the length of the course used in this study may have been short, motivational effects were
found. In longer courses, it may be expected that motivation to learn is a more powerful
predictor of training outcomes. Future studies should examine the relationship between

course length, motivation to learn, and training outcomes.

116

Finally, most of the measures in this study were collected from the same source.
The potential for method variance is always present. However, the lack of extremely high
intercorrelations in the presence of high reliabilities suggests that this may have not been a
problem. Also the presence of siginificant interactions also suggests that method variance
was not a problem. Future studies could employ coworkers or supervisors for collecting

measures such as motivation to learn.

C_oac_lus_ion

Despite the limitations cited above, this study makes a number of contributions to
the training literature. First, it suggests that individuals are not passive recipients of
training assignments. The way in which training programs are framed can enhance or
diminish the effectiveness of training interventions. Second, individuals react differently to
training assignments. Self-perceptions of training needs as well as attributions can
determine the extent to which training assignments affect pre—training characteristics.
Third, this study highlights the importance of motivation in training effectiveness. Training
program design muSt go beyond delivery issues if they are to maximize training outcomes.
Finally, this Study highlights the importance of a sysrems approach to training. Training is
a value-laden activity which is embedded within a larger organizational reality. Training
researchers and practitioners must consider a number of broader issues when designing

training interventions.

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APPENDICES

130
APPENDIX A

Training Materials

AIR DEFENSE TASK

REMEDIAL TRAINING MANUAL

131

INTRODUCTION

Past research on this task has shown that individuals who are classified as below average
on the Air Defense Task might benefit from a remedial training course covering a variety of
aspects relating to the task. This training manual is a result of this research. The manual
consists of four major parts. The first part is a review of the general overview of the task
as well as the instructions for determining the overall threat level of a given target. The
second section describes in more detail how to determine the point at which a target
changes threat levels based on their value on a given attribute (e. g. speed, corridor status,
etc.). The third section describes some helpful strategies for measuring the nine target
attributes which should aid you in determining the threat level and the corresponding
response. Finally, some keyboard shortcuts or "hot keys" are described which should
decrease the time that it takes you to measure the attributes and make your decision.

The experimenter will guide you through this training manual. You will be allowed ample
time to review the material before going on to each new seCtion. If you finish a section
before the allotted time you may consider going back and reviewing the material again. Do
not start a new section before being instructed to do so by the

experimenter. As you may already have noriced, there is a lot of material to memorize.
Try to learn as much as you can. If you pay close attention to the material presented in this
manual you should be able to perform the Air Defense Task very effectively.

132

SECTION 1

Review of General Instructions

133

AIR DEFENSE TASK

INTRODUCTION

The year is 1994 and you are part of a US. naval carrier group's command and control
team stationed in the middle east. A regional conflict between two nations in this area has
recently broken out. Your mission is to protect sea- going commercial traffic in the area
from accidental and intentional attacks. As history indicates, this is a highly sensitive task.
For example, in 1987, an Iraqi jet accidentally fired two Exocet missiles into the Frigate
USS Stark, killing 37 American servicemen and crippling the vessel. One year later, the
USS Cruiser Vincennes accidentally sh0t down an Iranian passenger plane killing 290
innocent civilians. Any repeat of mistakes of this kind will probably lead to a withdrawal
of American forces from the area. Such a withdrawal would have disaStrous economic and
political ramifications that would spread well beyond this region.

fJfHE TASK FORCE

A naval carrier battle team is an awesome array of ships and support units. It consists of a
concentric ring of missile firing warships which pr0tect the aircraft carrier at its center. The
aircraft carrier, in return, provides an overall umbrella of air protection for the entire task
force. The carrier's 90 planes can unleash air Strikes against targets at land, sea, and even
underwater. A carrier group can dominate up to 196,000 square miles of ocean. Your
carrier group consists of the Carrier itself, a Ticonderoga class Aegis Cruiser, AWACs
reconnaissance planes and a land based Coastal Air Defense (CAD) unit. Although the
Carrier itself is equiped with some air patrol capacities, the Cruisers, AWACs and CAD
units provide the bulk of air traffic patrol. Taken together, the air patrol groups of the
Carrier, the Cruiser, the AWACs and the CAD unit make up the command and control
team.

TEAM MISSION

The team of which you are a part, will role play the Commanding Officers of various units
in the carrier group. Your mission is to monitor the air space surrounding the carrier
group, making sure that neutral ships are not attacked. In performing this role, you must
make certain that you do not allow loss of life resulting from accidental or intentional
attacks on ships in the task force. At the same time, it is also of paramount importance that
you do not inadvertently shoot down friendly military aircraft or civilian aircraft. Many
passenger flights move in and out of the region, and friendly military aircraft from nations
not involved in the conflict also patrol the area. The navy can ill-afford any mistakes of
either the Stark or Vincennes variety.

DECISIONS

134

Your task is to decide what response the carrier group should make toward incoming
aircraft. Aircraft that are being tracked on radar are called tagets. You base your decisions
on data you collect by measuring charaCteriStics of the air targets. These measures are
obtained from SOphisticated radar equipment. You must make a critical choice regarding
each target. There are five potential responses, IGNORE, MONITOR, WARN, READY,
and DEFEND. These are described below:

IGNORE:

MONITOR:

WARN:

READY:

DEFEND:

This means that the carrier group should devote no further attention to the
target, but instead focus on other possible targets in the area. The group
should never ignore a target that might possibly attack. This would most
assuredly lead to loss of lives on the ship attacked.

Here the carrier group should continuously track the target on radar. A
carrier group can monitor only a few targets, thus monitoring diminishes the
group's overall patrol capacity.

In this case the carrier group sends a message to the target indentifying the
group and alerting the target to Steer clear. Warning targets that should be
ignored detracts from the salience of legitimate warnings. Warning targets
that intend to attack is also bad, since the warning makes it easier for the
attacker to locate the ship.

This means to Steer the ship into a defensive posture and to set defensive
weapons on automatic. A Ship in a readied position is rarely vulnerable to
attack. This stance Should not be taken to non-threatening targets since
weapons set to automatic can fire mistakenly at innocent targets that fly too
close to the carrier group. A Ship in this position cannot readily use
offensive weapons on th target.

This is "weapons away" and means to attack the target with Tomahawk
cruise missiles. A defend decision cannot be aborted once initiated. Defend
is an appropriate reSponse when you feel an attack is imminent.

135

CJ-JARAC I ERISTICS OF TAROETS

The incoming air targets can be measured on nine. attributes. These are listed below along
w1th the ranges of possible values on the attributes:

(1) SPEED:

(2) ALTITUDE:

(3) SIZE:

(4) ANGLE:

(5) IFF:

(6) DIRECTION:

(7) CORRIDOR
STATUS:

w)RADAR
TYPE:

(9) RANGE:

100 to 800 miles per hour (mph)

5,000 to 35,000 feet

Size of the target ranging from 10 to 65 meters
-15 (rapid descent) to +15 degrees (rapid ascent)

"Identification Friend or Foe". This is a radio signal that identifies
whether an aircraft is civilian, para-military, or military, ranging
from .2 Mhz (an airliner) to 1.8 Mhz (a fighter).

from +30 degrees (passing far to the east or west of the carrier) to 0
degrees (coming straight to the carrier).

a corridor is a lane Open to commercial air traffic. Status is
expressed in terms of miles from the center of the corridor, ranging
from 0 miles (in the middle of it) to 30 miles (way out of it)

the kind of radar possessed by the aircraft ranging from Class 1
(weather radar only to Class 9 (weapons radar)

distance of the aircraft from the Carrier ranging anywhere from 0 to
200 miles

136

DETERMINING THE LEYEL OF THREAT

In general, the degree to which an incoming target is threatening depends on its standing on
these nine attributes. There are five Simple rules to remember in determining the danger
associated with any target:

(a)

(b)

(C)

(d)

(6)

all else being equal, in terms of IFF, military: targets are more threatening than ciVilian
targets (see attribute #5)

SPEED and DIRECTION go together, so that fast targets coming straight in are most
threatening (see #1 and #6 above). Speed alone and direction alone mean nothing.
There is nOthing to fear if fast targets are not headed toward the group. There is
n0thing to fear from Slow objects headed directly for the group.

ANGLE and RANGE go together, so that descending targets that are close are
especially threatening (see #4 and #9 above). Angle alone and range alone mean
nothing. Descending targets that are far away, or close targets that are on the way up
are not threatening.

ALTITUDE and CORRIDOR STATUS go together, so that low flying targets that are
way outside the corridor are especially threatening (see #2 and #7 above). Altitude
alone and corridor status alone mean nothing. There is nothing to fear from high
flying targets well outside the corridor or low flying targets in the middle of the
corridor.

SIZE and RADAR go together, so that small objects with weapons radar are
especially threatening (see #3 and #8 above). There is nothing to fear from small
targets with weather radar only or from large targets with weapons radar.

RANOE OFALJRIBOJES

The following chart will help you determine the level of threat associated with the different
values of all nine attributes.

Degree of Threat

 

Non-Threatenin g Somewhat Threatening Very Threamtg
Speed 100-275 mph 325-500 mph 600-800 mph
Altitude 35,000-27,000 ft 23,000-17,000 ft 13,000-5,000 ft
Size 65-43 m 37-23 m 17-10 m
Angle +15 to +8 dgs +3 to -3 dgs -8 to -15 dgs
[FF .2 to .6 Mhz .9 to 1.1 Mhz 1.4 to 1.8 Mhz
Direction 30 to 22 dgs 18 to 12 dgs 8 to 0 dgs
Corridor St. 0 to 8 mi 12 to 18 mi 22 to 30 mi
Radar Type Class 1 & 2 Class 5 Class 8 & 9

Range 200 to 110 mi 90 to 60 mi 40 to 1 mi

 

137

HOW RULES @MBINE TO DETERMINE JUDOEMENTS

The five rules combine to determine overall threat represented by the target. So for
example, if a team detected a (a) military aircraft that is (b) flying in straight and fast, (c)
was close and descending, (d) was flying low and way outside the corridor, and (e) was
small and had weapons radar; the Ship is being attacked and Should DEFEND.

If the team detected (a) a civilian aircraft, that is (b) passing slow at an angle, (0) was far
away and ascending, (d) was flying high and in the middle of the corridor and (e) was large
and had weather radar; this is a passanger plane that should be IGNORED.

Intermediate responses like MONITOR, WARN, or READY are to be used when the target
is threatening according to some of the rules but not all. For example, a military aircraft
that is close and descending (see rule c), small and with weapons radar (see rule e), but is
traveling slowly at an angle to the group (see rule b), and is high and in the middle of the
corridor (see rule (I) might need to be WARNED. It Should not be IGNORED, but neither
Should it be Sh0t down.

DECISION UTCOMES

Once you make your decision to either IGNORE, MONITOR, WARN, READY, or
DEFEND, you will be provided with feedback regarding your performance. Five
outcomes can result from the defensive posrure that you choose. These are:

HIT: A hit means that your decision was exactly correct. So for example, the
target Should have been "warned" and that was exactly what the team
decided. A hit is worth 2 points to your overall score. The color bars at
the top and bottom of the screen will be green when this occurs.

NEAR MISS: A near miss means that you were off by one place in terms of your
agressiveness level. For example, if your decision was "warn" when it
Should have been "monitor" this would be a near miss (a little too
aggressive). It would also be a near miss if your decision was "warn"
when it Should have been "ready" (a little too passive). A near miss is a
pretty good outcome. A near miss is worth 1 point. The color bars at
the top and bottom of the screen will be aquamarine when this occurs.

MISS: A miss means that your decision was off by two places. This is worth 0
points. The color bars will be purple when this occurs.

INCIDENT: An incident means that your decision was off by three places. An
incident means that you just narrowly avoided disaster (e. g. being hit
yourself or mistakenly shooting down a friendly target). This outcome
results in a loss of 1 point. The color bars will be red when this occurs.

DISASTER: A disaster means that your decision was off by four places. This
outcome results in a loss of 2 points. The color bars will be black in
this case.

138

SECTION 2

Threat Level Points of Inflection

139

Determining Threat Level

AS you learned in seetion 1, the correct response is determined by the level of threat
associated with the nine attributes of the target. However, you also learned that the
relationship between the attributes and threat level is somewhat complicated. Some
attributes combine so that the level of threat is determined by the joint value of two
attributes. These rules are liSted below:

(a) all else being equal, in terms of IFF, milita_ry tggets are more threatening than civilian
targets (see attribute #5)

(b) SPEED and DIRECTION go together, so that fast targets coming Straight in are most
threatening (see #1 and #6 above). Speed alone and direction alone mean nothing.
There is n0thing to fear if fast targets are n0t headed toward the group. There is
nothing to fear from slow objects headed directly for the group.

(c) ANGLE and RANGE go together, so that descending targets that are close are
especially threatening (see #4 and #9 above). Angle alone and range alone mean
n0thing. Descending targets that are far away, or close targets that are on the way up
are not threatening.

(d) ALTITUDE and CORRIDOR STATUS go together, so that low flying targets that are
way outside the corridor are especially threatening (see #2 and #7 above). Altitude
alone and corridor status alone mean nothing. There is nothing to fear from high
flying targets well outside the corridor or low flying targets in the middle of the
corridor.

(e) SIZE and RADAR go together, so that small objects with weapons radar are
especially threatening (see #3 and #8 above). There is nothing to fear from small
targets with weather radar only or from large targets with weapons radar.

As you can see from the rules above, there are four "combination rules" or rules that relate
threat levels based on the values of two attributes. The key to determining if any particular
combination is threatening is to determine whether any one of the components is
threatening. This is the function of the table listed below.

Degree of Threat

n-Th in e ha Thr nin nin
Speed 100-275 mph 325-500 mph 600-800 mph
Altitude 35,000-27,000 ft 23,000-17,000 ft 13,000-5,000 ft
Size 65-43 m 8 37-23 m 17-10 m
Angle +15 to +8 dgs +3 to -3 dgs ' -8 to —15 dgs
[FF .2 to .6 Mhz .9 to 1.1 Mhz 1.4 to 1.8 Mhz
Direction 30 to 22 dgs 18 to 12 dgs 8 to 0 dgs
Corridor St. 0 to 8 mi 12 to 18 mi 22 to 30 mi
Radar Type Class 1 & 2 Class 5 Class 8 & 9

Range 200 to 110 mi 90to60mi 40 to 1 mi

 

140

You may have noticed from the table above that the point at which an attribute changes
from non-threatening to somewhat threatening and from somewhat threatening to very
threatening is ambiguous. For example, non-threatening SIZE ranges from 65-43 m,
however, somewhat threatening SIZE ranges from 37-23 m. You can see that the change
from one threat level to the other occurs somewhere between 43m and 37m. This is called
the POINT OF INFLECTION. For each of the nine attributes there are two points of
inflection. These correspond to the changes from non-threatening to somewhat threatening
and from somewhat threatening to very threatening.

An efficient strategy for determining threat level is to know the two points of inflection for
each of the nine attributes. This way if an attribute that is part of a combination is below
the point of inflection for non-threatening, you know that the combination is non-
threatening. This is a very efficient strategy which will help you perform better on the Air
Defense Task. The table below presents the points of inflection for all nine attributes.
Your task for this section of the training course is to memorize these values.

From the combination rules, you also learned that if any one of the two attributes is non-
threatening, the entire combination is non-threatening. Threfore, if you can only memorize
a few of these numbers, you Should try to memorize the left hand column. This is the point
at which an attribute goes from non-threatening to somewhat threatening.

POINTS OF INFLECTION

 

Non to Somewhat Somewhat to Very
Threatening Threatening

SPEED 300 mph 550 mph
ALTITUDE 25,000 ft 15,000 ft
SIZE 40 m 19 m
ANGLE + 6 dgs - 6 dgs

IFF .75 Mhz 1.25 Mhz
DIRECTION 20 dgs 10 dgs
CORRIDOR STATUS 10 mi 20 mi
RADAR TYPE Class 3 Class 7

RANGE 100 mi SOmi

 

141

SECTION 3

Measurement Strategies

142

When a target appears, your job is to measure the nine attributes and determine the
appropriate response depending on the level of threat of the target. Through various
keystrokes which will be explained in detail in the next section, you will Open a
MEASURE box. In this box you will see all nine attributes listed. Once you measure all
the attributes you can review the values with the SUMMARY function found in the
MEASURE box. The values will be listed in the order in which they were measured.
Given this fact, an important Strategy to employ is to measure the attributes in such a way
that they facilitate your judgments regarding the interaCIionS between attributes. For
example, if SIZE and RADAR go together, measuring SIZE then RADAR puts these two
values next to each other in the SUMMARY box thus facilitating your decision making
process. Below is a suggested order of measurement which Should make your task easier.
The attributes which go together are listed together. The important part of this list is which
attributes should be next to each other on the REVIEW box and not necessarily the entire
order. For example, as you can see from the list SIZE and RADAR go together.
However, these two attributes can be measured at any time as long as they are measured
together. Remember that IFF goes by itself.

RECOMMENDED ORDER OF MEASUREMENT
IFF

SPEED
DIRECTION

ANGLE
RAN GE

ALTITUDE
CORRIDOR STATUS

SIZE
RADAR

If you can't memorize all the attributes in this order, at least try to remember the letters in
each word that are outlined. These correspond to the "HOT KEYS" which you will learn
how to operate in the next section.

143

SECTION 4

"Hot Key" Operation

144

There are a number of ways to Operate the MEASURE, SUMMARY, and JUDGEMENT
commands. AS stated in the last section, the MEASURE box allows you to measure the
values of the nine attributes of the target. The SUMMARY command allows you to review
all the information that you have measured about the target. The JUDGMENT box allows
you to record the appropriate judgment for the particular target.

MEA RE

When a new target appears you must first measure the target on the nine attributes in order
to detemtine the level of threat. This can be accomplished by pressing the ALT key until
the menu-bar is highlighted then pressing either the DOWN -ARROW or the ENTER key.
This Opens up the MEASURE box. In the box, all nine attributes are listed.

To measure an attribute you can either (1) ARROW DOWN to highlight the appropriate
attribute (e.g. Speed) and press the ENTER key or (2) press the RED highlighted letter for
the attribute that you want to measure. This highlighted letter is known as the HOT KEY.
Most Of the time the HOT KEY is the first letter of the attribute (e.g. S for Speed) but not
always.

When you measure an attribute the value will be Shown in a box for a few seconds. You
don't have to memorize this value. The measured values are Stored In the SUMMARY box
which you can access at any time.

SUMMARY

There are two ways to get to the SUMMARY box. The first is by opening the MEASURE
box (ALT key then ENTER) and going down with the ARROW KEY to SUMMARY. The
second is much quicker and more efficient. The summary key comes up on the screen
every time you press the F2 key.

You can access the SUMMARY box as many times as you need. Only the attributes that
you have measured will appear in this box. They will appear in the order that you
measured them. This is why it is important to measure the attributes so that the attributes
that go together appear next to each Other in this box. Once you have measured all the
attributes and determined the level of threat you must make a JUDGMENT.

W

T 0 make a judgment (e.g. Defend) you must open the JUDGMENT box. To accomplish
this press the ALT key to highlight the MENU BAR. You can then either ARROW
RIGHT to Judgment and press ENTER or Simply press J after highlighting the MENU
BAR.

At this point you must render a judgment. You can either ARROW DOWN to the
appropriate judgment or press the red HOT KEY for your judgment.

Once you have recorded your judgment you will see a feedback screen and a new trial will
Start.

TR TE Y
Given that your performance will be based on both the accuracy of your judgments as well

as the number of targets that you encounter it is to your advantage to memorize and utilize
the keyboard shortcuts and HOT KEYS described above.

145
APPENDIX B

Locus of Control 1 Scale

Using the scale below indicate your level of agreement with the following items. Do not
answer how you think you are expeCIed to answer. Answer in an honeSt fashion.

OO\IO\LIIJ>UJN

10.
11.

1 = Strongly Disagree

2 = Disagree

3 = Neither agree nor disagree
4 = Agree

5 = Strongly Agree

Becoming a success is a matter of hard work; luck has little or nothing to do
with it.
In the long run, peOple get the respect they deserve in this world.

When I make plans, I am almost certain I can make them work.

What happens to me is my own doing.

Without the right breaks one cannot be a good leader.
Many of the unhappy things in people's lives are partly dire to bad luck.
In my case, getting what I want has little to do with luck.

Who gets promoted Often depends on who was lucky enough to be in the
right place first.

Most people don't realize the extent to which their lives are controlled by
accidental happenings.

Many times I feel I have little influence over the things that happen to me.

In the long run, the bad things that happen to us are balanced by the good
ones.

146
APPENDIX C

ROtter's (1966) Locus of Control Scale

PART I: For each of the following pairs of Statements choose the one which mOSt closely
reflects your feelings and attitudes. There are no right answers. Answer in an honest
fashion and nm in the way in which you think you are expecred to answer. Mark only
ONE statement per pair.

If a.
b.
2. a.
b
3. a
b
4. a
b
5. a
b
6. a.
b
7. a.
b
8.f. a.
b
9. a
b
10. a.
b

Children get into trouble because their parents punish them too much.
The trouble with most children nowadays is that their parents are too easy with
them.

Many of the unhappy things in people's lives are partly due to bad luck.

. People's misfortunes result from the mistakes they make.

. One Of the major reasons why we have wars is because people don't take enough

intereSt in politics .

. There will always be wars, no matter how hard people try to prevent them.

. In the long run people get the respect they deserve in this world.
. Unfortunately, an individual's worth often passes unrecognized no matter how

hard he or she tries.

. The idea that teachers are unfair to Students is nonsense.
. Most students don't realize the extent to which their grades are influenced by

accidental happenings.

Without the right breaks one cannot be an effective leader.

. Capable people who fail to become leaders have nor taken advantage of their

opportunities.

No matter how hard you try, some people just don't like you.

. People who can't get Others to like them don't understand how to get along with

others.

Heredity plays the major role in determining one's personality.

. It is one's experience in life which determine what one is like.

. I have often found what is going to happen will happen.
. Trusting to fate has never turned out as well for me as making a decision to take a

definite course Of aCtion.

In the case Of the well prepared Student there is rarely if ever such a thing as an
unfair test. '

. Many times exam questions tend to be SO unrelated to course work that Studying

is really useless.

ll.

12.

13.

l4.f.

15.

16.

17.

18.

19.f.

20.

21.

22.

23.

24.f .

0'.”

‘3'!”

0‘.”

147

. Becoming a success is a matter of hard work, luck has little or nothing to do with

It.

. Getting a god job depends mainly on being in the right place at the right time.

The average citizen can have an influence in government decisions.

. This world is run by the few people in power, and there is not much the little guy

can do about it.

When I make plans, I am almost certain that I can make them work.

. It is not always wise to plan too far ahead because many things turn out to be a

matter Of good or bad fortune anyhow.

There are certain peOple who are just no good.

. There is some good in everybody.

In my case, getting what I want has little or nothing to do with luck.

. Many times we might just as well decide what to do by flipping a coin.

. Who gets to be the boss Often depends on who was lucky enough to be in the

right place first.

. Getting people to do the right thing depends upon ability, luck has little or nothing

to do with it.

. As far as world affairs are concerned, mOSt of us are the victims of forces we can

neither understand, nor control.

. By taking an active part in political and social affairs the people can control world

CVCIIIS.

Most peOple don't realize the extent to which their lives are controlled by
accidental happenings.

. There is really no such thing as "luck."

One Should always be willing to admit mistakes.

. It is usually best to cover up one's mistakes.

It is hard to know whether or not a person really likes you.

. How many friends you have depends on how nice a person you are.

In the long run the bad things that happen to us are balanced out by the good
ones.
Most misfortunes are the result of lack Of ablity, ignorance, laziness, or all three.

With enough effort we can wipe out political corruption.

. It is difficult for people to have much control over the things politicians do in

office.

Sometimes I can't understand how teachers arrive at the grades they give.

. There is a direct connection between how hard I study and the grades I get.

A good leader expects people to decide for themselves what they Should do.
A good leader makes it clear to everybody what their jobs are.

25.

26.

27.f.

28.

29.

‘7'!” 0‘5”

0‘!”

5".”

0‘9”

148

Many times I feel that I have little influence over the things that happen to me.

. It is impossible for me to believe that chance or luck plays an important role in my

life.

PeOple are lonely because they don't try to be friendly.

. There's not much use in trying too hard to please people, if they like you, they

like you.

There is too much emphasis on athletics in high school.

. Team Sports are an excellent way to build character.

What happens to me is my own doing.

. Sometimes I feel that I don't have enough control over the direction my life is

taking.

Most of the time I can't understand why politicians behave the way they do.

. In the long run the people are responsible for bad government on a national as

well as on a local level.

f = filler item

149
APPENDIX D

Self-Esteem Scale

Using the scale below indicate your level of agreement with the following items. Do not
answer how you think you are expected to answer. Answer in an honest fashion.

1 = Strongly Disagree

2 = Disagree

3 = Neither agree nor disagree
4 = Agree

5 = Strongly Agree

_ 1. I feel I am a person of worth, at least on an equal basis with Others.
__ 2. I feel that I have a number of good qualities.

_ 3. All in all, I am inclined to feel that I am a failure.

_ 4. I am able to do things as well as most Other people.

I feel I do nor have much to be proud of
I take a positive attitude toward myself.
On the whole, I am satisfied with myself.

I wish I could have more respect for myself.

\DOO\lO\lJt

I certainly feel useless at times.

10. At times I think I am no good at all.

150
APPENDIX E

Pre-Training Knowledge Measure

PART I: Mark the correct answer for each of the questions below. Make sure to mark
your answers on this form as well as the computer form.

1) 1.5 Mhz represents which of the following?

a) A non-threatening target.

b) A somewhat threatening target.

c) A very threatening target.

(1) A target on the border between two threat levels.
e) A target that is out of the possible range of values.

2) +20 degrees of angle represents which of the following?

a) A non-threatening target.

b) A somewhat threatening target.

c) A very threatening target.

d) A target on the border between two threat levels.
e) A target that is out of the possible range of values.

3) 5 miles outside the corridor represents which of the following?

a) A non-threatening target.

b) A somewhat threatening target.

c) A very threatening target.

d) A target on the border bemeen two threat levels.
e) A target that is out of the possible range of values.

4) 35 degrees of direction represents which of the following?

a) A non-threatening target.

b) A somewhat threatening target.

c) A very threatening target.

d) A target on the border between two threat levels.
e) A target that is out of the possible range of values.

5)

6)

7)

8)

9)

151

Class 10 radar type represents which of the following?

a) A non‘threatening target.

b) A somewhat threatening target.

c) A very threatening target.

d) A target on the border between two threat levels.
e) A target that is out of the possible range of values.

400 miles per hour represents which of the following?

a) A non-threatening target.

b) A somewhat threatening target.

c) A very threatening target.

d) A target on the border between two threat levels.
e) A target that is out of the possible range of values.

30,000 foot altitude represents which of the following?

a) A non-threatening target.

b) A somewhat threatening target.

c) A very threatening target.

(I) A target on the border between two threat levels.
e) A target that is out of the possible range of values.

20 meters represents which of the following?

a) A non-threatening target.

b) A somewhat threatening target.

c) A very threatening target.

(I) A target on the border between two threat levels.
e) A target that is out of the possible range of values.

25 miles outside the corridor represents which of the following?

a) A non-threatening target.

b) A somewhat threatening target.

c) A very threatening target.

(I) A target on the border between two threat levels.
e) A target that is out of the possible range of values.

10) 850 miles per hour represents which of the following?

a) A non-threatening target.

b) A somewhat threatening target.

c) A very threatening target.

d) A target on the border between two threat levels.
e) A target that is out of the possible range of values.

11)

12)

13)

14)

15)

16)

152

Class 1 radar represents which of the following?

a) A non-threatening target.

b) A somewhat threatening target.

c) A very threatening target.

(I) A target on the border between two threat levels.
e) A target that is out of the possible range of values.

-12 degrees of angle represents which of the following?

a) A non-threatening target.

b) A somewhat threatening target.

c) A very threatening target.

(I) A target on the border between two threat levels.
e) A target that is out of the possible range of values.

80 miles for range represents which of the following?

a) A non-threatening target.

b) A somewhat threatening target.

c) A very threatening target.

(I) A target on the border between two threat levels.
e) A target that is out of the possible range of values.

Which of the following combinations represents a more threatening target?

a) slow and land radar targets

b) high flying and fast targets

c) descending and inside traffic corridor targets

(1) outside the traffic corridor and high flying targets
e) close and descending targets

Which of the following combinations represents a more threatening target?

a) slow targets with weather radar

b) high flying and fat targets

c) descending and inside traffic corridor targets
(1) small targets with weapons radar

e) close and ascending targets

Which of the following combinations represents a more threatening target?

a) slow and land radar targets

b) high flying and fast targets

c) descending and inside traffic corridor targets
(1) fast targets headed toward the group

e) close and ascending targets

17)

18)

19)

20)

Which of the following combinations represents a more threatening target?

a) slow and land radar targets

b) high flying and fast targets

c) descending and inside traffic corridor targets

d) outside the traffic corridor and low flying targets
e) close and ascending targets

All else equal, which of the following is/are characteristic(s) of a threatening target?

a) Low flying targets.

b) Military targets.

0) Targets with weather radar

d) 2 of the above are charaCIeristics of a threatening target.
e) 3 of the above are characteristics of a threatening target.

All else equal, which of the following is/are characteristic(s) of a threatening target?

a) Targets that are high flying.

b) Targets with weapons radar.

c) Targets that are fast and inside the traffic corridor.
(1) Targets that are descending and close.

e) Targets that are large and descending.

All else equal, which of the following is/are charaCIeristic(s) of a threatening target?

a) Targets that are large.

b) Targets with weather radar.

c) Targets that are fast and coming straight in.

d) Targets that are small and inside the traffic corridor.
e) Targets that are ascending and close.

154
APPENDIX F

Pre-Training Performance Measure

PART I : For the following targets, make the appropriate decision regarding the defensive
posture which you should take. Mark your answer on both forms.

_1_:_

SPEED: 172 miles per hour (mph)
ALTITUDE: 10,248 feet
SIZE: 10 meters
ANGLE: -14 degrees
IFF: 1.3 Mhz
DIRECTION: 4 degrees
CORRIDOR STATUS: 29 miles
RADAR TYPE: Class 2
RANGE: 31 miles

YQUR DECISION (circle);
(1) IGNORE

(2) MONITOR
(3) WARN

(4) READY
(5) DEFEND

155

 

SPEED: 321 miles per hour (mph)
ALTITUDE: 26,605 feet
SIZE: 41 meters
ANGLE: 9 degrees
IFF: .7 Mhz
DIRECTION: 21 degrees
CORRIDOR STATUS: 19 miles
RADAR TYPE: Class 4
RANGE: 108 miles
YOUR DECISION (circle):
(1) IGNORE
(2) MONITOR
(3) WARN
(4) READY

(5) DEFEND

156

;

SPEED:

ALTITUDE:

SIZE:

ANGLE:

IFF:

DIRECTION:
CORRIDOR STATUS:
RADAR TYPE:
RANGE:

291 miles per hour (mph)
14,321 feet

21 meters

-6 degrees

.7 Mhz

9 degrees

21 miles

Class 6

101 miles

YOUR DECISION (circle):

(1) IGNORE
(2) MONITOR
(3) WARN

(4) READY
(5) DEFEND

157

1.4?

SPEED:

ALTITUDE:

SIZE:

ANGLE:

IFF:

DIRECTION:
CORRIDOR STATUS:
RADAR TYPE:
RANGE:

591 miles per hour (mph)
14,016 feet

19 meters

-6 degrees

1.3 Mhz

9 degrees

11 miles

Class 7

43 miles

YOUR DECISION (Qgg' le):

(1) IGNORE
(2) MONITOR
(3) WARN

(4) READY

5) DEFEND

IH'

SPEED:

ALTITUDE:

SIZE:

ANGLE:

IFF:

DIRECTION:
CORRIDOR STATUS:
RADAR TYPE:
RANGE:

172 miles per hour (mph)
10,248 feet

14 meters

-14 degrees

1.6 Mhz

4 degrees

23 miles

Class 2

31 miles

YOUR DECISION (circle):

(1) IGNORE
(2) MONITOR
(3) WARN

(4) READY
(5) DEFEND

159

_6_:_

SPEED:

ALTITUDE:

SIZE:

ANGLE:

IFF:

DIRECTION:
CORRIDOR STATUS:
RADAR TYPE:
RANGE:

281 miles per hour (mph)
14,018 feet

21 meters

-7 degrees

.7 Mhz

10 degrees

21 miles

Class 6

106 miles

YOUR DECISION (circle):

(1) IGNORE
(2) MONITOR
(3) WARN

(4) READY
(5) DEFEND

160

lhl

SPEED:

ALTITUDE:

SIZE:

ANGLE:

IFF:

DIRECTION:
CORRIDOR STATUS:
RADAR TYPE:
RANGE:

595 miles per hour (mph)
14,937 feet

19 meters

-4 degrees

1.3 Mhz

9 degrees

21 miles

Class 4

49 miles

YOUR DECISION (circle):

(1) IGNORE
(2) MONITOR
(3) WARN

(4) READY
(5) DEFEND

161

 

&
SPEED: 597 miles per hour (mph)
ALTITUDE: 16,115 feet
SIZE: . 18 meters
ANGLE: -6 degrees
IFF: 1.3 Mhz
DIRECTION: 19 degrees
CORRIDOR STATUS: 21 miles
RADAR TYPE: Class 4
RANGE: 109 miles
YOUR DECISION (circle):
(1) IGNORE
(2) MONITOR
(3) WARN
(4) READY

(5) DEFEND

162

I}?

SPEED:

ALTITUDE:

SIZE:

ANGLE:

IFF:

DIRECTION:
CORRIDOR STATUS:
RADAR TYPE:
RANGE:

345 miles per hour (mph)
6,525 feet

14 meters

-10 degrees

1.7 Mhz

7 degrees

0 miles

Class 1

75 miles

YQUR DECISION (circle):

(1) IGNORE
(2) MONITOR
(3) WARN

(4) READY
(5) DEFEND

163

SPEED:

ALTITUDE:

SIZE:

ANGLE:

IFF:

DIRECTION:
CORRIDOR STATUS:
RADAR TYPE:
RANGE:

299 miles per hour (mph)
16,768 feet

18 meters

-6 degrees

1.3 Mhz

9 degrees

23 miles

Class 2

31 miles

YOUR DECISION (circle):

(I) IGNORE
(2) MONITOR
(3) WARN

(4) READY
(5) DEFEND

164
APPENDIX G

Perceived Performance Scale

Using the scale below indicate your level Of agreement with the following items. These
items measure how well YOU THINK you performed on the Training Assignment Test.
DO not answer how you think you are expeCted to answer. Answer in an honest fashion.

I = Strongly Disagree

2 = Disagree

3 = Neither agree nor disagree
4 = Agree

5 = Strongly Agree

_ l. I performed very well on this test.

_ 2. 1 know I did better than most people on this test.
__ 3. I did pretty bad on this test.

_ 4. I performed terribly during this test.

5. My performance on this test was excellent.

165
APPENDIX H

Expected Training Assignment

Based on my performance on the Training Assignment Test, 1 am certain I should be
assigned to (check the appropriate training):

Advanced Training: I performed very well and deserve advanced training.

Remedial Training: 1 performed very poorly and need remedial training.

166
APPENDIX I

Attributions Scale

Using the scale below indicate your level of agreement with the following rtems. These
items are intended to measure what you feel were the main causes for your performance

during the W. DO not answer how you think you are expected to
answer. Answer 1n an honest fashion.

1 = Strongly Disagree

 

2 = Disagree
3 = Neither agree nor disagree
4 = Agree
5 = Strongly Agree
Luck Attributions
1. My performance on the Training Assignment Test was due to luck.

2. Other causes resulted 1n my performance on the Training Assignment Test,
luck had nothing tO do with it

3. My performance on the Training Assignment Test was due to causes outside
Of myself which I have no influence over.

4. I had nothing to do with my performance on the Training Assignment Test.

5. I had nO control over my level of performance on the Training Assignment
Test.

Effen Am’bue‘ens

1. I performed at the level that I did because of the amount Of effort I put into
this test.

2. My level Of effort resulted in my level of performance on the Training
Assignment Test.

3. My level of effort had nothing to do with my performance on the Training
Assignment Test.

4. I was responsible for my performance on the Training Assignment Test
because Of the amount Of effort I put into the test.

5. Effort played no pan in my performance on the Training Assignment Test.

167

Task Difficelty Attributions

l.
2.

I performed at the level that I did because Of the difficulty of the test.

The type of test was responsible for my performance on the Training
Assignment Test.

Characteristics Of the test caused my performance on the Training
Assignment Test.

Characteristics Of the test had nothing to do with my performance on the
Training Assignment Test.

1 probably would have performed differently with another type Of test.
'On

My performance level was due tO my inborn ability tO perform on this types
Of tests.

My ability was responsible for my performance on the Training Assignment
Test.

My ability had nothing to do with my performance on the Training
Assignment Test.

My performance on the Training Assignment Test would have been
different if it wasn't for my inborn ability.

My inborn ability caused my performance on the Training Assignment Test.

168
APPENDIX J

Pre-Training Self-Efficacy Scale

Using the scale below indicate your level of agreement with the following items. These
items are intended to measure your confidence in performing the Air Defense Task. DO not
answer how you think you are expected to answer. Answer in an honest fashion.

10.

1 = Strongly Disagree
2 = Disagree
3 = Neither agree nor disagree
4 = Agree
5 = Strongly Agree
I feel confident in my ability to perform the Air Defense Task effectively.

I think I can eventually reach a high level of performance on the Air Defense
Task.

I am sure I can learn how to perform this task effectively in a relatively short
period of time.

I don't feel that I am as capable of performing the Air Defense Task as other
people.

On the average, Other peOple are probably much more capable of performing
this task as I am.

I am a fast learner for these types of tasks, in comparison to other people.

I am not sure I can ever reach a high level Of performance on this task, no
matter how much practice and training I get.

It would take me a long time to learn how to perform this task effectively.
I am not confident that I can perform this task successfully.

I doubt that my performance will be very adequate on the Air Defense Task.

169
APPENDIX K

Fairness Perceptions Scale

Using the scale below indicate your level of agreement with the following items. These
items are intended to measure your perceptions regarding your training assignment. DO not
answer how you think you are expected to answer. Answer in an honest fashion.

ONUIhUJN

10.

l = Strongly Disagree

2 = Disagree

3 = Neither agree nor disagree

4 = Agree

5 = Strongly Agree
DISTRIBUTIVE IU STICE

I believe that the decision to assign me to (remedial, advanced) training was
a fair one.

I deserved to be assigned to (remedial, advanced) training.

I think it is unfair that I was assigned to (remedial, advanced) training.

I seriously question my assignment to (remedial, advanced) training.

The decision to assign me to (remedial, advanced) training was a fair one.

I would disagree with anyone who tried to tell me that the decision to send
me to (remedial, advanced) training was a fair one.

Given my performance on the task, I think my training assignment is really
unjustified.

I consider the decision to assign me to (remedial, advanced) training to be a
fair outcome.

I would have made the same training assignment in my own case.

I am really disappointed about my training assignment.

10.

170

PROCEDURAL JUSTICE

The test used to make training assignments was not a reliable and valid
indicator Of my ability to perform the Air Defense Task.

The test used to make training assignments is an unfair test of a person's
true ability to perform the Air Defense Task.

Using the Training Assignment Test to make assignmnets was unfair.

The procedure used to make training assignments included consistent
standards for assigning people.

The assignment procedure Obtained accurate information about each
person's abilities to perform the Air Defense Task.

The assignment decisions were influences by things which should not have
been considered.

Under the circumstances, the process used to decide training assignments
was fair.

I have strong doubts that the Training Assignment Test really measures a
person's ability to perform the Air Defense Task.

I feel other procedures should have been used to make training assignments.

The Training Assignment Test should not have been used to make uaining
assignments.

171
APPENDIX L

Motivation to Learn Scale

Using the scale below indicate your level of agreement with the following items. These
items are intended to measure how motivated you are to learn the material presented in the
(remedial, developmental) course to which you have been assigned. Do not answer how
you think you are expected to answer. Answer in an honest fashion.

10.

1 = Strongly Disagree

2 = Disagree

3 = Neither agree nor disagree
4 = Agree

5 = Strongly Agree

I am motivated to learn the skills emphasized in the (remedial, advanced)
training program.

I will try to learn as much as I can during this training course.
I want to improve my performance on the Air Defense Task.

1 am going to put fonh a lot of effort during this (remedial, advanced)
training program.

I am going to blow off this training program.

I don't expect to pay much attention to the material presented during this
(remedial, advanced) training program.

I am very unmotivated to learn anything during this training course.
If I can't understand some part of the training, I will try harder.
I have no desire to increase my performance on this task.

I really could care less about learning anything in this training course.

1)

2)

3)

4)

5)

172
APPENDIX M

Learning Measure

At which point does Speed change from non-threatening to somewhat threatening?

a) 550 mph
b) 300 mph
c) 100 mph
(1) 275 mph
e) 325 mph

At which point does Altitude change from non-threatenin g to somewhat threatening?

a) 23,000 ft
b) 13,000 ft
c) 35,000 ft
(1) 25,000 ft
e) 15,000 ft

At which point does Size change from non-threatening to somewhat threatening?

a) 40 m
b) 37 m
c) 17 m
d) 65 m
e) 10 m

At which point does Corridor Status change from non-threatening to somewhat
threatening?

a) 0 mi

b) 12 mi
c) 30 mi
(1) 22 mi
e) 10 mi

At which point does Radar change from non-threatenin g to somewhat threatening?

a) Class 1
b) Class 5
c) Class 3
d) Class 8

e) Class 10

6)

7)

8)

9)

10)

11)

173

At which point does IFF change from non-threatening to somewhat threatening?

a)
b)
C)
d)
C)

1.4 Mhz
.75 Mhz
.2 Mhz

1.1 Mhz
1.8 Mhz

Which of the following pairs of attributes should be measured so that they appear
next to each other on the SUMMARY box?

a)
b)
C)
d)
C)

Speed and Direction
IFF and Angle
Altitude and Size
Radar and Direction
Angle and DIreCtion

Which of the following pairs of attributes should be measured so that they appear
next to each other on the SUMMARY box?

a)
b)
C)
d)
C)

Size and Range

IFF and Radar

Altitude and Radar

Angle and Range

Speed and Corridor Status

Which of the following pairs of attributes should be measured so that they appear
next to each other on the SUMMARY box?

a)
b)
C)
d)
C)

Size and Radar

IFF and Corridor Status
Angle and Radar

Speed and Angle

Size and Speed

Which of the following pairs of attributes should be measured so that they appear
next to each other on the SUMMARY box?

IFF and Radar

Angle and Direction
Corridor Status and IFF
Altitude and Radar

Altitude and Corridor Status

Which of the following is the fastest way to Open the SUMMARY box?

ALT then S

F2

F4

ALT then ENTER then S
none of the above

174

12) Which of the following keys activates the menu bar?

a) TAB

b) ENTER

c) DOWN ARROW
(1) F2

e) ALT

13) When the menu bar is highlighted, which of the following keys can both be used to
open the menu selection?

a) F2 and TAB

b) ENTER and DOWN-ARROW
c) CNTRL and ENTER

(1) SHIFT and DEL

e) DOWN-ARROW and SHIFT

175
APPENDIX N

Training Reactions

Using the scale below indicate your level Of agreement with the following items. These
items are intended to measure your confidence in performing the Air Defense Task. Do not
answer how you think you are expected to answer. Answer in an honeSt fashion.

p—l

\OOOQOMh-UJN

H
.O

1 = Strongly Disagree
2 = Disagree
3 = Neither agree nor disagree
4 = Agree
5 = Strongly Agree
I found the training program to be very useful.
I liked the training program.
I'm glad I attended this training course.
The training course was a waste of time.
I didn't get anything out of the training program.
I learned very interesting and useful information during the training course.
The training course was well prepared.

I should have never been made to attend the training program.

I think the training program made me better able to perform the Air Defense
Task.

My performance on the Air Defense Task was completely unaffected by
what I learned in the training course.

176
APPENDIX O

Participant Consent Forms

CONSENT FORM
(experimental groups)

AIR DEFENSE TASK TRAINING STUDY

This study investigates the effectiveness of two Specially designed training programs in
improving performance on a computer tasrt. In the first of two sessions, you will be asked
to complete a Training Assignment Test as well as a few other measures. This first session
will last approximately one hour. You will then be asked to sign up for a susequent
session in which you will be assigned to one of two training programs. You will then be
given an opportunity to work on the Air Defense Task. This second session should last
approximately two hours.

Your participation in this Study is stricdy voluntary and you may refuse to answer any
questions which you may find inappropriate without any penalty. In addition, you can
discontinue the experiment if you feel it necessary to do so. However, in order to receive
credit you must attent BOTH SESSIONS. Your individual results in this study will be
CONFIDENTIAL. You will be assigned a unique three-digit number which will appear on
all questionnaire forms. The experimenter will only report the final data in an aggregate
form which does not allow any particular individual to be identified. These aggregate
results of the experiment will be available from the experimenter.

If you have any quesrions or concerns following this Study you may contact Miguel
Quir'iones at 353-9166.

I have read the consent form and choose to participate in this study:

SIGNED:

 

NAME:

 

DATE: / /

177

CONSENT FORM
(control group)

AIR DEFENSE TASK TRAINING STUDY

This study investigates the effectiveness of a specially designed training program in
improving performance on a computer task. In the first of two sessions, you will be asked
to complete a Knowledge Test as well as a few other measures. This first session will last
approximately one hour. In the second session you will receive training and then given the
Opportunity to work on the computer task. This second session should last around two
hours.

Your participation in this study is strictly voluntary and you may refuse to answer any
questions which you may find inappropriate without any penalty. In addition, you can
discontinue the experiment if you feel it necessary to do so. However, in order to receive
credit you must attend BOTH SESSIONS. Your individual results in this study will be
CONFIDENTIAL. You will be assigned a unique three-digit number which will appear on
all questionnaire forms. The experimenter will only report the final data in an aggregate
form which does not allow any particular individual to be identified. These aggregate
results of the experiment will be available from the experimenter.

If you have any questions or concerns following this study you may contact Miguel
Quifiones at 353-9166.

I have read the consent form and choose to participate in this study:

SIGNED:

 

NAME:

 

DATE: / /

178
APPENDIX P

General Instructions

AIR DEFENSE TASK TRAINING STUDY

    

General Instructions

179

AIR DEFENSE TASK

W

The year is 1994 and you are part of a US. naval carrier group's command and control
team stationed in the middle east. A regional conflict between two nations in this area has
recently broken out. Your mission is to protect sea-going commercial traffic in the area
from accidental and intentional attacks. As history indicates, this is a highly sensitive task.
For example, in 1987, an Iraqi jet accidentally fired two Exocet missiles into the Frigate
USS Stark, killing 37 American servicemen and crippling the vessel. One year later, the
USS Cruiser Vincennes accidentally shot down an Iranian passenger plane killing 290
innocent civilians. Any repeat Of mistakes of this kind will probably lead to a withdrawal
of American forces from the area. Such a withdrawal would have disastrous economic and
political ramifications that would spread well beyond this region.

THE TA K F RCE

A naval carrier battle team is an awesome array of ships and support units. It consists of a
concentric ring Of missile firing warships which prOtect the aircraft carrier at its center. The
aircraft carrier, in return, provides an overall umbrella of air protection for the entire task
force. The carrier's 90 planes can unleash air strikes against targets at land, sea, and even
underwater. A carrier group can dominate up to 196,000 square miles of ocean. Your
carrier group consists of the Carrier itself, a Ticonderoga class Aegis Cruiser, AWACs
reconnaissance planes and a land based Coastal Air Defense (CAD) unit. Although the
Carrier itself is equiped with some air patrol capacities, the Cruisers, AWACs and CAD
units provide the bulk of air traffic patrol. Taken together, the air patrol groups of the
Carrier, the Cruiser, the AWACs and the CAD unit make up the command and control
team.

TEAM MISSION

The team of which you are a part, will role play the Commanding Officers of various units
in the carrier group. Your mission is to monitor the air space surrounding the carrier
group, making sure that neutral ships are nOt attacked. In performing this role, you must
make certain that you do not allow loss of life resulting from accidental or intentional
attacks on ships in the task force. At the same time, it is also of paramount importance that
you do not inadvertently shoot down friendly military aircraft or civilian aircraft. Many ,
passenger flights move in and out of the region, and friendly military aircraft from nations
not involved in the conflict also patrol the area. The navy can ill-afford any mistakes of
either the Stark or Vincennes variety.

DE ISI NS

180

Your task is to decide what response the carrier group should make toward incoming
aircraft. Aircraft that are being tracked on radar are called mgets. You base your decisions
on data you collect by measuring characteristics of the air targets. These measures are
obtained from sophisticated radar equipment. You muSI make a critical choice regarding
each target. There are five pOtential responses, IGNORE, MONITOR, WARN, READY,
and DEFEND. These are described below:

IGNORE:

MONITOR:

WARN:

READY:

DEFEND:

This means that the carrier group should devore no further attention to the
target, but instead focus on Other possible targets in the area. The group
should never ignore a target that might possibly attack. This would mOSt
assuredly lead to loss of lives on the ship attacked.

Here the carrier group should continuously track the target on radar. A
carrier group can monitor only a few targets, thus monitoring diminishes the
group's overall patrol capacity.

In this case the carrier group sends a message to the target indentifying the
group and alerting the target to steer clear. Warning targets that should be
ignored detracts from the salience of legitimate warnings. Warning targets
that intend to attack is also bad, since the warning makes it easier for the
attacker to locate the ship.

This means to steer the ship into a defensive posture and to set defensive
weapons on automatic. A ship in a readied position is rarely vulnerable to
attack. This stance should not be taken to non-threatening targets since
weapons set to automatic can fire mistakenly at innocent targets that fly too
close to the carrier group. A ship in this position cannot readily use
Offensive weapons on th target.

This is "weapons away" and means to attack the target with Tomahawk
cruise missiles. A defend decision cannot be aborted once initiated Defend
is an appropriate re3ponse when you feel an attack is imminent.

HARA

ITI

181

FTAR E

The incoming air targets can be measured on nine attributes. These are listed below along
with the ranges of possible values on the attributes:

(1) SPEED:

(2) ALTITUDE:

(3) SIZE:

(4) ANGLE:

(5) IFF:

(6) DIRECTION:

(7) CORRIDOR
STATUS:

@)RADAR
TYPE:

(9) RANGE:

100 to 800 miles per hour (mph)

5,000 to 35,000 feet

size of the target ranging from 10 to 65 meters
-15 (rapid descent) to +15 degrees (rapid ascent)

"Identification Friend or Foe". This is a radio signal that identifies
whether an aircraft is civilian, para-military, or military, ranging
from .2 Mhz (an airliner) to 1.8 Mhz (a fighter).

from +30 degrees (passing far to the east or west of the carrier) to 0
degrees (coming straight to the carrier).

a corridor is a lane open to commercial air traffic. Status is
expressed in terms of miles from the center of the corridor, ranging
from 0 miles (in the middle of it) to 30 miles (way out of it)

the kind of radar possessed by the aircraft ranging from Class 1
(weather radar only to Class 9 (weapons radar)

distance of the aircraft from the Carrier ranging anywhere from 0 to
200 miles

182
DETERMINING THE LEVEL OF THREAT

In general, the degree to which an incoming target is threatening depends on its standing on
these nrne attributes. There are five simple rules to remember in determining the danger
associated with any target:

(a) all else being equal, in terms of IFF, milita_ry targets are more threatening than civilian
targets (see attribute #5)

(b) SPEED and DIRECTION go together, so that fast targets coming straight in are most
threatening (see #1 and #6 above). Speed alone and direction alone mean nothing.
There rs nOthing to fear if fast targets are n0t headed toward the group. There rs
nothing to fear from slow objects headed directly for the group.

(O) ANGLE and RANGE go together, so that descending targets that are close are
especially threatening (see #4 and #9 above). Angle alone and range alone mean
nothing. Descending targets that are far away, or close targets that are on the way up
are not threatening.

(d) ALTITUDE and CORRIDOR STATUS go together, so that low flying targets that are
way outside the corridor are especially threatening (see #2 and #7 above). Altitude
alone and corridor status alone mean nOthing. There is nothing to fear from high
flying targets well outside the corridor or low flying targets in the middle of the
corridor.

(e) SIZE and RADAR go together, so that small objects with weapons radar are
especially threatening (see #3 and #8 above). There is nothing to fear from small
targets with weather radar only or from large targets with weapons radar.

W

The following chart will help you determine the level of threat associated with the different
values of all nine attributes.

Degree of Threat

 

Non-HmmMgwmam—W
Speed 100-275 mph 325-500'mph 600-800 mph
Altitude 35,000-27,000 ft 23,000-17,000 ft 13,000-5,000 ft
Size 65-43 m 37-23 m 17-10 m
Angle +15 to +8 dgs +3 to -3 dgs -8 to -15 dgs
IFF .2 to .6 Mhz .9 to 1.1 Mhz 1.4 to 1.8 Mhz
Direction 30 to 22 dgs 18 to 12 dgs 8 to 0 dgs
Corridor St. 0 to 8 mi 12 to 18 mi 22 to 30 mi
Radar Type Class 1 & 2 Class 5 Class 8 & 9

Range 200 to 110 mi 90 to 60 mi 40 to 1 mi

 

183

HOW RULE MBINE TO DETERMINE JUDGEMENTS

The five rules combine to determine overall threat represented by the target. So for
example, if a team detected a (a) military aircraft that is (b) flying in straight and fast, (c)
was close and descending, (d) was flying low and way outside the conidor, and (e) was
small and had weapons radar; the ship is being attacked and should DEFEND.

If the team detected (a) a civilian aircraft, that is (b) passing slow at an angle, (c) was far
away and ascending, (d) was flying high and in the middle of the corridor and (e) was large
and had weather radar, this is a passanger plane that should be IGNORED.

Intermediate responses like MONITOR, WARN, or READY are to be used when the target
is threatening according to some of the rules but not all. For example, a military aircraft
that is close and descending (see rule c), small and with weapons radar (see rule e), but is
traveling slowly at an angle to the group (see rule b), and is high and in the middle of the
corridor (see rule d) might need to be WARNED. It should not be IGNORED, but neither
should it be sh0t down.

DECISION OUTOOMES

Once you make your decision to either IGNORE, MONITOR, WARN, READY, or
DEFEND, you will be provided with feedback regarding your performance. Five
outcomes can result from the defensive posture that yOu choose. These are:

HIT: A hit means that your decision was exactly correct. So for example, the
target should have been "warned" and that was exactly what the team
decided. A hit is worth 2 points to your overall score. The color bars at
the top and bOttom of the screen will be green when this occurs.

NEAR MISS: A near miss means that you were off by one place in terms of your
agressiveness level. For example, if your decision was "warn" when it
should have been ”monitor" this would be a near miss (a little too
aggressive). It would also be a near miss if your decision was "warn"
when it should have been "ready" (a little too passive). A near miss is a
pretty good outcome. A near miss is worth 1 point. The color bars at
the top and bottom of the screen will be aquamarine when this occurs.

MISS: A miss means that your decision was off by two places. This is worth 0
points. The color bars will be purple when this occurs.

INCIDENT: An incident means that your decision was off by three places. An
incident means that you just narrowly avoided disaster (e. g. being hit
yourself or mistakenly shooting down a friendly target). This outcome
results in a loss of 1 point. The color bars will be red when this occurs.

DISASTER: A disaster means that your decision was off by four places. This
outcome results in a loss of 2 points. The color bars will be black in
this case.

184
APPENDIX Q

Participant Debriefing Forms
EXPERIMENTAL GROUPS

Thank you very much for your participation in this study. The purpose of this study was
to examine the effects of assigning individuals to differently framed or labeled training
programs. Educational systems as well as work organizations routinely assign individuals
to different training programs without considering the feedback implications that these
assignments can have. You may be well aware of the negative consequences of assigning a
child to a class called “Special Ed". These labels can have demoralizing effects on the
trarnees.

Your training assignment was determined RANDOMLY. Your actual performance on the
Training Assignment Test had no bearing on your training assignment. The same was true
of all Other participants. It is important that you undersrand that the assignments in no way
reflect yours and the Other participant‘s ability to perform the Air Defense Task. It was felt
that this minor deception was necessary to investigate the effects of this important
phenomenon. I hope that you do not come away from this task feeling differently about
your abilities (unless it is more positive!). If you have any questions about the results of
the study or feel you would like more information at a later point in time please do not
hesitate to contact Miguel A. Quifiones at 353-9166. It is VERY IMPORTANT that you
DO NOT DISCUSS this experiment with anyone else until everyone has completed the
study. This would be around the end of the term. Talking to other potential participants
could jeopardize the usefulness of the results of this study. Again, thank you for your
cooperation and good luck during your studies here at Michigan State.

CONTROL GROUP

Thank you very much for your participation in this study. The purpose of this study was
to examine the effects of assigning individuals to differently framed or labeled training
programs. Educational systems as well as work organizations routinely assign individuals
to different training programs without considering the feedback implications that these
assignments can have. You may be well aware of the negative consequences of assigning a
child to a class called "Special Ed". These labels can have demoralizing effects on the
trarnees.

You were part of the control group. You did not receive any information regarding labels
and training assignments. By having a control group I will be able to establish a "reference
point" from which to judge the effects of labeling individuals and training programs. The
experimental group's training assignment was determined RANDOMLY. Their actual
performance on a Training Assignment Test had no bearing on their training assignment.
The same was true of all other participants. It is important that you understand that their
assignments in no way reflect theirs and the other participant's ability to perform the Air
Defense Task. It was felt that this minor deception was necessary to investigate the effects
of this important phenomenon. I hope that you do nor come away from this task feeling
differently about your abilities (unless it is more positive!). If you have any questions
about the results of the study or feel you would like more information at a later point in time
please do not hesitate to contact Miguel A. Quifiones at 353-9166. It is VERY
IMPORTANT that you DO NOT DISCUSS this experiment with anyone else until
everyone has completed the study. This would be around the end of the term. Talking to
other potential participants could jeOpardize the usefulness of the results of this study.
gigain, thank you for your cooperation and good luck during your studies here at Michigan
tate.