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MIC CH IGAN STATE

II I IIII IZII

 

 

 

 

 

 

 

 

 

 

 

 

IIIIIIIIIIIIIIIIIIIIIIIIIIIII

0091 9338

This is to certify that the

thesis entitled

Goal Setting, Cognitive Ability
and Task Strategy

presented by

Jillian Shapiro

has been accepted towards fulfillment
of the requirements for

M.A. Psychology

degree in

 

 

 

Date June 5. 1990

0.7639 MS U is an Affirmative Action/Equal Opportunity Institution

 

 

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LIBRARY
Mlchlgan State
Unlversity

 

PLACE IN RETURN BOX to remove this checkout from your record.
TO AVOID FINES return on or before date due.

DATE DUE DATE DUE DATE DUE I'

 

firm 42 a 2002

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

——I=‘——I

 

 

MSU Is An Affirmative Action/Equal Opportunity Institution

emote”!

GOAL SETTING, COGNITIVE ABILITY AND TASK STRATEGY

BY

Jillian Shapiro

A THESIS

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

MASTER OF ARTS
Department of Psychology

1990

ABSTRACT
GOAL SETTING, COGNITIVE ABILITY AND TASK STRATEGY
BY

Jillian Shapiro

Research has demonstrated that the positive motivational
effects of specific and difficult goals commonly found for
simple tasks is less reliable on complex tasks. The present
study attempts to describe the effects of goals on
performance on complex tasks through the influence of
strategy type, strategy development effort and cognitive
ability. Subjects working on a crossword puzzle task were
assigned either a general goal, an easy to moderate goal, or
a difficult goal. Two general types of strategies used by
subjects contributed unique variance on performance.

Results indicated no relationship between goals and
performance. However, for one strategy type, individuals
with difficult goals chose a strategy leading to better
performance than those with general goals. For the other
type of strategy, this relationship was moderated by general
cognitive ability. In addition, although there was no main
effect of effort on strategy type, this relationship was

moderated by ability for one strategy type.

To my parents, Max and Lois Shapiro,

with love and gratitude.

iii

ACKNOWLEDGEMENTS

I would like to thank the members of my thesis
committee, Kevin Ford, John Hollenbeck, and Dan Ilgen, for
sharing their knowledge and ideas with me. I would
especially like to thank John, without whose creativity,
intelligence, good humor, and never-ending enthusiasm, I
could not have completed this thesis.

Several friends and family also deserve special thanks.
I am indebted to Cheri Ostroff for her technical help and
friendship all through graduate school. I also appreciate
the support of several other good friends, Ellen Herzog,
Fereshteh Khorrami, Helene Krifcher, and Mary Ann Richmond
and of my wonderful brother, David, who always makes me feel
better when I need to. I am also infinitely grateful to my
parents for their financial and emotional support, for
sharing the good and the bad, for worrying for me, and for
fun visits and vacations during school.

I want to express my tremendous appreciation for the
help of Keith Hattrup, my "partner in adventures", my
champion, and my friend. Thank you for helping me with
statistics, programming, and writing. Thank you for

listening to my stories. Thank you for laughing with me.

iv

But most of all, thank you for letting me know the terrific
person that you are.-

Finally, I would like to extend additional appreciation
to Keith, my brother, my father, and especially my mother
for helping to score and code the plethora of data involved
in this research. This help saved me much time and

frustration.

TABLE OF CONTENTS

LIST OF TABLES . . . . . . . . . . . . . . . . . .
LIST OF FIGURES . . . . . . . . . . . . . . . . .
INTRODUCTION . . . . . . . . . . . . . . . . . .
Goal Setting . . . . . . . . . . . . . . . .

Goal Difficulty and Specificity, Defined

Goal Specificity and Difficulty Effects
Task Complexity and Goal Setting . . .
Task Strategy . . . . . . . . . . . . . . .

Task Strategy, Strategy Development and
Setting 0 O O O O O O O O O O O 0

Task Strategy Defined . . . . . .
Strategy Development . . . . . . .

Task Strategy and Goal Setting,
Empirical Research . . . . .

Cognitive Processes and Task Strategy .
Cognitive Ability Defined . . . .
General Cognitive Ability . . . .
Specific Cognitive Abilities . . .
Task Specific Skills . . . . . . .

Ability and Goal Setting . . . . . . .

Conclusions . . . . . . . . . . . . . .

Summary . . . . . . . . . . . . . . . .

Hypotheses . . . . . . . . . . . . . . . . .

11

11

11

13

17

24

24

25

27

28

29

36

36

37

RESULTS

Hypothesis 1: The Overall Interaction of
Goal Setting and Cognitive Ability on
Performance . . . . . . . . . . . . . .

Hypothesis 2: Goal Setting and Cognitive
Effort 0 O O O O O O O O O I O O O O O O

Hypothesis 3: Overall Interaction of Goal
Setting and Cognitive Ability on
Strategic Chaice O O O O O O O O O O O O

Hypothesis 4: The Strategic Choice Process

Hypothesis 5: Factors that Mediate the
Relationship Between Goals and Strategic
Chaice O O O O C O O O O O O I O C O I O

Hypothesis 6: The Immediate Determinants of
Performance . . . . . . . . . . . . .

Hypothesis 7: Factors that Mediate the
Relationship Between Goals and
Performance . . . . . . . . . . . . . .

Hypothesis 8: The Determinants of Strategy
Quality . . . . . . . . . . . . . . . .

Subjects . . . . . . . . . . . . . . . . . . . .
Task . . . . . . . . . . . . . . . . . . . . . . .
Design . . . . . . . . . . . . . . . . . . . . . .
Procedure . . . . . . . . . . . . . . . . . . .

Measures 0 O O O I O O O O O O O O O O O O O O 0

Cognitive Ability . . . . . . . . . . . . .

Manipulation Check . . . . . . . . . . . . .
Effort Towards Strategy Development . . . . .
Strategies Used . . . . . . . . . . . . . . .

Quality of Strategy . . . . . . . . . . . . .

Analysis . . . . . . . . . . . . . . . . . . . . .

vii

37

38

4O

41

41

44

44

44

49

49

49

50

50

52

52

53

54

55

55

56

61

Descriptive Statistics . . . . . . . . . . . . .
Factor Analysis of the Strategy Items . . .
Ability Tests . . . . . . . . . . . . . . .
Objective Measures of Strategy . . . . . .

The Hypotheses . . . . . . . . . . . . . . . . .

The Overall Interaction of Goal Setting and
Cognitive Ability on Performance . . .

Goal Setting and Cognitive Effort . . . . .

Overall Interaction of Goal Setting and
Cognitive Ability on Strategic Choice

The Strategic Choice Process . . . . . . .

Factors That Mediate the Relationship Between

Goals and Strategic Choice . . . . . .

The Immediate Determinants of Performance .

Factors that Mediate the Relationship Between

Goals and Performance . . . . . . . .

The Determinants of Strategy Quality

Path Analysis of the Overall Model . . . . . . .
Verbal and Analytic Ability . . . . . . . . . .
DISCUSSION . . . . . . . . . . . . . . . . . . . . .
Discussion of Results . . . . . . . . . . . . .

The Overall Interaction of Goal Setting and
Cognitive Ability on Performance . . .

Goal Setting and Cognitive Effort . . . . .

Overall Interaction of Goal Setting and
Cognitive Ability on Strategic Choice

The Strategic Choice Process . . . . . . .
The Immediate Determinants of Performance .

Additional Analyses . . . . . . . . . . . . . .

viii

61

61

64

64

65

65

66

66

7O

7O

72

72

72

75

75

80

8O

8O

82

83

84

84

85

Overall Interaction of Goal Setting and
Cognitive Ability on Strategic Choice .

The Strategic Choice Process

Factors That Mediate the Relationship Between
Goals and Strategic Choice .

The Immediate Determinants of Performance . .

Factors that Mediate the Relationship
Goals and Performance

The Determinants of Strategy Quality

Integration of Post Hoc Analyses .

Limitations of this Study

Recommendations for Future Research
Final Comments

LIST OF REFERENCES

APPENDIX A:
APPENDIX B:
APPENDIX C:

APPENDIX D:

APPENDIX E:
APPENDIX F:

ENDNOTES .

Summary of Hypotheses
Consent Form

Experience With Crossword Puzzles

Between

Manipulation Check on Goal Difficulty and
Goal Specificity .

Effort Towards Developing Strategies . . .

Strategies Used

ix

87

91

95

95

95

95

104

111

113

115

117

125

127

128

129

130

132

136

Table

Table

Table

Table

Table

Table

Table

Table

Table

LIST OF TABLES

Means, Standard Deviations, and
Reliabilities . . . . . . . . . . . . .

Intercorrelations Among Variables . . . . .

Regression Analysis with Performance (Score)
as the Dependent Variable and Cognitive
Ability and Goals Entering as Main and
Interaction Effects . . . . . . . . . .

Regression Analysis with Strategy Development
Effort as the Dependent Variable and
Goal Condition Entering as a Main
Effect. . . . . . . . . . . . . . . . .

Regression Analysis with Strategy Type
(Easy/Medium) as the Dependent Variable
and Cognitive Ability and Goals Entering
as Main and Interaction Effects . . . .

Regression Analysis with Strategy Type
(Easy/Medium) as the Dependent Variable
and Strategy Development Effort and
Cognitive Ability Entering as Main and
Interaction Effects . . . . . . . . . .

Regression Analysis with Performance (Score)
as the Dependent Variable and Strategy
Type (Easy/Medium) and Cognitive Ability
Entering as Main and Interaction
Effects . . . . . . . . . . . . . . .

Regression Analysis with Score as the
Dependent Variable and Verbal Ability
and Analytic Ability Entering as Main
Effects . . . . . . . . . . . . . . . .

Regression Analysis with Strategy Type
(Easy\Medium) as the Dependent Variable
and Verbal Ability and Analytic Ability
Entering as Main Effects . . . . . . . .

62

63

67

68

69

71

73

77

79

Table

Table

Table

Table

Table

Table

Table

Table

Table

10.

11.

12.

13.

17.

18.

Regression Analysis with Strategy Type
(Scanning) as the Dependent Variable and
Cognitive Ability and Goals Entering as
Main and Interaction Effects . . . . . .

Regression Analysis with Strategy Type
(Scanning) as the Dependent Variable and
Strategy Development Effort and
Cognitive Ability Entering as Main and
Interaction Effects . . . . . . . . . .

Regression Analysis with Strategy Type
(Scanning) as the Dependent Variable and
Strategy Development Effort Cognitive
Ability and Goals Entering as Main and
Interaction Effects . . . . . . . . . .

Regression Analysis with Performance (Score)
as the Dependent Variable and Strategy
Type (Scanning) and Cognitive Ability
Entering as Main and Interaction
Effects . . . . . . . . . . . . . . . .

Regression Analysis with Strategy Quality as
the Dependent Variable and Cognitive
Ability and Goals Entering as Main and
Interaction Effects . . . . . . . . . .

Regression Analysis with Strategy Type
(Scanning) as the Dependent Variable and
Verbal Ability and Analytic Ability
Entering as Main Effects . . . . . . . .

Regression Analysis with Strategy Type
(Scanning) as the Dependent Variable and
Analytic Ability and Goals Entering as
Main and Interaction Effects . . . . . .

Regression Analysis with Strategy Type
(Scanning) as the Dependent Variable and
Verbal Ability and Goals Entering as
Main and Interaction Effects . . . . . .

Regression Analysis with Strategy Type
(Scanning) as the Dependent Variable and
Strategy Development Effort and Analytic
Ability Entering as Main and Interaction
Effects . . . . . . . . . . . . . . . .

xi

88

92

96

97

99

100

101

102

105

Table 19. Regression Analysis with Strategy Type
(Scanning) as the Dependent Variable and

Strategy Development Effort and Verbal

Ability Entering as Main and Interaction.
. . . 106

Effects . . . . . . . . . . . . .
. 108

Table 20. Summary of Results . . . . . . .

xii

Figure

Figure

Figure

Figure

Figure

Figure

Figure

Figure

Figure

Figure

Figure

Figure

Figure

1.

LIST OF FIGURES

Representative Tasks for Various Complexity

Levels . . . . . . . . . . . . . . . . .

2. Relationships Among Ability, Goals, and

3.

Performance . . . . . . . . . . . . . .

Expected Interaction Between Ability and

Goals on Performance. . . . . . . . . .

4. Expected Relationships Among Goals, Ability,

5.

12.

13.

and Strategy Type . . . . . . . . . . .

Expected Interaction Between Ability and

Goals on Strategy Type . . . . . . . . .

Expected Relationships Among Strategy

Development Effort, Ability, and
Strategy Type . . . . . . . . . . . . .

Expected Interaction Between Ability and

The

The

Strategy Development Effort on Strategy
Type O O I O I O O O O O O O O O O O O 0

Expected Mediating Effect of Strategy
Development Effort on the Relationship
Between Goals and Strategy Type . . . .

Expected Mediating Effect of Strategy
Type and Strategy Development Effort on
the Relationship Between Goals and
Performance . . . . . . . . . . . . . .

Expected Relationships Among Goals, Ability,

and Strategy Quality . . . . . . . . . .

Expected Interaction Between Ability and

Goals on Strategy Quality . . . . . . .

Model of Goal Setting and Strategy on a

Complex Task . . . . . . . . . . . . . .

Interaction Effect Between g and Use of

Easy/Medium Strategy on Score . . . . .

xiii

39

39

42

42

43

43

45

45

47

47

48

74

Figure
Figure

Figure

Figure

Figure

Figure

14.

15.

16.

17.

18.

19.

Path Analysis . . . . . . . . . . . . . . . 76
Modified Path Analysis . . . . . . . . . . 76

Interaction Effect Between g and Goals on
Use of the Scanning Strategy . . . . . . 89

Interaction Effect Between Strategy
Development Effort and g on the Use of
the Scanning Strategy . . . . . . . . . 94

Interaction Effect Between Goals and
Analytic Ability on the Use of the
Scanning Strategy . . . . . . . . . . . 103

Interaction Effect Between Strategy

Development Effort and Analytic Ability
on the Use of the Scanning Strategy . . 107

xiv

INTRODUCTION

Goal setting is a well researched area in
organizational psychology. Much of this research has
focused on the effects of goal characteristics, such as goal
specificity and goal difficulty, on performance. While it
has long been accepted that specific and difficult goals
lead to improved performance, researchers are more recently
exploring the factors that moderate this goal-performance
relationship.

Task type has proven to be a consistent moderator of
the relationship between goal characteristics and
performance. Recent research has shown that on complex
tasks, the effects of goals are less pronounced than on
simple tasks (Wood, Mento, & Locke, 1986). Further, goal
setting may influence performance through its effect on task
planning (e.g., Earley, Connoly, & Ekegren, 1989).

This thesis addresses the effects of specific,
difficult goals on task plans on a complex task and the role
of cognitive ability on this relationship. The first
section of this thesis reviews goal setting literature.
Following this, literature regarding task strategy and task

type as it relates to goal setting is discussed. Cognitive

2
ability is addressed as a potential moderator of goal
effects on strategy.
9.93m

Goals are defined by Locke, Shaw, Saari, & Latham
(1981) as "what an individual is trying to accomplish;
[they are] the object or aim of action" (p.126). At the
individual level, research has often shown task performance
to be related to goal setting. This relationship is the
result of two primary functions of goals. First, goals
motivate people to put forth effort. Second, goals serve to
guide this effort in the correct direction. The occurrence
of these two functions is largely dependent upon the
characteristics of goals. In particular, goal specificity
and difficulty are primarily related to task performance.
The following sections will define and discuss these two
aspects of goals.
3 J EIEE° J! I E 'E' .! I E' 1

According to Locke et a1. (1981), a major attribute of
a goal is its content. Content refers to the actual task
performance outcome expected, such as the amount of product
assembled. Dimensions of goal content that have received
the most attention in literature are goal difficulty and
goal specificity.

Goal specificity refers to the precision with which the
quality or quantity of the expected outcome is described
(Locke et a1., 1981). Specificity is usually stated in

terms of quantity per amount of time, such as words per

3
terms of quantity per amount of time, such as words per
minute or miles per hour . A more specific goal is one that
is stated in more precise quantitative or qualitative units
(Ilgen, Salas, Shapiro & Weiss, 1989). For example, a goal
of ‘typing sixty words per minute' is more specific than a
goal of ‘improving typing'. Likewise, a goal of ‘improving
typing' is more specific than a goal of ‘1earning office
skills'. Most studies define the most general goal as a
‘do your best' condition.

In addition to goal specificity, goal difficulty is a
frequently addressed construct in the literature. A more
difficult goal is one that requires more effort and
attention for goal accomplishment than an easy goal. A
difficult goal may also require more knowledge and skill
than an easy goal (Locke et al., 1981). According to
Terborg (1976), goal difficulty has two elements. The first
of these is the normative probability of goal attainment.
This represents the level of difficulty of the task from the
perspective of all who perform the task.’ It is described by
a ratio of the number of individuals who attain a goal to
the number who attempt it. A second component of goal
difficulty is an individual's probability of goal
attainment. This category of goal difficulty is
characterized by the ratio of an individual's success at
goal attainment to his or her attempts. A third element of

goal difficulty is the cost of goal attainment (Naylor &

4
Ilgen, 1984). This cost may be reflected in varying amounts
of ability, time and effort, skill, and experience.

Many empirical studies describe goal difficulty in
terms of absolute or objective probability of goal
attainment (e.g., Garland, 1984: Hollenbeck & Brief, 1987:
Latham, Steele & Saari, 1982: Locke, 1982: Terborg & Miller,
1978). If this is the case, researchers may partition goal
difficulty levels by an absolute probability of goal
attainment based on a pilot sample or by a post hoc analysis
of performance. Some studies have used the ability of the
individual performing the task and his or her past
performance as a relative index of difficulty for each
subject (e.g., Yukl & Latham, 1978: Hollenbeck, Williams &
Klein, 1989). Few, if any, studies define goal difficulty
in terms of the cost of goal attainment to the task
performer (Naylor & Ilgen, 1984).

E J E °E° '! i EIEEI 1! EEE !

Locke's (1968) goal theory asserts that specific,
difficult goals, if accepted by the task performer, result
in higher levels of task performance relative to easy goals,
‘do best' goals or no goals. This is because specific,
difficult goals, when accepted by the task performer, serve
to increase and improve effort, persistence, direction, and
strategy development (Locke, et al., 1981).

A large majority of studies have found a positive
relationship between difficult, specific goals and

performance in both the laboratory and the field (although

5
results in the field are sometimes contaminated) (Locke, et
'al., 1981).

Most goal setting studies have varied only goal
difficulty (e.g., Yukl 8 Latham, 1978: Latham, Steele 8
Saari, 1982) or have varied both difficulty and specificity
(e.g., Latham 8 Saari, 1979: Ivancevich, 1977). In general,
studies do not vary goal specificity alone. This may be
because increased difficulty often occurs concurrently with
increased goal specificity. That is, as specificity
increases above a ‘do best' level, task demands also tend to
increase simultaneously.

For example, Locke (1982) varied the goal level of
specific goals in a laboratory experiment. Locke's subjects
participated in a one minute brainstorming task to give uses
for common objects. Locke assigned fourteen different goal
levels among subjects. These ranged from easy to
impossible. The goal levels began at a value of 2 (easy)
and increased by increments of two until a value of 28
(impossible). Results showed that performance continued to
increase as a function of goal level, even for goals that
were beyond the capabilities of all subjects. Other studies
have found similar results (e.g., Hollenbeck 8 Brief, 1987).

Latham and Steele (1983) compared ‘do best' goals with
various levels of specific and difficult goals in a
laboratory study. Subjects in this experiment participated
in a toy assembly project. Results of the experiment showed

that specific goals led to higher performance than ‘do your

6
best' goals and that performance was positively and linearly
related to goal difficulty. Similar results have been
repeated in other experiments (e.g., Latham 8 Saari, 1979).

A recent laboratory experiment by Locke, Chah, Harrison
and Lustgargen (1989) addressed the issue of separating the
effects of goal specificity and difficulty. Locke et al.
(1989) found that as goal specificity decreased, performance
variance increased. Further, they found that task
performance improved as a function of goal level. The
authors explain that while goal difficulty acts to
‘energize' effort and, over time, contribute to persistence,
goal specificity serves to direct task activity (Locke, et
al., 1989).

Although the results of the studies described above
have been consistent, the extent of the relationship between
difficult, specific goals and easy or ‘do best' goals is
influenced by task type. A meta-analysis by Wood et al.
(1986) showed this relationship is moderated by task
complexity such that the performance gains associated with
specific, difficult goals are greater for simple than for
complex tasks. The following section will define task
complexity and discuss this relationship.

I 1 ; J '! g 3 J 5 t!'

In contrast to simple tasks, complex tasks generally
have multiple and conflicting end states, multiple and
conflicting paths to end states, and uncertain linkages

among outcomes and paths to outcomes. These characteristics

7
contribute to increased information load and cognitive
demands on individuals (Campbell, 1988).

Wood (1986) describes a model of tasks in which there
are two types of input components for any task. These are
acts and information cues. Acts are the activities required
by the task for the creation of a product (e.g., lifting).
Information cues are pieces of information regarding the
task that an individual can use to make judgments required
during the course of the task. In this model, there are
three types of task complexity. These are (1) component
complexity, (2) coordinative complexity, and (3) dynamic
complexity.

Component complexity is a function of the number of
acts and information cues required for task completion. As
these increase, component complexity increases.

Coordinative complexity refers to relationships between task
inputs and products. This includes timing, frequency,
intensity, and location requirements for performing task
acts. Dynamic complexity is described by the degree to
which individuals performing a task need to adjust to
changes in the means-ends hierarchy of task acts during task
activity.

Wood (1986) describes total task complexity as a linear
combination of these three types of complexity, where
dynamic complexity is weighted more heavily than
coordinative complexity and coordinative complexity is

weighted more heavily than component complexity.

 

 

I
when mm sconce mu
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Miracle manage-
ment

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work

' various complexity
418).

8

Originally, goal setting theory and research focused on
simple tasks and avoided issues of ability and strategy.
Locke (1968) emphasized ". . . simple tasks in which
learning complex new skills and making long-term plans and
strategies is not necessary to achieve goals -- tasks of the
type in which effort and concentration are likely to have a
relatively direct effect on output or choice." (p. 161.)

Only recently, as researchers explore more facets of
goal theory, has task complexity been increasingly
considered to be of primary importance for goal setting
research (e.g., Huber, 1985: Wood, Mento 8 Locke, 1986:
Locke, et al., 1981). Therefore, there is a need to further
study goal effects on complex tasks.

Using the Wood (1986) definition of task complexity,
Wood, et al. (1986) did a meta-analysis examining the
relationship between task complexity and goal setting.

Based on a complexity scale ranging from one to ten, the
authors were able to classify general task types (e.g., toy
assembly, school or college course work) on the scale. This
classification is shown in Figure 1. Of the 125 studies
examined in the meta-analysis, eighty-seven had a complexity
rating of three or less and the remaining thirty eight fell
between four and seven.

Wood et al. found that task complexity moderated the
relationships between goal attributes (i.e., specificity and
difficulty) and task performance. The authors found that

the performance gains associated with specific, difficult

 

1
I
Reecion Time W Toy w Seeing Min sated or oeleoe SepeMeion Sdence no
work mu work W
Simple arith- Anagram Prediction Midde menace-
merit: wort: mt
Perceptual Typing Floor plea TecMicien
speed analysis wort:

Figure 1. Representative tasks for various complexity
levels. From Wood et a1. (1986, p. 418).

10
goals were greater for simple tasks than for complex tasks.
The results of the meta-analysis indicated that the
moderating effect of task complexity was greater for the
relationship between difficult goals and performance than
for the relationship between difficult and specific goals
and performance. Overall, at all levels of task complexity,
some performance improvement resulted from setting specific
and difficult goals as opposed to easy, general goals.
However, this effect was largest for simple tasks and
smallest for complex tasks (12.15% and 7.79% productivity
increases respectively). The level of productivity increase
for moderately complex tasks fell between that of simple and
most complex tasks. The authors suggest that the
differences in goal effects between complex and simple tasks
may disappear over time as individuals develop effective
strategies and gather relevant information for task
completion. Therefore, they stress that future research be
aimed at looking at the effects of information sharing,
cognitive abilities, and strategy development on goal
setting.

The Wood et al. finding is consistent with Locke's
(1968) belief that strategy and ability issues associated
with complex tasks will influence goal effects. If research
is to study goal difficulty effects on complex tasks,
researchers must integrate strategy and ability issues into

goal setting models. The following sections will discuss

11
task strategy, strategy development and the relationship

between strategy and goal setting.

I§§K_§£I§§§g!

e:. : ‘os ,-_ we '1 - 0911:! 11! ~-. _' :-

Ta§k_§;zatggy_ggfingg. A generally accepted definition
of task strategy is that it is a plan of action (Locke, et
al., 1981). This is a very general definition, and
therefore permits a great degree of variance in its
operationalization and description across studies. Many
experimenters operationalize strategy by using only a post-
experimental self-report measure (e.g., Locke, Frederick,
Lee 8 Bobko, 1984: Earley 8 Perry, 1987: Earley, Wojnaroski
and Prest, 1987). For example, a laboratory study by Earley
et a1. (1987) assessed strategy, which was defined as the
"planning or organizing activities of an individual (p.
108)." This assessment was made by giving subjects a four
item post task questionnaire reflecting the number of steps
of a plan, a plan's importance to task accomplishment, the
presence of a pre-task plan development, and the presence of
any plan at all.

Locke, et al. (1984) distinguish between two facets of
task activity. These are persistence and direction of
effort on one hand and strategy on the other. According to
Locke et a1. (1984) while these first two behaviors do lead
to goal accomplishment, they are not strategies. Strategies
are different in that they involve different ways of

performing a task. Like Earley et al. (1987), Locke et al.

12
measured strategy with a post experimental questionnaire.
The questionnaire listed possible strategies and asked
subjects to specify if they used these strategies and to
what extent they were used.

Earley, Lee and Hanson (1990) defined task strategy as
the series of steps that an individual goes through to
complete a task. Earley et a1. (1990) used the strategic
management literature in conjunction with expert lists of
strategies to develop dimensions of task strategy quality.
The dimensions arrived at were time frame of the plan, the
breadth of duties encompassed by the plan, contingency
aspects of the plan (e.g., "I make sure that if something
unexpected happens, my work plan will give me the
flexibility to handle things"), and the resources involved
by the plan.

Overall, the literature on task strategy does not
present a single description of task strategy. It may be
the case, however, that strategy does not lend itself to
such a description. Different dimensions of task strategy
are relevant only for different tasks. For example, on some
tasks, unexpected events may not occur. In such a case,
Earley et al.'s (1990) dimension of contingency would not be
relevant. Therefore, it is necessary to define strategy
generally, as the procedure followed to accomplish a task
and to note that these procedures may vary with respect to
detail, complexity, or other dimensions, depending on the

task.

13

St:a;ggy_p§yglgpment. The production of the procedures
considered and the process of selection of options by an
individual is the process of strategy development. Locke et
a1. (1981) describe strategy development as the fourth
mechanism through which goal setting influences performance
(effort, persistence, and direction being the other three).
Like effort, persistence, and direction, strategy
development is motivated by goals, but unlike these other
three mechanisms its effect is indirect, due to the
cognitive nature of strategy development and its dependence
upon skill development and creative problem solving (Locke
et al., 1981).

Wood and Locke (1990) discuss the process by which
individuals arrive at task strategies. Wood and Locke's
model describes the steps that an individual goes through to
arrive at a task strategy. The model assumes individual
goal commitment. First, the authors explain that the task
and the goal themselves automatically prompt an individual
to search for a “stored plan." A stored plan of action is
one that exists in an individuals subconscious prior to the
introduction of a task or goal. This type of plan is
brought to a person's attention by association with a
relevant task. A stored plan is either universal or task
specific. A universal plan is one that applies to all
tasks. Specifically, a universal plan may be the direction
of attention, the expending of effort, or persistence of

task behavior. Individuals are generally only marginally

14
aware of these plans. A task specific plan is one that has
been previously learned through task experience or modeling.
It has been learned to the extent that even though the
behavior it generates is applicable only to one task or a
set of analogous tasks, it is used almost automatically by
an individual. Several stored plans may be prompted
simultaneously. If this is the case, an individual must
decide which plan is best.

"New task specific plans" are developed by an
individual when he or she decides that none of the available
stored plans are appropriate to the task at hand. The
authors explain that the production of these plans, unlike
the stored plans, require the individual to consider the
task and situation in order to gather relevant task
information. The quality and comprehensiveness of this
process may determine the effectiveness of the new
strategies developed.

After an individual has decided upon a task strategy,
the next step in the development of the strategy is to
perform the task and respond to task performance feedback by
either continuing to use the same strategy or by altering
the current strategy to make it more effective.

In this model of strategy development, there are
several points at which an individual must make a strategy
relevant decision. First, an individual must decide whether
a stored plan is appropriate or which stored plan is most

appropriate for effective task performance. If a stored

15

plan is judged to be inappropriate, then an individual must
develop a new plan. Further, the acceptability of the plan
in use is judged during task activity according to the task
feedback received. At any of these decision points, errors
can be made that will affect task performance. These
decisions and their error variance may be affected by task
goal attributes (Wood 8 Locke, 1990).

Lord and Hanges (1987) describe three alternative
methods by which individuals make strategy relevant
decisions. These are 1) rational choice model, 2)
routinized choice processes, and 3) action first choice
processes. The rational choice model involves an analytic
approach to developing strategies. Using rational choice,
an individual will take care to develop effective, well
thought out strategies early on in the task. Routinized
choice involves sequential testing of alternative
strategies, without much effort given to choosing at once
the best way to approach the task. Action first choice
incorporates trial and error, with only a limited search for
effective strategy alternatives.

All three of the methods described by Lord and Hanges
(1987) operate in a control systems framework. Therefore,
the model chosen by the task doer is dependent upon the
frequency of the feedback loop available. When feedback is
slow, the modification of a strategy is also slow and
therefore the cost of strategy errors is high. When a task

is familiar (at least something is known about expected

16

outcomes) and also has slow feedback, a rational choice
model will be used because it is important for initial
strategy decisions to be correct. On the other hand, when
feedback is fast, and errors can be quickly corrected,
changes in strategy are less costly and a task doer will
employ routinized choice. When a task is unfamiliar and the
feedback loop is fast, individuals will employ action first
choice processes so they can learn about the task by trying
many strategies and receiving much feedback.

Locke et al.'s (1981), Wood and Locke's (1990), and
Lord and Hanges' (1987) conceptualizations of strategy
development are consistent. They are all dependent upon
goals as the initiators of strategy development. Further,
Wood and Locke's (1990) and Lord and Hanges' (1987) models
describe goals as the regulators of behavior. They are the
standards upon which strategy effectiveness is judged.
Also, all three models point to the role of cognitive
abilities in strategy development. Locke et al. (1981)
describes the performance effects of goals through strategy
development as indirect, because of the factor that
cognitive processes play in the formulation of strategies.
Wood and Locke's (1990) necessitates a consideration of
cognitive abilities because of the many judgments that a
task doer faces with respect to strategy appropriateness and
because of the task doer's reliance upon cognitive storage
and retrieval of information regarding task strategies.

Lord and Hanges' (1987) model of strategy decisions involves

l7

cognitive abilities in the same sense that Wood and Locke's
model does, especially in the rational choice process.

While theory regarding strategy development issues has
reflected the role of goal setting and cognitive processes,
empirical research thus far has primarily focused on the
direct relationship between difficult, specific goals and
strategy development and on the relationships among
difficult, specific goals, strategy, and performance. The

following section will discuss empirical research this area.

 

the individual level, several empirical studies have
examined the effects of goal setting on task strategy and of
the relationship of these constructs to task performance.
Earley et al. (1987) recently found results in a laboratory
study that showed a positive relationship between goal
specificity and the use of relevant task strategies.
Included in the authors' hypotheses were predictions that
(1) individuals who were assigned a specific goal would plan
more than individuals given a ‘do best' goals and that (2)
planning would be a mediator in the relationship between
goal setting and performance. Subjects participated in a
business simulation where they had to choose a medium in
which to advertise a fictitious product and write statements
in support of their choice of media. Subjects in the
assigned condition were told to produce four arguments
justifying a choice of medium for thirty-five products.

Planning was assessed by post-task questions that addressed

18
the number of steps in a plan, the amount of time spent
planning, and the importance of planning to the individual.
The results of the experiment supported the first
hypothesis, but did not demonstrate that planning mediated
the relationship between goal setting and performance. Goal
setting predicted performance even after the variance due to
planning and information had been removed. The authors also
found a main effect for goals on performance and on the
amount of effort individual put towards task accomplishment.
There was also a positive relationship between strategy
development and performance and between task effort and
performance.

In this experiment, Earley et al. (1987) also
manipulated the amount of information individuals received
with respect to the product markets and advertising media of
the products. The authors hypothesized that being given
task relevant information is much like receiving a more
specific goal in that it gives an individual a basis on
which to plan. Therefore, the authors hypothesized that
giving task relevant information would stimulate planning
and that increased planning would result in increased
performance. Results indicated that, as with specific
versus ‘do best' goals, subjects who received information
planned more, expended more energy on the task, had more
strategy development, and performed better than individuals
who did not receive information. There was no interaction

found between goal setting and planning.

19

In a field study by the same authors (Earley et al.,
1987) similar results were found. The purpose of the field
study was to examine the relationships of the laboratory
study described above in the work environment across a
number of jobs and tasks. The planning measure used by the
authors emphasized the length of plan (described as the
number of separate steps used in working on a project) and
the amount of time spent planning rather than aspects of
plan effectiveness or development. Results showed that the
presence of goals was positively correlated with the length
of plan as well as for performance and effort for a variety
of jobs (service, management, production, and clerical).
Also, the amount of training received by individuals was
positively related to the amount of planning, performance,
and effort. This relationship may be analogous to the
effect of providing task relevant information on planning in
the laboratory study. The authors suggest the cognitive
benefit of goals is to get an individual to think about both
the task and how to perform it. The authors note that
asking subjects to list the number of steps in a task in a
questionnaire may be confounded by the ability of the
participant to express himself or herself.

In a later field study, Earley et a1. (1990) found that
quality of task strategy did affect the relationship of
goals to performance. The authors surveyed 347 employees of
varying job types (e.g., production, clerical) and of three

levels of job tenure. The survey reported whether

20

individuals had goals, the difficulty and specificity of
their goals, the complexity of their jobs, and the presence
of task strategies. The results of this field study
indicated that job experience affects the relationships
among goal setting, strategy, and performance.
Specifically, the authors concluded that, on a task with a
number of independent acts and a variety of information
cues, while goal setting stimulated strategy development,
job experience was necessary for an individual to be able to
judge the effectiveness of the strategy. Other results of
the study illustrated that specific, difficult goals led to
more effective work strategies than general goals and this
strategy effectiveness resulted in better performance.

Earley, Connoly and Ekegren (1989) found that on a
stock market prediction task specific and difficult goals
increased the amount of strategy search activities by
individuals, but resulted in decreased performance. The
authors attribute this decrease in performance to the
ineffectiveness of strategy search in a situation where
there are so many possible strategies that not all of them
can be tested and evaluated. Subjects in one experiment had
either a difficult, specific goal or a ‘do your best' goal.
In a second study, the authors gave subjects either a
specific-easy, specific-moderate, specific-difficult,
tapering-specific (decreasing with number of predictions),
or a ‘do your best' goal. Similar results were found in

study 2. Additionally, it appeared that the differences in

21
performance occurred in part as a result of the specific
goal subjects shifting strategies more frequently in the
early task trials than the general goal subjects.

Similar results were found in a third study. In this
study, subjects in the difficult goal condition performed
worse than ‘do your best' subjects because they shifted
strategies too frequently. The authors conclude that in
addressing goal setting and strategy research more attention
should be paid to task characteristics and individual
characteristics that distinguish boundary conditions for the
helpfulness of goal setting.

A recent study by Earley, Lee and Lituchy (1989)
considered the strategy development issue from a control
systems perspective. In this laboratory study the authors
hypothesized that compared to having a ‘do your best' goal,
having a specific, difficult goal or having a learning goal
would cause individuals to develop specific criteria by
which to judge the accuracy of a particular strategy. A
learning goal was one that advised subjects not to focus on
the outcome of the task, but on developing an effective
strategy. The hypotheses were supported.

Subjects in the specific, challenging condition and in
the learning condition developed specific criteria by which
to judge their strategies, spent more time developing and
changing their strategies, and had better performance. Time
spent developing strategies and amount of strategy change

mediated the relationship between goal and strategy quality.

22

Further, subjects who received task training engaged in less
strategy change throughout the task compared to subjects who
received no training. Subjects who had a specific,
challenging goal, but who did not receive training had
difficulty finding a strategy that satisfied their criteria.

At the group level, Smith, Locke and Barry (in press)
addressed strategy issues on an organizational simulation.
The authors varied goal specificity and difficulty and time
spent planning and assessed the quality of the planning
process, and performance. The authors had two goal
conditions: a ‘do best' goal and a specific, challenging
goal. Planning quality was assessed with a questionnaire
that evaluated communication, integration, degree of
comprehensive and systematic analysis, and degree to which
resources were considered. Results of the study indicated
that specific and challenging goals were positively
correlated with performance in all but the first game
session (there were six sessions in all). The highest
correlations occurred in sessions four, five, and six.
Further, specific and difficult goals were positively
related to planning quality, and time spent planning was
positively related to performance when the quality of the
planning process was high. Formal planning time did not
improve performance when planning quality was low.

Although the groups that were not required to spend

time planning were not prevented from planning, the authors

23
did not hypothesize a relationship between goals and time
spent planning.

Overall, the strategy/goal setting literature arrives
at two main conclusions: (1) specific and difficult goals
on complex tasks direct attention to a task and thereby
stimulate strategy development (most often measured by time
spent developing strategies and the detail of those
strategies) and (2) factors such as task relevant
information, training, and task experience may enhance the
benefit of goal setting On performance through strategy.

The first conclusion cited above is consistent with
Locke et al.'s (1981) discussion of strategy. It is well
accepted that on simple tasks specific, difficult goals
increase individuals' desire to perform well. While on
simple tasks this desire is directly transferred to task
performance through increased effort, it appears that on
complex tasks, the desire to perform well functions to
increase strategy development.

This relationship, however, does not always result in
improved performance and may in fact result in decreased
performance (e.g., Earley, Connoly 8 Ekegren, 1989: Earley
and Perry, 1987). The link to performance is achieved
through the effectiveness or quality of the strategy used.
Individuals who spend much time developing ineffective
strategies will not benefit with respect to performance.
The key to good performance is not the time spent developing

strategies or the detail of those strategies, but the

24
quality of them. The second conclusion cited above
indicates the importance of variables that mediate the
relationship between strategy development and strategy
effectiveness. Neither strategy development or specific,
difficult goals themselves lead to strategy quality. The
relationship of strategy development to strategy quality is
dependent upon influences such as information, experience,
or training (e.g., Earley et al., 1987: Earley, Connoly 8
Ekegren, 1989: Earley, Lee 8 Lituchy, 1989).

In summary, the goal setting research points to a model
of goals leading to strategy development, which in turn lead
to high quality strategies in the presence of certain
moderating variables. While several moderating variables
have been researched, empirical research has not addressed
the potential of cognitive ability as a mediating variable
in the relationship between strategy development and
strategy quality. In the absence of experience, training,
or information, cognitive ability may enable individuals to
develop effective strategies. The following section will
discuss cognitive ability and its role in the relationship
between strategy development and strategy effectiveness.
MW

Qggnitiyg_apility_pgjingg. Many constructs exist that
describe cognitive processes. Those that address individual
capacity to process multiple pieces of information and to
make judgments regarding the applicability of information to

relevant situations are relevant to the study of task

25

strategy development. Individuals who are able to
comprehend and process more pieces of information may be
able to generate a greater number of strategies than
individuals who are less able to do so. Further,
individuals who have greater cognitive abilities may be
better able to judge the applicability of those strategies
to tasks. The cognitive abilities relevant to strategy
development occur at three levels. These are (1) general
cognitive abilities, (2) specific cognitive abilities, and
(3) task specific skills.

ggpgxal_gggni§iyg_gbility. General cognitive ability
(in this thesis, the term general cognitive ability will be
used interchangeably with general intelligence) was
described as early as the nineteenth century by Francis
Galton, was empirically examined in the early twentieth
century by Charles Spearman, and is still often analyzed and
debated in many aspects (Jensen, 1986). Humphreys (1985)
described three categories of definitions of general
intelligence. These are traditional, cognitive processes,
and factor-analytic. Traditional definitions of general
intelligence describe it as an innate ability or a potential
for learning. Such an interpretation is characterized by
the use of intelligence tests to describe ability level and
by the use of descriptors of intelligence, such as
adaptability, innovativeness, or ability to deal with
symbols. A cognitive processes view of intelligence is a

more recent perspective on intelligence. Research in this

26
area assesses intelligence through laboratory experiments
that tap the cognitive operations of activities such as
problem-solving and attention.

The factor-analytic approach to intelligence is based
on Spearman's work. According to this approach general
intelligence (labeled 9) is the broadest factor in a
hierarchy of factors that emerge from tests of intelligence
(Humphreys, 1985). Factors on the hierarchy below this are
less general and are termed primary and secondary (and so
on) factors or group factors. Examples of group factors are
verbal, spatial, and numerical abilities (Jensen, 1986).

Although the 9 factor describes intelligence, it does
not completely define it. According to Spearman (1927), it
can not be concluded that g measures intelligence, only that
it says something about it. Spearman felt that while
scientists could measure g, they could not pin its
relationship to the specific cognitive and neurological
workings of the brain.

According to Jensen (1986), this is still the case when
g is discussed solely in terms of psychometrics. A
cognitive process approach to understanding intelligence,
however, studies the relationships among cognitive processes
such as attention, memory, and information processing.
Jensen (1986) notes that a close look at the relationship
between 9 loadings on various tests indicates that the size
of the g loading seems to reflect the amount of cognitive

processing necessary to arrive at a correct solution. Other

27
empirical work supports similar relationships (e.g., Vernon,
1983: Ackerman, 1986).

§pggijig_gggnitiyg_Abili§ig§. The study of specific
cognitive abilities originated in the 1930's with L. L.
Thurstone who became interested in how many and what kinds
of factors accounted for g (Nunnally, 1978). Nunnally
describes several main ability factors. Examples of these
are 1) verbal factors, 2) numerical ability, and 3)
reasoning factors.

Verbal factors generally include verbal comprehension
and verbal fluency. Verbal comprehension is often measured
by a vocabulary test. Verbal fluency focuses on the ability
to quickly produce words or sentences. It is often measured
by asking individuals to list synonyms or to list words that
fall into some type of category (Nunnally, 1978).

Numerical facility centers on the rapidity and accuracy
with which individuals can solve arithmetic problems.
Reasoning factors fall into three main categories. The
first, general reasoning, involves the ability to develop
solutions to problems (often mathematical reasoning
problems). Deductive reasoning concerns the ability to draw
conclusions based on given information. The third category
of reasoning, according to Nunnally (1978) is seeing
relationships and involves an ability consider a
relationship between to ideas and use it to understand a

relationship between other ideas. Verbal or picture

28
analogies are often used to assess this category (Nunnally,
1978).

I§§k_§pggifiig_§kill§. Task specific skills are those
skills that enable a person to effectively perform a
specific task but that are not necessarily generalizable to
other tasks. Literature on differences between experts and
novices is relevant in understanding task specific skills.
Experts (those high on task specific skills and performance
on those tasks) have more task specific knowledge than
novices and differ in their cognitive organization of that
knowledge (de Jong 8 Ferguson-Hessler, 1986). Experts
represent information differently in their long term
(Larkin, McDermott, Simon, 8 Simon, 1980) and short term
memories (Chase 8 Simon, 1973) than do novices. Expertise
is characterized by three cognitive components. These are
(1) chunking, (2) hierarchical cognitive organization, and
(3) problem-type-centered cognitive structures.

Chunks are units of information differentiated by some
rule or pattern from other chunks (Best, 1986). Experts are
able to recall both more and larger chunks (Chase 8 Simon,
1973). IFor example, master chess players are able to
reconstruct chess board set ups from memory better than
novices because they are able to organize chunks on a chess
board in a meaningful way so that more chunks can be stored
and recalled (Chase 8 Simon, 1973).

Problem-type centered cognitive structures enable a

problem solver to identify an appropriate procedure by which

29
to solve a problem. These cognitive structures are called
problem schemata and describe a particular problem or set of
problems. A specific and appropriate schema contains the
necessary knowledge and procedure to solve the problem
easily and quickly. A more general scheme provides only a
general prescription. If an appropriate schema is not
available, then a more general schema is activated. An
expert will have specific problem schema available so that
he or she will have and be able to access the knowledge with
which to solve a problem (Chi 8 Glaser, 1985).

Hierarchical problem organization is present in experts
and is characterized by having relevant knowledge arranged
hierarchically with respect to detail. Higher levels are
characterized by general laws and definitions which become
more detailed at lower levels (Reif 8 Heller, 1982). For
example, physics experts are able to sort cards with physics
problems written on them into a few unifying or
superordinate principles. Novices can not perceive these
unifying principles (Chi, Glaser, 8 Rees, 1982).

EIIJ°I l E J E !!'

Both Locke (1968) and Locke et al. (1981) asserted that
task skill was a necessary condition for goal setting to
work. If an individual does not have the ability with which
to perform the task, he or she will not be able to achieve a
goal. Usually, research has treated ability with a task
pre-test or a measure of self-efficacy, assessing perceived

or real task skills. Wood et al. (1987) recognized that

30
there is a differentiation between goal effects for simple
versus complex tasks and that this differentiation may be
based on cognitive abilities. Wood et al. suggested that in
the future researchers examine the role of cognitive
abilities and goal setting on strategy development.

One promising area with which to begin assessing the
role of cognitive abilities and goal setting on strategy
development is with research linking general cognitive
abilities and information processing.

For example, studies have linked cognitive abilities
with attention and speed of information processing. In one
study, Ackerman (1986) found that g was more highly
correlated with tasks that require controlled cognitive
processing than those that require automatic processing.
Controlled processing is required when there are no
consistent rules or consistent sequences of information-
processing components involved in the task. Automatic
processing, on the other hand, is fast and cognitively
effortless. Automatic processes permit other cognitive
processes to be carried on simultaneously. Tasks that
facilitate automatic processing are those with invariant
rules and invariant components and sequences components of
information-processing. Automatic processing is achieved
through extensive practice (Ackerman, 1986).

Ackerman used a consistent mapping task (CM) to induce
automatic processing and a varied mapping task (VM) to

induce controlled processing. An example of a consistent

31
mapping task is one in which a subject is asked to identify
a target letter from a group of other letters (distractors).
The target letter is one of a group of letters that does not
change for any trial. Over time, subjects will identify
target letters with increased speed. The task becomes
automatized to the extent that the subject can perform other
tasks simultaneously with no performance decrement. On a
similar task using a varied mapping task, subjects would
also identify a target letter among distractors, but the set
of memory items (the potential targets) would change for
each trial. In this case, performance only improves
slightly with early practice and the task can not become
automatic (Ackerman, 1986).

Ackerman employed several CM and VM tasks as well as
several measures of intelligence (which were factor analyzed
to assess g). The results of his study showed that g and
the specific cognitive ability test (e.g., verbal test)
associated with a specific task (e.g., a verbal task)
accounted for a significantly larger percent of the
performance variance for the VM tasks than for the CM
tasks. General cognitive ability accounted for most of this
variance (as compared to the primary factor, such as verbal
ability). The association between g and VM performance
continued for hundreds of trials. In the CM condition the
relationship between performance and g attenuated over time.
For the CM task, the high ability subjects showed relatively

little performance improvement over time. On the other

32
hand, the low ability subjects showed substantial
improvement over trials. However, although the high ability
subjects were in effect learning less than the low ability
subjects, they still maintained relative performance
superiority.

Vernon (1983) also addressed the relationship of
general cognitive ability and information processing.

Vernon proposed that individual differences in intelligence
may be partially due to differences in the speed and
efficiency with which individuals can perform basic
components of information-processing that are related to
memory encoding, short term memory (STM) processing, and
long term memory (LTM) retrieval.

Using several tasks involving these processes Vernon
found that the speed with which individuals can perform
these various cognitive tasks was significantly and highly
related to their intelligence (as measured by the Wechsler
Adult Intelligence Scale). Further, Vernon proposed that
individuals with a broader knowledge base and strategy base
have acquired these over time as a result of faster and more
efficient information-processing capabilities.

The relationship between intelligence and information-
processing supports a link between intelligence and strategy
development on complex tasks if information processing is
considered an integral part of strategy development for such
tasks. According to Wood (1986), task complexity is defined

by acts, products, and information cues. Task complexity

33
increases as the number of acts, products, and information
cues increases as well as when the number and dynamics of
relationships among these components increases. In order to
develop effective strategies, individuals must be able to
make sense of all the components of a task. Clearly, on a
complex cognitive task, individuals must use controlled
cognitive processes to achieve this. Further, such
processes have a strong positive relationship with
intelligence and this relationship is maintained over time
and practice (Ackerman, 1986).

Also, the speed at which individuals can carry out
basic information processing components, such as memory
encoding, short term memory (STM) processing, and long term
memory (LTM) retrieval may be reflected in the speed at
which they are able to process task information cues and
relationships between cues, products, and actions. This
ability may enable individuals with high intelligence to
develop strategies faster and more effectively. Further,
high cognitive ability individuals may have a broader base
of stored strategies from which to chose and develop new
strategies.

While the relationship between cognitive information
processing and intelligence may point to a connection
between intelligence and strategy development, another way
to conceptualize the latter relationship may be developed
from research on validation of intelligence tests. A meta-

analysis by Hunter (1986) showed that general cognitive

34

ability predicts performance in all jobs, beyond the
relationship between job knowledge and general cognitive
ability. Hunter's data showed that the validity of this
relationship increased as a function of job complexity.
Cognitive ability also predicted training success. However,
the validity of this relationship did not vary
systematically across job complexity (Hunter, 1986). The
fact that the predictive validity of general intelligence
increased as a function of job complexity for job
performance but not for training success may indicate that
the relationship between job complexity and intelligence is
a long term, rather than a short term, relationship.

Gutenberg, Arvey, Osburn, and Jeanneret (1983) also
found that tests of cognitive ability are most valid for
complex jobs (i.e., jobs that require high levels of
information processing and decision making). Wood et al.
(1986) pointed out that this relationship is converse of the
relationship between goal setting and task complexity, where
goal setting is least effective for complex tasks and most
effective for simple tasks. Campbell (1988) noted that
cognitive search skills are more important on complex tasks
because of the non-routine nature of such tasks. The
relationship of cognitive ability to task activity is also
most relevant on complex tasks because several alternative
behaviors aimed at task accomplishment are possible and
choices must be made among them. Therefore, one primary

difference between complex and simple tasks lies in the

35
potential necessity of strategy development on complex
tasks. Key to the development of effective strategies are
the choices made among possible strategies. The quality of
such choices may stem from an individual's cognitive
ability.

According to social learning theory individuals with
high cognitive abilities will be better able to exercise the
forethought necessary to see future consequences (i.e.,
goals) as current regulators of behavior (Bandura 8 Cervone,
1983). This combined with Wood and Locke's (1990) model of
strategy development suggests that individuals high on
cognitive ability will be better able to make effective
strategy relevant decisions. These decisions include
whether a stored plan is appropriate or if a new plan needs
to be developed and if a new plan, once developed and
possibly implemented, is acceptable. Therefore, it follows
that research take a closer look at the relationship between
cognitive processes and strategy development.

Task specific skills, unlike cognitive processes, are
directed only at a specific task or set of tasks and
therefore may have effects on strategy development that are
different from those of general or specific cognitive
abilities. Increased knowledge along with chunking,
hierarchically arranged cognitive structures, and problem
specific cognitive schema may enable expert performers to
bypass some if not all of the strategy development that

novices must proceed to.

36
92921351211;

Cognitive ability research points to a need to look at
this important construct as it relates to strategy
development. Cognitive ability is related to information
processing and as such may have an important positive effect
on strategy effectiveness. While goal difficulty and
specificity act to increase effort towards task
accomplishment, on a complex task an individual with low
cognitive ability, either general or specific, will not be
able to harness that effort into constructive task activity.

However, there are two conditions that may negate the
need for strategy development. Expert/novice research and
theory indicates that expert task doers may not require
strategy development. A second condition that preempts
strategy development is task simplicity. Strategy
development is not required on simple tasks because
strategies are not necessary for task completion or are so
obvious as not to require thought.

These two conditions, task expertise and task
complexity, will be held constant in this study. The task
will be complex and no participants in the study will be
experts.

SBEEAI!

In summary, the strategy/goal setting research looks at
the relationship between goal specificity and difficulty and
length (in terms of time and detail) of strategy, relevance

of strategy, and strategy search. The results of empirical

37

research show that increased difficulty and specificity are
positively correlated with these dimensions of_strategy in
some situations. Some studies have shown that information,
experience and training affect goal setting effects. Also,
although it has been suggested that cognitive processes may
influence the relationship between strategy and performance,
empirical research has not to date addressed this issue.

This experiment will re-examine the relationship of
goal specificity and difficulty to strategy development.
The hypotheses will address the role of cognitive processes
as a moderator in this relationship. The following section
proposes specific hypotheses addressing these issues.

W

The main thrust of this study is to integrate the
effects of specific, difficult goals and ability on a
complex task. This study will assess how goals affect
performance via the moderating effect of ability on the
relationship between goal setting and performance. Specific
hypotheses are discussed below.
.190 ,-—'_ ° 9- 0v- 1 1ti
Cognigivg Ability on Eezfigrmance

Hypothesis la proposes that individuals with specific,
difficult goals will perform better than individuals with
general or less difficult goals. Specifically, individuals
with difficult, specific (SD) goals will perform better than
individuals with easy to moderate, specific (EM) goals or

general, ‘do best' (DB) goals. This is consistent with the

38

Wood et. al. (1986) meta-analysis. Further, hypothesis 1b
proposes that this positive relationship between goal
specificity and difficulty and performance will be moderated
by cognitive ability on a complex task. Specifically, the
positive relationship between SD goals and performance will
be greater for high cognitive ability subjects as compared
to low ability subjects. Figures 2 and 3 illustrate these
relationships.

t ' ° 0 ' a ' 'v

Hypothesis 2 is concerned with the relationship between
the effort that individuals put towards developing
strategies and the type of goal that they have. On a
complex task, the focus is on cognitive effort, as opposed
to physical effort, as may be involved with a simple task,
such as a psychomotor exercise. Specifically, hypothesis 2
proposes that as goal difficulty and specificity increase,
the cognitive effort that individuals put toward strategy
development will also increase. This is consistent with
studies by Earley et al. (1987) which found that individuals
with more specific, difficult goals spent more time
developing strategies, had strategies of more detail, and
found strategy development to be more important to task
activity than individuals with ‘do best' goals. This is
also consistent with Earley, Connoly and Ekegren (1989) who
found that goal specificity and difficulty led to greater
strategy search. The mechanism through which this occurs is

based on theory and research that shows that difficult,

 

39

 

Goal
Difficulty
and
Specificity

 

9 Performance

 

 

 

 

 

 

 

 

 

Figure 2. Expected relationships among ability, goals, and
performance.

 

high
High 9
Performance
Low 9
how

 

I I I

DB EM SD
Goal Type

Figure 3. Expected interaction between ability and goals on
performance.

40

specific goals increase the effort that individuals apply
toward task activity (Locke et al., 1981). It is proposed
here that on a complex task, performers will be inclined to
focus this increased effort on the development of strategies
for task accomplishment.
,‘ee ,;:~ ' .A‘,'-., 7, ; 1 e, e e- ,1 1o 21'.

’v . . t . 0'

Individuals may choose one or several different
strategies out of many possible strategies available to
perform a task. These strategies may be of different
qualities, indicating that some will result in better
performance than others. Hypothesis 3a proposes a main
effect of goal setting on choice of strategy type such that
the difficulty and specificity of the goal that an
individual is striving for will affect the type of strategy
that he or she will choose. This is indirectly supported by
work by Smith, Locke and Barry (in press) and by Earley et
al. (1990). Further, hypothesis 3b proposes that this
relationship will be moderated by cognitive ability.
Specifically, the relationship between goal specificity and
difficulty and strategy choice will be different for
individuals high on cognitive ability. Recalling that
different strategies may be associated with different levels
of quality (i.e., high quality defined here as associated
with high performance) this cognitive ability effect
parallels the effect of training found by Earley, Lee and

Lituchy (1989) and the effects of experience found by Earley

41
et al. (1990) and is supported by work on information
processing and cognitive ability by Ackerman (1986) and
Vernon (1983). These relationships are illustrated in
Figures 4 and 5.
4° t ' e r c

Hypothesis 4 deals with the process through which
individuals choose the strategies that they will use. More
specifically, hypothesis 4a proposes that the cognitive
effort that individuals put towards strategy development
will determine the type of strategy that they choose.
Further, hypothesis 4b proposes that this relationship will
be moderated by cognitive ability. Individuals may put much
effort into strategy development, but on a complex task,
where they have little or no experience, individuals will be
able to develop high quality strategies only if they are

high on cognitive ability. This is illustrated by Figures 6

and 7.
root e is ° _. or, at M-o'. - - f-.. 'ois_'- :e w--

Hypothesis 5 proposes that hypothesis 4 is a mediator
of hypothesis 3 such that if the relationship in hypothesis
4 is controlled for, the effect of hypothesis 3 will go to
zero. This would indicate that without the effects of
strategy development effort, goals will not influence
strategy choice. This is indirectly supported by work by
Earley, Lee and Lituchy (1989) who assessed time spent

developing strategies and amount of strategy change and

42

 

 

 

 

Goal
Difficulty Strategy
and ' TypeC0)*
Specificity

 

 

 

 

 

* ‘Q' indicates a level of strategy quality

Figure 4. Expected relationships among goals, ability, and
strategy type.

 

 

 

high
High g
Strategy
TypeCO)
Low 9
low
DB EIvi so

Goal Type

Figure 5. Expected interaction between ability and goals on
strategy type.

43

 

 

 

 

 

 

 

 

 

Strategy
Deve I opment w Strategy
Effort : TypeCO)

Figure 6. Expected relationships among strategy development
effort, ability, and strategy type.

 

 

 

high
High g
Strategy
TypeCOD
Low g
low
low high

Strategy Development Effort

Figure 7. Expected interaction between strategy development
effort and ability on strategy type.

44

found that these variables mediated the relationship of
goals to the quality of strategies that individuals chose.
Figure 8 shows this relationship.
.19- 9: '1 -: 9: mu‘!.e : I- equ':.o ~ -. ': oqL.; :
Hypothesis 6a proposes that the type of strategy that
individuals choose will have a main effect on performance.
Some strategies will be of higher quality than others and
will lead to better performance. Hypothesis 6b proposes
that there will also be a positive main effect of cognitive
ability on performance. Individuals who are higher on
cognitive ability will perform better due to their better
information processing skills and knowledge.
.1.” 9- ' 7' -. . — 0o. yo... ; r: 1: -_ ~9‘9, . =: "1
Goals_and_£erfermence

Hypothesis 7 proposes that hypothesis 6a and hypothesis
4 mediate the relationships stated in hypothesis 1 such that
the effects of goals on performance will not be seen if
- strategy type and effort towards strategy development are
controlled. This emphasizes the role of strategy choice and
strategy development effort on the relationship between
goals and performance. This relationship is shown in Figure
9.

' ' s e u 't

Since different types of strategies will lead to
different levels of performance, the issue of the quality of
strategy becomes relevant when discussing strategy choice.

In this study, strategy quality is defined by the effect of

 

Goal
Specificity
and
Difficulty

 

45

 

V7

 

 

 

 

Strategy

Development

Effort

 

 

 

 

Strategy
TypeCOD

 

 

Figure 8. Expected mediating effect of strategy development
effort on the relationship between goals and strategy type.

 

Goal
Specificity

 

 

‘7

 

and
Difficulty

 

 

 

 

Strategy
Development
Effort

 

Strategy
TypeCOJ

 

 

 

Performance

 

 

 

 

 

 

 

Figure 9. The expected mediating effect of strategy type
and strategy development effort on the relationship between
goals and performance.

46

the strategy on performance. A high quality strategy, while
not the only predictor of performance, will account for
alarge amount of the variance in performance compared to a
low quality strategy. Hypothesis 8a proposes that specific
and difficult goals, compared to easy to moderate, or
general goals will lead to higher quality strategies. This
is consistent with work by Smith, Locke, and Barry (in
press), Earley, Lee, and Lituchy (1989), and Earley et al.
(1990). Further, hypothesis 8b proposes that cognitive
ability will moderate the relationship described in
hypothesis 8a. The relationship between goal specificity
and difficulty will be greater for individuals high on
cognitive ability. This reiterates hypothesis 3b, but
focuses more directly on the quality outcome of the choice
of strategy. These relationships are shown in Figures 10
and 11.

Appendix A presents a summary of the hypotheses.
Combining hypotheses 1 through 8 develops a model of goal

setting for complex tasks that is pictured in Figure 12.

 

47

 

 

 

 

 

 

 

 

Goal
Specificity Strategy
and E9 Oual ity
Difficulty T

Figure 10. Expected relationships among goals, ability, and
strategy quality. .

 

 

 

high
High g
Strategy
Quality
Low g
low
D'B EIxA SD

Goal Type

Figure 11. Expected interaction between ability and goals
on strategy type.

48

 

 

 

 

 

 

 

    

 

 

 

Goal Strategy
Difficulty Strategy
Performance
and F=4> Development .===¢> Typecoj .==¢>
. . . Effort
SpeCITICIty

 

 

 

 

 

 

Figure 12. Model of goal setting and strategy on a complex
task.

METHOD
521113.212:

Subjects were 253 undergraduate psychology students.
Subjects received extra course credit for participating in
this study.

1.8.5.15

The task consisted of sets of crossword puzzlesz. Each
set contained 18 puzzles of varying levels of difficulty.
There were six easy, six medium, and six difficult puzzles
in each set. All subjects received identical sets of
puzzles. Subjects had thirty minutes to work on the
puzzles, in any order they chose. Subjects used different
colored pens to work on the puzzles so that whether or not
they did the puzzles in the order given them could be
objectively assessed. They used a blue pen for the first
ten minutes of task time, a red pen for the second ten
minutes, and a green pen for the third ten minutes.

Goals were set in terms of total points to be attained.
Points were earned by filling in correct answers. A word on
an easy puzzle was worth 1 point. A word on a medium puzzle
was worth 2 points. A word on a difficult puzzle was worth

3 points.

49

50
mien

The independent variables were goal difficulty and
specificity and cognitive ability. There were two levels of
goal difficulty and two of goal specificity. These were a
‘do your best' (DB) goal, a low to moderate and specific
(EM) goal, and a difficult, specific (SD) goal. The goals
for each level of difficulty were determined in a pilot
study. Thirty-four undergraduate psychology students
participated in the pilot study. They were given the same
set of puzzles that was used in this study. In the pilot
study subjects were told to do their best on the puzzles.
The mean performance of the group was 97 points with a
standard deviation of 59.96. The goals assigned in this
study were as follows. A vague goal was worded as "do your
best to attain as many points as you can". The easy to
moderate and specific goal was 70 points. The difficult and
specific goal was 160 points. Therefore, the difficult goal
is approximately at the 84th percentile and the easy to
moderate goal is approximately at the 3lst percentile of
performance on the pilot study.

Dependent variables were the effort put towards
developing strategies, type of strategy, quality of
strategies, and performance.

BIQQEQEIQ

Participants were given a brief and very general

description of the study. Next they were asked to read and

sign a consent form, indicating that they voluntarily agreed

51
to participate and that they were aware that they could
leave the experiment at any time without loss of credit and
that their performance and survey data would be kept
anonymous (see Appendix B).

After consent forms were collected students were given
the cognitive ability test. This took approximately 30-40
minutes to complete. Next, participants were given a brief
questionnaire that assessed crossword puzzle experience and
affinity (see Appendix C).

Subjects were then introduced to the task. They were
given a packet of puzzles with brief written instructions
that included their goal. They were told not to look at the
puzzles until they heard the instructions. After subjects
read the instructions they were repeated orally by the
experimenter. Questions regarding instructions were
answered. Subjects were told to take out their puzzles.
After this, the experimenter told the subjects that they
needed to wait two minutes before they could begin writing
answers. This period gave students time to begin planning,
if they chose to do so.

At the end of the task time (30 minutes) students were
given a survey to assess the effort they put towards
strategy development, the strategies they used, and a
manipulation check of goal difficulty and specificity.

After subjects completed these questionnaires they were

given a debriefing form describing the study. When they

52
finished reading this and listened to a five minute oral
debriefing they were dismissed.
MQESEIQS
; I!°V 51.1.!

Selected parts of the Employee Aptitude Survey
developed by Grimsley, Ruch, Warren, and Ford were used.
They were the verbal comprehension, verbal reasoning, word
fluency, and symbolic reasoning sections.

The verbal comprehension test, according to Ruch and
Ruch (1980) was designed to "measure ability to use words in
oral and written communication and in planning". The test
consists of thirty items and takes five minutes to complete.
Essentially, the test involves identifying synonyms. This
test has an alternate forms reliability of 0.85. Ruch and
Ruch (1980) report that it correlated 0.85 with the verbal
subtest of the Primary Mental Abilities Test and 0.75 with
the verbal subtest of the Cooperative School and College
Ability Test. In a sample similar to the one to be used in
this study Whitener (1988) found a coefficient alpha
reliability of 0.75.

The verbal reasoning test assesses an ability to draw
conclusions from given statements. It has thirty items and
requires five minutes to complete. Ruch and Ruch (1980)
report that it has an alternate form reliability of 0.82.
Further, it has a correlation of 0.74 with the reasoning
subtest of the Primary Mental Abilities Test, correlations

of 0.51 and 0.53 with the verbal and quantitative subtests

53
of the Cooperative School and College Ability Tests, and a
correlation of 0.56 with the Otis Employment Test.

The word fluency test is a five minute test that
requires individuals to list as many words as possible
beginning with a given letter in five minutes. It is
standardized for the letters C, M, and S. It has an
alternate form reliability (i.e., using a different letter
as an alternate form) of 0.76. Ruch and Ruch (1980)
reported a correlation of 0.64 with the word fluency subtest
of the Primary Mental Abilities Tests. In this study the
letter C was used.

The symbolic reasoning test was designed to assess an
individual's ability to understand symbolic relationships.
It is also a five minute, thirty item test. It requires
test takers to draw conclusions based on given relationships
among symbols. According to Ruch and Ruch (1980) it has an
alternate forms reliability of 0.82. Further, it correlated
0.52 with the reasoning subtest of the Primary Mental
Abilities Test and 0.41 with the quantitative portion of the
Cooperative School and College Ability Tests.
MABiEQlAEiQD.§B§QE

A manipulation check questionnaire was used to
determine if subjects knew their goal and if they used the
goal given them or chose a goal of their own. Subjects were
asked what the goal assigned to them was, if they had a goal
of their own other than the assigned one, and if so, what

was the goal. Although these questions could be used to

54

eliminate students who had a specific numerical goal that
was different from the goal assigned to them, it is unclear
whether the students understood the questions. While some
students were able to state their goal (about 78 percent)
and whether they used it or chose another, many other
students responded by describing strategies, rather than
goals and other students responded in a very general way.
For example, some responses to the item "What was the goal
assigned to you?" included answers such as "word
orientation", "to get extra-credit points", and "to complete
certain tasks in a given amount of time". When asked if
they had a goal other than the assigned goal, subjects often
responded with strategies, such as "get as many of the
‘hard' answers as possible", "to do as many of the ‘easy'
words as possible", and "to finish one whole puzzle".
However, since the goal was repeated twice by the
experimenter and was presented in writing to each student,
it seems reasonable to assume that the goal condition was
effectively manipulated.
Effert_T2sard§_§tratsax_nexe122ment

Effort was determined by a post task questionnaire.
Participants were given a questionnaire assessing the time
they spent developing their strategies, the amount of
concentration that went into strategy development, and other
relevant items. Items 3 through 7 were adapted from Earley

et al. (1987).

55

W

In order to determine what strategies were used
students answered a survey describing their strategies. A
list of potential strategies was developed from a pilot
study of 34 students (see appendix F). Participants rated
the extent to which they used various possible strategies.
The strategies are not, for the most part, mutually
exclusive. Therefore, subjects may have used varying
combinations of strategies. The list of strategies was
factor analyzed to determine underlying, general strategies.
Additionally, the colored pens used by the students as well
as a count of the actual numbers of the puzzles worked on
provided an objective measure of whether subjects did the
puzzles in the order that they were in the packet. Since
students used different colored pens at different times, it
was possible to determine if subjects did not do the puzzles
in the order that they were originally in the packet.
Another objective measure of strategy was the percent of
easy, medium, and hard puzzles that the students worked on.
The total number of each type of puzzle worked on was
counted. From this count, the percent of each type of
puzzle worked on could be calculated.
W

In this study, a high quality strategy is one that is
associated with a high level of performance. Therefore, the
quality of a strategy was determined by the frequency with

which high performance was associated with that strategy.

56
Multiple regression, with performance as the dependent
variable and strategy use (i.e., used exclusively, did not
use at all, etc.) as the independent variable was used to
describe strategy quality.
bushels

The analyses were done on a sample size of 214. This
excluded individuals who were classified as experts.

Experts were those who reported having worked on seven or
more crossword puzzles in the last month. This was
approximately two standard deviations above the mean for
this item. This sample also excluded eight subjects who
reported having a numerical goal that was different from the
one assigned to them. These eight students were excluded on
the basis of their response to two manipulation check items.
Specifically, these subjects responded to the item "What was
the goal assigned to you?" with a numerical goal that was
different from the assigned goal or responded with a
specific numerical goal when asked what, if any, goal they
set that was different from the assigned goal. Also
excluded were eighteen other subjects who, for various
reasons, could not complete the study (e.g., left for part
of the study).

Hierarchical multiple regression was used to test the
hypotheses. Goal specificity and difficulty were dummy
coded such that the SD goal condition was the reference
variable. Analysis of the specific hypotheses is discussed

below.

57

Hypothesis 1a proposed that individuals with SD goals
would perform better than individuals with general or less
difficult goals. Specifically, it was asserted that
individuals with SD goals would perform better than
individuals with EM or DB goals. Further, hypothesis 1b
proposed that this positive relationship between SD goals
and performance would be moderated by cognitive ability on a
complex task. Specifically, it was hypothesized that the
positive relationship between SD goals and performance would
be greater for high cognitive ability subjects as compared
to low ability subjects. These hypotheses were tested with
multiple regression, entering the dummy coded goal
conditions first, cognitive ability second, and the
interactions between cognitive ability and goal conditions
third.

Hypothesis 2 proposed that SD goals would also be
associated with the cognitive effort that individuals put
toward strategy development. In this analysis, effort
toward strategy development was the dependent variable and
the goal conditions were entered in the first step.

Hypothesis 3a proposed a main effect of goal setting on
choice of strategy type such that the difficulty and
specificity of the goals that individuals were striving for
would affect the type of strategy that they chose. Further,
hypothesis 3b proposed that this relationship would be
moderated by cognitive ability. In this equation, strategy

type, as determined by the factor analysis of the strategy

58
questionnaire, was the dependent variable and goals,
cognitive ability, and the interaction between goals and
cognitive ability were entered in the first, second, and
third steps respectively.

Hypothesis 4a proposed that the cognitive effort that
individuals put towards strategy development would determine
the type of strategy that they chose. Further, hypothesis
4b proposed that this relationship would be moderated by
cognitive ability. For this analysis, strategy type was the
dependent variable. Strategy development effort was entered
in the first step, cognitive ability in the second, and the
interaction between effort and ability in the third.

Hypothesis 5 proposed that hypothesis 4 would be a
mediator of hypothesis 3 such that if the relationship in
hypothesis 4 was controlled for, the effect of hypothesis 3
will go to zero. This would indicate that if the effects of
strategy development effort are controlled for, the
relationship between goals and strategy type will not be
seen. For this analysis, strategy type was the dependent
variable. Strategy development effort was entered in the
first step, cognitive ability in the second, strategy
development effort by ability interaction in the third,
goals in the fourth, and the goal by ability interactions in
the fifth step.

Hypothesis 6 proposed that the type of strategy that
individuals chose would have a main effect on performance.

Also, a positive main effect for g was proposed. For this

59
analysis, performance was the dependent variable and
strategy type and g were entered in the first step and
second steps respectively.

Hypothesis 7 proposed that hypothesis 6a and hypothesis
4 would mediate the relationships stated in hypothesis 1
such that the effects of goals on performance would not be
seen if strategy type and effort towards strategy
development are controlled. In this analysis, performance
was the dependent variable strategy development effort was
entered in the first step, cognitive ability in the second,
strategy development effort by cognitive ability interaction
in the third, strategy type in the fourth, goal conditions
in the fifth, and goal by cognitive ability interactions in
the fifth step.

Hypothesis 8a proposed that specific and difficult
goals, compared to easy to moderate, or general goals would
lead to higher quality strategies. Further, hypothesis 8b
proposed that cognitive ability would moderate the
relationship described in hypothesis 8a. The analysis for
hypotheses used strategy quality as the dependent variable.
Strategy quality was computed from a regression equation
that entered score as the dependent variable and strategy
type as the independent variable. For hypothesis 8, goals
were entered in the first step, cognitive ability in the
second step, and cognitive ability and goal interactions in

the third step.

60

Further analyses included a path analysis of the model
of strategy and goal setting on complex tasks.
Additionally, exploratory analyses were done assessing the
effects of specific cognitive abilities on performance and
strategy. The cognitive ability tests fall into two
categories. These are verbal abilities (verbal
comprehension and word fluency) and analytic abilities
(verbal and symbolic reasoning). Hierarchical multiple
regression, with either strategy type or score as the
dependent variable and verbal and analytic ability entering
in separate steps were done to assess the possibility that
verbal ability and analytic ability work in different ways
to influence performance. Given the level of complexity and
the verbal nature of this task, one might expect that verbal
ability will have a positive main effect on performance,
while analytic ability will be more highly related to
strategy quality. Individuals high on verbal ability will
have the cognitive resources to perform well on the task by
filling in many words. Subjects with strong analytic skills
will be able to use their reasoning skills to develop

effective strategies.

RESULTS

The results are presented in the order of the
experimental hypotheses. First, descriptive statistics of
the measures are presented. Second, results of regression
analyses of the hypotheses are described. Third an overall
path model is shown. Finally, exploratory analyses
describing the relationships of analytic and verbal
abilities to the dependent variables are presented.

Descripgive Stagistics

Table 1 presents the means, standard deviations, and
reliabilities of the main variables of the study. Table 2
displays intercorrelations among the variables. All results
are based on a sample size of 214 which included all
subjects who were not crossword puzzle experts.
W

The factor analysis of the strategy items was done
using principle factors with varimax rotation of factors
with eigen values greater than 1. This resulted in the
development of one six item scale that assessed the extent
to which subjects worked on easy and medium as opposed to
hard puzzles. The items included in this scale are numbers
2, 3, 6, 7, 8 and 9. The reliability of this scale is 0.75

(Cronbach's alpha).

61

 

 

Variable Mean S.D. Reliability
Score (points) 103.68 47.40
Strategy‘
Easy/Medium
Puzzles 1.80 .54 .75
Scanning 1.86 .71
g (general cognitive
ability)2 .oo 2.43 .79
Verbal ability3 .00 1.45 .58
Analytical ability‘ .oo 1.62 .78
Efforts 13.99 3.81

 

1
of use.

Based on a 3 point scale with 3 indicating a high degree

Based on the sum of the standardized scores of the four

ability tests.

Based on the sum of the standardized scores of the word
fluency and verbal comprehension tests.
Based on the sum of the standardized scores of the verbal
reasoning and symbolic reasoning tests.
Based on the sum of seven 5 point items, with 5 indicating

high effort.

63

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64
21.1.! I !

Reliabilities for the symbolic reasoning test and the
verbal reasoning test were computed for the first half of
the test and then adjusted using the Spearman-Brown formula
for correction of test length. This was necessary since
very few participants were able to finish these tests.
Reliability for general cognitive ability (the sum of the
four tests) was computed using only the verbal
comprehension, verbal reasoning, and symbolic reasoning
tests since the word fluency score is based only on a number
of words generated. For the same reason, reliability of the
verbal ability measure (the sum of verbal comprehension and
word fluency) is the same as the reliability of the verbal
comprehension test. Although standard scores were used for
the analyses, reliabilities for g and for analytical ability
were computed using raw scores. These reliabilities were
calculated using Nunnally's (1978) formula for reliability
of linear combinations.

WW2

The objective measure of whether subjects did the
puzzles in the order that they were given in the packet was
correlated with the self-report of this strategy.
Specifically, the objective measure of whether students did
puzzles in the order that they were in the packet was
assessed in two ways. First by considering the numbers of
the puzzles worked on (i.e., with respect to their position

in the packet) with the order of color used (red, then blue,

65

then green). For example, if a student worked on puzzles 1,
2, and 3 and answered puzzle 1 in green, it is obvious that
the puzzles were not done in order. Also, it could be
assessed whether the puzzles were done in order by whether
the numbers of the puzzles worked on were consecutively
ordered. The correlation between the objective measure and
the strategy item "I did the puzzles in the order that they
were given" was 0.50 (p < 0.01), indicating that this self-
report was reliable. Further, a comparison of an objective
count of the number of easy and medium puzzles done with the
subjective measure of this strategy indicated that the
subjective measure is construct valid. Specifically, the
correlation of the objective percentage puzzles that were
worked on that were easy and medium with the strategy score
for the Easy/Medium scale is 0.65 (p < 0.01).

. The flypothesgs

v 'o 1 tin n ' 'v
We:

The first hypothesis addressed the relationships among
performance, general cognitive ability, and goals.
Hypothesis la posited that individuals with SD goals would
perform better than individuals with D8 or EM goals.
Further, hypothesis 1b proposed that this positive
relationship between goal specificity and difficulty and
performance would be moderated by cognitive ability on a
complex task. Specifically, the positive relationship

between SD goals and performance would be greater for high

66
cognitive ability subjects as compared to low ability
subjects. Table 3 shows the multiple regression results of
the analysis of these relationships.

As can be seen from Table 3, there was no main effect
for goals on performance and there was no interaction
between goals and general cognitive ability. There was only
a strong main effect of g on performance. Therefore,
hypothesis 1 was rejected.

Hypothesis 2 proposed that as SD goals would lead to
higher cognitive effort toward strategy development than DB
or EM goals. Table 4 shows the results of the regression
for this hypothesis. There was no significant main effect
for goals on strategy development effort. Therefore,
hypothesis 2 was rejected.

OVf . Q - . '., . 0., -t '1- a 1 ..,j ° - L'. on
5! l . :1 .

Hypothesis 3a proposed a main effect of goal setting on
choice of strategy type such that SD goals, as compared to
EN or DB goals would affect the type of strategy chosen.
Further, hypothesis 3b proposed that this relationship would
be moderated by cognitive ability. The results of this
regression are shown in Table 5.

There was a significant main effect for goals on the
use of the Easy/Medium puzzle strategy. This was an
ineffective strategy, and subjects with SD goals were less

likely to use it. Specifically, this goal effect is

Step

 

Variables
entered in
the equation

F R2 F of
change change

 

goals

Dummy 1

(EM vs. SD)
&

Dummy 2

(DB vs. SD)

9

ggalg X g
Dummy l X g
&
Dummy 2 X g

.01

.26

.26

1.12

24.90*** .25 71.73***

14.94*** .00 .26

 

***
*‘k
‘k

p < 0.01
p < 0.05
p < 0.10

Step

68

Table 4

Variables R2
entered in
the equation

R2 F of
change change

 

£2215
Dummy 1
(EM vs. SD)
&
Dummy 2
(DB vs. SD) .01

 

*** p < 0.01
** p < 0.05
* p < 0.10

69

 

Table 5
a-o,-~ST-1 .4. '_ 5,9 ,gtzos .510‘ w- -eiuy
e V 't'v ' ’t
Q2als_Entering_as_nain_and_ln:eragtign_szest§
Step Variables thot R2 F of
entered in change change
the equation
1 Egélé-
Dummy 1
(EM vs. SD)
&
Dummy 2
(DB vs. SD) .03 3.43**
2 g .03 2.38* .00 .31
3 93.921.21.91
Dummy l X g
&
Dummy 2 x g .03 1.48 .00 .86

 

*** p < 0.01
** p < 0.05
‘* p < 0.10

7O

accounted for by the DB versus SD condition. Therefore,
subjects with DB goals were more likely to use this
ineffective strategy than were subjects with SD goals.
There was no interaction effect between goals and g on this
dependent variable. Thus, this hypothesis was only
partially supported.
I] S! ! . :1 . E

Hypothesis 4a proposed that the cognitive effort that
individuals put towards strategy development would determine
the type of strategy that they chose. Further, hypothesis
4b proposed that this relationship would be moderated by
cognitive ability. The results of the analysis of these
hypotheses are presented in Table 6. There were no main or
interaction effects for either effort or g. The amount of
cognitive effort that subjects put into strategy development
did not determine the extent to which they used the
Easy/Medium puzzle strategy. This decision was also
‘unaffected by level of general cognitive ability.

fIherefore, hypothesis 4 was rejected.
c e e ' s ' twe d
Wham
Hypothesis 5 proposed that the effects of goals on
estrategy choice occur through the influence of effort.
Without the effects of effort, goals would not affect
€31:rategy choice. Since no effect of goals on effort or of
at fort on strategy type was demonstrated, testing of this

hyPothes is was obviated .

71

 

Table 6
e s 0 w e as ed u s
V ' te ev 0 nt
BEE ! i : i!° 21.1.! E ! .
t' e ts
Step Variables R2“,t F RZ F of
entered in change change
equation
1 Effort .00 .73
2 g .01 .82 .01 .34
3 Efijg;§_x_g .01 .57 .00 .06

 

*** p < 0.01
** p < 0.05
* p < 0.10

 

Hypothesis 6a proposed that the type of strategy that
individuals chose would have a main effect on performance.
Hypothesis 6b asserted that there would be a positive main
effect for g on performance. As can be seen from Table 7,
there was a main effect on performance for strategy as well
as for general cognitive ability. Table 7 also shows an
unanticipated interaction between strategy type and score.
This interaction is illustrated in Figure 13 and reveals
that the detrimental effect of working on easy and medium
puzzles as opposed to difficult puzzles is greater for high
ability subjects. High ability subjects have the most to
gain from working on difficult puzzles since they are the

ones most likely to get correct answers on these puzzles.

. r a .-gT. : ,- ;- 1 °-._1 9 :- --. o.,‘ {.2
W

Hypothesis 7 proposed that hypothesis 6a and hypothesis
«4 would mediate the relationships stated in hypothesis 1
such that the effects of goals on performance would not be
seen if strategy type and effort towards strategy
cievelopment were controlled. Since there was no effect of

gmoals on performance, or of effort on strategy type, this

hypothesis was not tested.
1E11e_2sLerminan§§_2f_§§ra§egx_nuali§1

Hypothesis 8a proposed that SD goals, compared to EM,
C>J=' DB goals would lead to higher quality strategies.

F’lllrther, hypothesis 8b proposed that cognitive ability would

 

Table 7
WW
Step Variables R"-mt F R2 F of
entered in change change
the equation
1
Easy and
Medium .06 14.45***
2 Q .31 46.97*** .25 74.48***
3 :21st
W .33 33.99*** .02 5.85**

 

*** p < 0.01
** p < 0.05
* p < 0.10

74

 

 

Score
150 F
130 —
High g
110 —
90 —
7O 1 1 l L l l I
1 1.2 1.4 1.5 1.8 2 2.2 2.4

Use of Easy/Medium Strategy

I?igure l3. Interaction between general cognitive ability
21nd strategy type on performance.

75
moderate the relationship described in hypothesis 8a. Since
only one strategy type was analyzed and since this type was
significantly correlated with performance, strategy type
was, in essence, redefined as strategy quality. Therefore,
this analysis was redundant with hypothesis 3 and is not
presented. From hypothesis 3, it can be seen that there is
a main effect for the DB goal condition on strategy quality.
. s' e v
Figure 14 illustrates the path model of the
relationships examined in this study. It can be seen from
the model that significant relationships exist between goals
and strategy type, between strategy type and performance,
and between general cognitive ability and performance.
Also, there is a significant moderating effect of g on the
relationship between strategy type and performance.
A modified path model, including only these four significant
relationships is shown in Figure 15. Since Easy/Medium is
an ineffective strategy, its effect on performance is
negative. It can be seen from the model that general
cognitive ability has the greatest effect on performance.
Verbal and Analytic Ability
Exploratory analysis of the effects of verbal and
analytic ability on performance and strategy did show that
verbal ability has a stronger effect on performance. It can
be seen from Table 8 that although both analytic and verbal
ability had significant main effects on performance, the

variance in score was primarily due to verbal ability. As

76

 

    
   

.04
.06
00 Best '
.10 Strategy .04 - -_22nxa
vs . Strategy
-—-‘-> Dave I 00mm. Performance
Difficult
Effort
Goals .

 

.1eta

 

Figure 14. Path model.

&)&5t

 

.18“ - 22:60.
vs. Strategy TypeCQ)
=================£> c================$> performance
Difficult (Easy/Medium) fl“
Goals
_.4g**
.99“*
9

Figure 15. Modified path model.

77

 

Table 8
W
W
I J!’E1'J'!E! . ".5“!

Step Variables Ram F R2 F of
entered in change change
the equation

1 Eerbal
Apiiigx .21 57.94***
2 Malaise
52111;; .27 38.48*** .06 16.73***

 

*** p < 0.01
** p < 0.05
* p < 0.10

78
shown in Table 9, neither verbal or analytic ability, like
general cognitive ability had a significant main effect on

the use of the Easy/Medium strategy.

 

Table 9
E i0 E J . '!l E! ! I
W
i V l J El'J'! 3 E J !i El']°! E ! .
W
Step Variables R2tot F R2 F of
entered in change change
the equation
1 ME
52111:! .00 -32
2 Verbal
gpiiigy .00 .44 .00 .56

 

*** p < 0.01
** p < 0.05
* p < 0.10

DISCUSSION

This section will discuss in detail the relationships
among goals, strategy, cognitive ability, and performance.
The first part will summarize and discuss the results
described above. Then, additional post hoc analyses that
further explore the model proposed here will be addressed.
Next, the post hoc analyses will be integrated with the
original results to develop conclusions consistent with both
sets of analyses. Finally, suggestions for future research

will be made.

no 0 EE 1!
Q!‘ 0 ‘.-. 1 ‘ -. ,,°! ._ ca ‘ ' 0 an: COA!
E!.J.! E E

The results of this study did not find a positive main
effect for goals on performance. This result is
inconsistent with the Wood et al. (1986) meta-analysis of
goal effects, which found that although positive goal
effects were less pronounced on complex tasks, they were
still present. However, other research has failed to find a
positive link between goals and performance on complex tasks
and has, in fact, reported a negative link. For example,
Earley, Connoly and Ekegren (1989) found that increased goal

difficulty resulted in performance decreases in three

80

81

studies that incorporated complex tasks. Similarly, Huber
(1985) found that subjects with difficult goals working on a
maze task had lower levels of performance than those with
easy goals. In these studies, the decreased performance was
attributed to ineffective strategy search or too much time
spent on information gathering. Earley, Connoly and Ekegren
(1989) describe four task conditions that may cause goals to
negatively affect performance. These occur when (1)
performance is more a function of task strategy than effort,
(2) there are many possible strategies, (3) the best
strategy is not obvious, and (4) there is little opportunity
for testing the effectiveness of various strategies. The
task incorporated in this study met all of these conditions.

Further, in this study, there was no interaction effect
between goals and general cognitive ability on performance,
indicating that there was no effect of goals on performance
for individuals at all levels of general intelligence. If
the absence of a main effect is attributed to the complexity
of the task, then the absence of a moderating effect of g
would indicate that the influence of complexity on goal
effects for this task are not surmountable by increased
intelligence.

This supports a complementary relationship between goal
setting and general cognitive ability proposed by Wood et
al. (1986). According to this proposition, there is a
complementary relationship between goals and cognitive

ability such that on tasks where goal setting works best,

82

general cognitive ability will have the least effect.
Conversely, where the g-performance relationship is
strongest, goal setting will have the least effect. Wood et
al. based this idea on research by Hunter (1986) and
Gutenberg et al. (1983) that examined the moderating effect
of task complexity on the relationship between performance
and general cognitive ability. This research found that
this moderating effect is in the opposite direction of the
moderating effect of task complexity on the relationship
between goals and task performance. In this study, which
focused on a complex task, there was a strong main effect of
g on performance, but no significant main effect of goals on
performance, thus illustrating one side of Wood et al.'s
complementary proposition.
Q2al_§etting_and_segnitixe_sz2rt

The second hypothesis dealt with the effects of goals
on the effort that individuals put toward developing
strategies. The results of this study demonstrated no main
effect of goals on the amount of effort that subjects put
forth. Theory and research by Looke et al. (1981), Earley,
Lee, and Lituchy (1989) and Earley et al. (1987) indicate
that a positive relationship could have been expected. The
absence of a positive relationship in this study may be
attributable to the nature of the goals. While people were
putting effort towards strategy development, this was not
the result of goals. Perhaps the goals were not of the type

that would stimulate effort towards strategy development on

83
this task. Specifically, they did not provide any
information relevant to strategy development.

A further explanation for failure to find support for
this hypothesis could be attributed to the two minute time
period that student's had prior to being permitted to fill
in answers. Since student's had nothing else to do during
this time period, they may have been able to do most or all
of their strategy development during this time. Their
incentive to plan may have been driven by boredom rather
than by goals and they may have engaged in strategy
development regardless of goal condition.

Ove,.lh 1 6,. °., -, o. - 'a- . d ..,' ve i3. ' on
a e ' I ' e

Hypothesis 3 addressed the relationships among ability,
goals, and strategy type. For the Easy/Medium strategy
there was a main effect of goals on strategy such that
subjects in the DB condition, as opposed to the SD condition
were more likely to work on easy and/or medium puzzles than
hard puzzles. This was an ineffective strategy. There was
no main effect of g on the use of this strategy and no
interaction effect between goals and g.

The former result is comparable to outcomes found by
Earley et al. (1990) and by Smith et al. (in press).

Earley, et al. (1990) found that specific, difficult goals
led to more effective work strategies than general goals and
this strategy effectiveness resulted in better performance.

Further, at the group level, Smith, Locke and Barry (in

84

press) found that specific and difficult goals were
positively related to planning quality.
WW

Hypothesis 4 proposed a main effect of effort on
strategy type and an interaction effect between effort and g
on strategy type. Neither of these effects were found for
the Easy/Medium strategy type. Subjects who put forth a
high level of effort towards developing strategies did not
use a more effective strategy than those who put forth
little effort. This outcome is similar to results found by
Smith et al. (in press) who found that there was no relation
between formal (i.e., assigned) planning time and planning
quality. Subjects in the Smith et al. study who were told
to plan did not necessarily develop effective plans.

Also it is possible that this is an obvious strategy.
In order to gain the most points it is beneficial to work on
puzzles that are worth more points. Therefore, the decision
to use this obvious strategy is not effort or intelligence
dependent.
T ' s o e a c

Hypothesis 6 asserted that both strategy type and
general cognitive ability would have main effects on
performance. Both these main effects were found.
Individuals who possessed higher levels of general
intelligence were able to perform better. This may be due
to their greater information processing skills and knowledge

base. Further, although both analytic ability and verbal

85
ability had main effects on score, this effect was much
stronger for verbal ability, indicating, as expected, the
verbal ability had its effect through increasing subjects'~
ability to answer questions.

Also, although it was not hypothesized, an interaction
was found between the Easy/Medium strategy and g on score.
While both high and low 9 individuals suffered decrements in
score as a result of increased use of this strategy, this
negative effect was more pronounced for high g individuals.
In retrospect, this is a logical result since it is the high
ability students who have the most to gain from working on
difficult puzzles.

't' a 5

One limitation of this study is that the use of factor
analysis to describe the strategies resulted in the
omission of many strategies that were employed by students.
Subjects used several strategies, but only one cohesive
factor, the Easy/Medium strategy, emerged. The remaining
eleven strategies may have had meaning with respect to the
hypotheses. These effects were not addressed, however,
since these strategies did not form any factors. Therefore,
all strategies were explored and additional analyses were
done to further investigate the hypotheses.

The seventeen possible strategies fell into two
categories of strategy. These were ‘between puzzle'
strategies and ‘within puzzle' strategies. The first twelve

strategies were between puzzle strategies and reflected ways

86
in which students determined which puzzles to work on. The
last five strategies were within puzzle strategies and
represented ways in which students could work on a puzzle
once that puzzle had been chosen. Therefore, individuals
would necessarily use one or some of both types of
strategies.

Nine out of the seventeen strategies had significant
bivariate correlations (p < .05) with performance, but only
three contributed unique variance on score. These were
strategies 2, 9, and 17. Strategies 2 and 9 were between
puzzle strategies and were included in the Easy/Medium
strategy scale. Strategy 17 was a within puzzle strategy
("I answered the questions I knew first, then the ones I
thought I knew, then I went back to fill in whatever was
left") and was not included in the scale that resulted from
the factor analysis of strategies. This strategy was an
ineffective strategy, as indicated by a significant negative
correlation with performance. This may have been an
ineffective strategy because individuals using it may have
spent too much time scanning the clues to find obvious
answers and not enough time working toward solutions. In
fact, this strategy correlated -0.24 (p < 0.01) with the
total number of puzzles worked on. This approach also fails
to maximize points by working off already filled in words.
Further, this strategy parallels dysfunctional strategies
incorporated by subjects with test anxiety. For example,

Sarason (1978) found that individuals with test anxiety

87
spent too much time looking for a minority of obvious
solutions and not enough time working towards solutions of
non-obvious items.

In order to more thoroughly understand the
relationships among goals, strategy, cognitive ability, and
performance by exploring these relationships on a ‘within
puzzle' strategy, additional analyses were done using
strategy 17 (Scanning). The analyses that were done using
the Easy/Medium scale were repeated using the Scanning
strategy. The results are discussed below.
0v- a a te a '01 . --, - 7 . 1!! ..,j 'v- l:'_' .
§££é£§gi£_§ngi£§

Hypothesis 3 proposed a main effect of goals on
strategy type and that this relationship would be moderated
by general cognitive ability. The results of this analysis,
using Scanning as the dependent variable are shown in Table
10. Although there was no main effect for goals, there was
a significant main effect for general cognitive ability and
a significant interaction between goals and ability. This
latter effect primarily results from the interaction between
the second dummy variable, which contrasted the DB condition
with the SD condition, and g. The interaction is shown in
Figure 16. Although individuals with DB goals used Scanning
(an ineffective strategy) to a similar degree, individuals
in the SD goal condition who were of low cognitive ability
used this strategy to an especially high degree while

individuals of high ability in the SD condition utilized

88

Table 10

 

Step Variables 122tot
entered in
the equation

R2 F of
change change

 

1 Eels
Dummy 1
(EM vs. SD)
&
Dummy 2
(DB vs. SD) .00

2 g .03

3 mm
Dummy 1 X g
&
Dummy 2 x g .05

.03 4.96**

.02 3.23**

 

*** p < 0.01
** p < 0.05
* p < 0.10

89

Use of Scanning Strategy

2,0 Low 9

High g

 

 

DB SD
Goal Type

Figure 16. Interaction between general cognitive ability
and goals on the use of the Scanning strategy.

90
this strategy significantly less. This is supportive of an
interactive effect of ability which was not supported with
the Easy/Medium strategy.

This moderating effect parallels training and
experience effects found by Earley, Lee and Lituchy (1989)
and by Earley et a1. (1990). Earley, Lee and Lituchy (1989)
found that subjects who received task training engaged in
less strategy change throughout the task compared to
subjects who received no training. Subjects who had a
specific, challenging goal, but who did not receive training
had difficulty finding a strategy that satisfied their
criteria. Similarly, in a field study, Earley et al. (1990)
found that job experience affected the relationships among
goal setting, strategy, and performance. The authors
concluded that, on a task with a number of independent acts
and a variety of information cues, while goal setting
stimulated strategy development, job experience was
necessary for an individual to be able to judge the
effectiveness of the strategy.

In this study general cognitive ability may have
functioned in the same way as training and job experience
did in the Earley et al. studies. Although subjects in this
study did not receive training and did not have task
experience, high ability subjects may have been able to draw
conclusions about the task that gave them a performance

advantage not experienced by the lower ability subjects.

91

This interactive effect is also supported by work on
information processing and cognitive ability by Ackerman
(1986) and Vernon (1983). The results of a study by
Ackerman (1986) showed that on a task where controlled
information processing was required, g accounted for a
percent of the performance variance. This association
between g and performance continued for hundreds of trials.
Vernon (1983) also addressed the relationship of general
cognitive ability and information processing. Vernon found
that the speed with which individuals can perform various
cognitive tasks was highly related to their intelligence.
Further, Vernon proposed that individuals with a broader
knowledge base and strategy base have acquired these over
time as a result of faster and more efficient information-
processing capabilities.

All individuals working on a complex task may engage in
strategy search. However, only those individuals with high
intelligence will be able to differentiate between high and
low quality strategies.

T t . ' ' es

Hypothesis 4 proposed a main effect for effort towards
strategy development and that this relationship would be
moderated by cognitive ability. The results of this
analysis with the Scanning strategy as the dependent
variable are shown in Table 11. Although there were no main
or interaction effects for either effort or g for the

Easy/Medium strategy, there was both a main effect for g and

92

Table 11

 

Step Variables R2mt F R2 F of
entered in change change
the equation

I Efifgrt .00 .20
2 g .02 2.44* .02 4.68**
3 Ejj91§_x_g .04 2.76 .02 3.35*

 

*** p < 0.01
** p < 0.05
* p < 0.10

93
an interaction effect between g and effort for Scanning.
Subjects who put forth a relatively high level of effort
towards strategy development who were high on g were less
likely to use the Scanning strategy than those high ability
subjects who contributed relatively little effort. However,
subjects low on g who put forth a high level of effort used
this strategy to a greater degree than low ability subjects
who did not expend much effort. This interaction effect is
shown in Figure 17.

This effect has some similarity to results found in a
recent study by Earley and Perry (1987). In their study,
Earley and Perry found that subjects with specific goals who
were primed with plans engaged in more planning activities
than those who were not primed. The effect of strategy on
performance however, was a function of the applicability of
the primed plan. The priming could cause a negative effect
on performance if the primed plan was ineffective. In the
present study, subjects low on g who engaged in a large
amount of strategy development, like the subjects in Earley
and Perry's study, increased their use of an obvious
strategy, even though it was ineffective.

This moderating effect supports the assertion that on a
complex task, increased effort alone does not result in the
use of high quality strategies. In order for individuals to
benefit from their increased effort, they must possess a

level of general or analytic cognitive ability that will

94

Use of Scanning Strategy

 

 

2.9 ~
2.7 _
Low g
2.5 e
2.3 —
2.1 —
1.9 _
Ffigh g
1 7 — +—‘--——‘-fi——fi~777‘““***——--~____-___‘+
1 S l l l l L l l l l l
9 10 11 12 13 14 15 16 17 18

Strategy Development Effort

Figure 17. Interaction between general cognitive ability
and strategy development effort on the use of the Scanning
strategy.

95

permit them to differentiate between effective and
ineffective strategies.

- 1- 4-2'. - !‘ ;-_. Ton 139 =- w-e ., s and
S te 'c

Hypothesis 5 proposed that effort would mediate the
relationship between goals and strategy choice. The results
of this analysis are shown in Table 12. There was no
mediating effect of effort on the interaction effect between
goals and ability on the use of the Scanning strategy.
Therefore, hypothesis 5 was rejected for this strategy.
h e ' e e ' e fo ance

Hypothesis 6a proposed that the type of strategy people
choose would affect performance. As can be seen from Table
13, individuals who used Scanning to a large degree
performed worse than subjects who used it to a lesser
degree. There was no interaction between g and Scanning.

. s ,1 ,-.', - - tel-,5- 959 =‘tW“! o.-s .12
er ce

Hypothesis 7 proposed a mediating effect of effort and
strategy type on the relationship between goals and
performance. Since the latter relationship was not found,
this hypothesis was not tested.
The_Deferminan§§_gf_§:ratsgx_flsali£x

Hypothesis 8 proposed that SD goals would lead to the
use of higher quality strategies than EM or DB goals. For
this analysis, strategy quality was the proportion of

variance in performance due to the use of both Scanning and

Step

Variables
entered in

the equation

96

Table 12

R2 F of
change change

 

Effect

9
EIIQI§_X_Q
m1;
Dummy 1

(EM vs. SD)
Dummy 2

(DB vs. SD)
Dummy l X g

Dummy 2 X g

.00

.02

.04

.04

.07

.20

2.44*

2.76**

1.92*

2.37**

.02 4.68**

.02 3.35*

.00 .66

.03 3.40**

 

*** p < 0.01
p < 0.05
p < 0.10

**
'k

97

 

Table 13
E s' E J . '!1 E E [i o 1
.‘ g- 0 0e. ! - -e --!e t a. 5‘0 1, ,0
WWW
Step Variables R2",t F R2 F of

entered in change change
the equation

1 §£I§£§Q¥
Scanning .03 5.69**

2 g .27 38.39*** .24 69.26***

3 W
x_g .27 25.48*** .00 .00

 

*** p < 0.01
** p < 0.05
* p < 0.10

98
the Easy/Medium strategy. ‘As Table 14 shows, there was a
significant main effect for goals which was largely due to
the DB versus SD condition. Subjects in the DB condition
did not use high quality strategies to the extent that
subjects in the SD condition did. There were no main or
interaction effects for general cognitive ability.

The effects of verbal and analytic ability on
performance and strategy were also examined with respect to
Scanning. It was expected that analytic ability would have
its effect on strategy development while verbal ability
would more directly influence performance. When Scanning
was entered as the dependent variable and analytic and
verbal ability entered as main effects it was shown that
there were significant positive main effects for analytic
but not for verbal ability. Subjects high on analytic
ability were less likely to utilize the Scanning strategy
than those high on analytic ability. These results are
shown in Table 15.

When Scanning was entered as the dependent variable and
goals and analytic ability or verbal ability entered as main
and interaction effects, it was shown that there was a
significant interaction effect between analytic ability and
goals, but between verbal ability and goals. These results
are shown in Tables 16 and 17. This interaction effect is
of the same nature as that of the interaction between
general cognitive ability and goals (as shown in Figure 16).

This interaction is shown in Figure 18.

99

Table 14

 

Step

Variables R2tot
entered in
the equation

R2 F of
change change

 

§Q§l§

Dummy 1

(EM vs. SD)
&

Dummy 2

(DB vs. SD) .02

g .03

Seals_x_g
Dummy l X g
&
Dummy 2 X g .03

1.84**

1.93

.01

.00 .30

 

*** p < 0.01
** p < 0.05
* p < 0.10

100

Table 15

 

 

cts
Step Variables R2tot F R2 F of

entered in change change
the equation

1 5111111119
Ability .03 7.74***

2 221231
Abilitx .03 3.87** .00 .04

 

*** p < 0.01
** p < 0.05
* p < 0.10

101

Table 16

 

Step

Variables R?
entered in
the equation

F R2 F of

change change

 

§Q§l§
Dummy 1
(EM vs. SD)
&
Dummy 2
(DB vs. SD) .00

£211.31! .04

Analytic .09

2.99 .04 8.39***

3.9*** .05 5.19***

 

*** p < 0.01
** p < 0.05
* p < 0.10

102

Table 17

 

Step Variables R?
entered in
the equation

R? F of
change change

 

1 goals
Dummy 1
(EM vs. SD)
&
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Dummy 1 X
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*** p < 0.01
** p < 0.05
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103

Use of Scanning Strategy

  

 

 

2.1 —
Low Analytic
Ability
2 _
1.9 —
1.8 —
High Analytic
Ability
1.7 P
1.6 1 1
DB SD
Goal Type

Figure 18. Interaction effect between analytic ability and
goals on the use of the Scanning strategy.

104

Additionally, although verbal ability had no
interaction effect with effort on the use of Scanning,
analytic ability, like 9, interacted with effort to
influence strategy use. Tables 18 and 19 show these
results. While subjects with low analytic ability increased
their use of Scanning with increased strategy development
effort, subjects low on analytic ability increased their use
of this ineffective strategy with increased effort. This
relationship is shown in Figure 19.

Table 20 summarizes the results of this study. The
first column of the table indicates which hypothesis is
being addressed. The second column briefly describes the
proposed relationship. The next four columns deal with
hypotheses for which specific strategies were not addressed.
In these columns a ‘Y' indicates that the relationship was
found. A ‘N' indicates it was not found. The first of
these four columns focus on hypotheses for which ability was
not involved. The last three of these columns address
hypotheses for which general cognitive ability, analytic
ability, or verbal ability, respectively, were of interest.
The remainder of the columns indicate results for hypotheses
which focused on a particular strategy, either Easy/Medium
or Scanning.

e ' ost o s s

The results of this study were mixed. While some of

the hypotheses were supported, others were not. Also, in

many cases, the effects sizes were not as large as may have

105

 

Table 18
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*** p < 0.01
** p < 0.05
* p < 0.10

106

 

Table 19
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t ' ' ' e 5
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2 29:91].
Abiliry .00 .22 .00 .25
3 mm
W -00 ~23 .00 -24

 

*** p < 0.01
** p < 0.05
* p < 0.10

107

Use of Scanning Strategy

 
   

 

 

2.3 -
Low Analytic
Ability
2.1 —
1.9 ~
High Analytic
Ability
1.7 —
1.5 L l l 1 L l l J l l
9 10 11 12 13 14 15 16 17 18 19

Strategy Development Effort

Figure 19. Interaction effect between analytic ability an
strategy development effort on the use of the Scanning
strategy.

108

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110
been expected. Further, the results with respect to the
Easy/Medium strategy and Scanning were not always
consistent. The sections below will attempt to summarize
and explain the inconsistencies between the strategies.

Hypothesis 3 addressed the relationships among ability,
goals, and strategy type. For the Easy/Medium strategy
there was a main effect of goals on strategy such that
subjects in the DB condition, as opposed to the SD condition
were more likely to work on easy and/or medium puzzles than
hard puzzles. This was an ineffective strategy. There was
no main effect of g on the use of this strategy and no
interaction effect between goals and g. In contrast, this
result was not found with Scanning. For Scanning, however,
there was a significant main effect for g as well as an
interaction between goals and g on strategy type.

Hypothesis 4 proposed a main effect of effort on
strategy type and an interaction effect between effort and g
on strategy type. Although the former effect was not found
for either strategy type, the latter effect was present for
Scanning.

The difference in results between these two strategies
may be due to the diverse nature of the strategies. The
Easy/Medium strategy addressed ‘between puzzle' plans, while
Scanning was a ‘within puzzle' strategy. Choosing the
former type of strategy may be more likely to be a precursor
to task activity while the latter strategy may be developed

during task activity (or once the former decision has been

111
made). In this sense, since individuals must choose which
puzzle to work on before they think about how to work on it,
Easy/Medium may be considered a primary strategy. It is
possible that goals have a stronger effect on strategic
activities that occur first while cognitive ability plays a
greater role on strategies developed later. Primary
strategies are not subject to trial and error learning to
the extent that secondary strategies are because they are
developed prior to task activity. Therefore, cognitive
ability is not as relevant on these types of strategies as
it is on secondary strategies which may involve a greater
amount of learning. Additionally, it may have been obvious
to all subjects that in order to maximize points it would be
beneficial to avoid easy puzzles and focus on difficult
puzzles. The deleterious effects of using the Scanning
strategy may not have been so obvious. Cognitive ability
may have less of an effect on obvious, as opposed to
nonobvious strategies.
Limirariogs of rhis Study

Several limitations of this study are worth noting.
With the exception of the main effect of g on performance,
the effect sizes were smaller than may have been expected.
Further, several hypothesized relationships were not found.

It is possible that the goal manipulation was not
completely effective. The manipulation check questions that
assessed whether subjects knew or used the goal that was

assigned to them was interpreted broadly by students and was

112
not useful as a manipulation check. Students in all goal
conditions may have been trying to do their best rather than
striving for their assigned goal. This may have occurred
for two reasons. First, students were not instructed to
stop when they achieved their goal. Therefore, the specific
goals may have operated as ‘do best' goals. Second, the
students were not required to monitor their performance.
While it would have been possible for them to get some idea
of their performance by counting the number of words they
completed and computing their points, it is unlikely that
they did this because of the time limits on the task.
Therefore, this lack of feedback may have influenced
students to work as hard as they could, without actually
knowing their progress with respect to their goal.

In addition to affecting the effectiveness of the goal
itself, this lack of feedback may have affected the strategy
development activities of the students. According to Lord
and Hanges (1987), strategy development operates in a
control systems framework. The way in which an individual
develops strategies is dependent upon the amount of feedback
received and the familiarity of the task. Since feedback on
this task was slow, testing of alternate strategies would be
limited. This may have limited the positive effects of high
ability by restricting the opportunity for high g

individuals to learn.

113
o o 5

To date, research linking goals to performance on a
complex task has found inconsistent results. Although this
study found no direct relationship between goals and
performance on a complex task, both detrimental (e.g.,
Earley, Connoly & Ekegren, 1989) and beneficial (Wood et
al., 1986) effects have been reported. This lack of
congruity points to a need for research to address this
issue more completely. Issues that need to be further
explored include aspects of the task, types of ability, and
characteristics of the strategy and the strategy development
process.

It is generally accepted that strategy issues influence
the effect of goals on performance on a complex task.
Future research should continue to address the specific
aspects of strategy that affect this relationship so that
some of the inconsistencies of existing research can be
resolved. These aspects fall into two categories. The
first, strategy development, focuses on the processes by
which individuals formulate and choose strategies. Wood and
Locke (1990) and Lord and Hanges (1987) have developed
models of strategy development that can be used to assess or
manipulate the way in which individuals develop strategies.
Other aspects of strategy development, such as amount of
strategy change, time spent on generating and refining
strategies, and effort towards strategy development can also

be used to better understand the goal-performance

114
relationship. While this study did not find a link between
goals and one aspect of the strategy development process
(i.e., effort), other studies have found a relationship
(e.g., Earley, Lee & Lituchy, 1989; Earley et al., 1987).
Therefore, future research should address the circumstances
under which this relationship will occur as well as other
aspects of the strategy development process (such as the
type of process used).

A second important issue in the area of strategy is the
strategy itself. Research has addressed characteristics of
strategy such as quality and detail. This study was able to
draw a link between goals and strategy quality, but not
between the strategy development process and strategy
quality. Therefore, in addition to continuing to address
linkages between aspects of strategy development such as
time and effort and strategy type or quality, future
research would benefit by exploring other aspects of the
strategy development process, such as the type of process
used and its relationship to strategy type.

An additional issue that could be addressed by research
is the differentiation between primary and secondary
strategies and obvious and nonobvious strategies and the
varying effects that goals, effort, and 9 have upon these
different types of strategy. Although this study was able
to provide some post hoc explanations for differences in the

effects for 9 upon these different strategies, more thorough

115
investigation of these dissimilarities may shed light on the
role of g on strategy development.

Research should also more thoroughly address other
issues of ability as they relate to strategy and goal
setting. The primary focus of this study was general
cognitive ability, although the separate contributions of
facets of ability on performance and strategy were also
touched upon. Additional research is needed to better
understand the role general cognitive ability as well as of
facets of cognitive ability on the relationships among
goals, strategy, and performance. Also, future research may
find that some of the relationships that hold true for
general cognitive ability (such as the moderating effect of
g on the relationship between goals and strategy choice) may
be negated by task specific skills.

Also, if the role of goal setting on complex tasks is
to be thoroughly understood, it may be helpful to design
studies that vary task complexity and make direct
comparisons of goal effects among various levels of
complexity.

W

This study provided limited support for a model of goal
setting on complex tasks that describes the influence of
goals on performance as occurring through their effects on
strategy. In some cases, subjects with difficult, as

opposed to ‘do best' goals were more likely to incorporate a

116
strategy that positively affected performance. Further,
this relationship may be moderated by cognitive ability.

Other results of this study linked effort and general
cognitive ability to choice of strategy such that
individuals with high ability decreased their use of an
ineffective strategy with increased effort while low ability
subjects increase the use of an ineffective strategy with
increased effort.

This study also compared the relationships between
verbal ability and performance and strategy and analytic
ability and performance and strategy. It was found that
verbal ability had a stronger effect on performance, while
analytic ability had a stronger effect on strategy for one
strategy type.

This research takes a step towards defining the process
-through which goal setting affects performance. It extends
current research by incorporating the role of cognitive
ability in a goal setting model for complex tasks. Further,
this study assesses the function of strategy in the goal
process.

Increased incorporation of technology in organizations
is driving up the level of complexity of many tasks.
Therefore, it is imperative that researchers address
motivational issues on complex tasks. Research needs to
more thoroughly assess the role of cognitive ability and
strategy development on complex tasks so that the beneficial
aspects of goals so frequently found on simple tasks can be

extended to complex tasks.

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Appendix A

Summary of Hypotheses

Hypothesis

H1

H2

l13

H4

H5

 

 

Goal
Difficulty

JJZS

Model

H18

 

 

and
Specificity

 

 

 

 

Goal
Difficulty

H10

17

 

Performance

 

 

 

and
Specificity

 

 

 

 

Goal
Difficulty

 

Effort Towards
Strategy Development

 

H30

 

 

and
Specificity

 

 

H3O

 

 

Effort Towards
Strategy Development

 

Strategy Type (0)

 

 

 

H

 

 

 

 

 

 

Goal

Difficulty
and ===>‘

Specificity

 

 

Effort Towards

Strategy l“

Deve I opment

 

 

 

 

Strategy Type (0)

 

 

 

Strategy Type (0)

 

 

 

 

 

 

Hypothesis

H6

H7

H8

126

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Model
H6a
Strategy Type (0) } pLPerformancej
H80
9 44 Performance l
Goal
Effort Towards
Diff icu l t Strategy
and y '—"° Strategy -9 Type [0) _4 Performance ]
Specificity Development
Goal
Difficulty H80 4 Strategy
and ' Quality
Specificity
H8!)

 

 

 

Appendix B

Consent Form

127
CONSENT FORM

For this research project you will be asked to work on a set
of crossword puzzles for 30 minutes. Before beginning work
on the puzzles you will be asked to take an ability test and
to answer several questions regarding your crossword puzzle
skills. After working on the puzzles, you will be asked to
provide answers to several questions about the task.

The experiment requires approximately an hour and a half to
two hours to complete and participation in the experiment is
voluntary. While your participation will provide you with
extra class credit, a decision not to participate will not
negatively affect your course grade. Additionally, there
are alternative options for achieving extra credit in your
class other than participation in the experiment. You also
have the right to discontinue your participation in the
experiment at any time for any reason. If you do not
believe you are willing to invest sincere effort in
achieving the goals of the experiment, please notify the
experimenter so that you may be dismissed. Otherwise, your
participation will contaminate the results of the study.

All results from your participation will be treated with
strict confidence. Within this restriction, the final
results of the experiment will be made available to you upon
written request.

You will also be fully debriefed at the conclusion of the
experiment. Any questions that you have regarding the
experiment will be answered at that time. If at any time
you have questions or concerns regarding this study or if
you wish to know the results of the study at a future date
please contact J. Shapiro through the graduate student
mailboxes in room 135 Snyder Hall (Psychology office).

I have read and understand the above statement. I will
consent to participate in this experiment without waiving my
right to discontinue my participation in the experiment at
any time without recrimination.

 

signature of student

 

print your name here

Appendix C

Experience With Crossword Puzzles

128

Please answer the following questions as accurately as you
can.

1. How much do you enjoy working on crossword puzzles?
(circle the number of the appropriate response)

1 2 3 4 5
like very like indifferent dislike dislike
much somewhat somewhat very much

2. How do you rate your ability to answer crossword puzzles
correctly?
(circle the number of the correct response)

1 2 3 4 5
outstanding above average below very
average average poor

3. In the last month, about how many crossword puzzles have
you worked on?

APPENDIX D

Manipulation Check on Goal Difficulty and Goal Specificity

129

Please answer the following questions as accurately as you
can.

1. What was the goal assigned to you?

2. Did you set any specific goals for this task, other than
the instructed goal? (circle the number of the appropriate

response)
1. yes

2. no

3. If the answer to question 2 was yes, what was the
goal(s) (e.g., " fill in 20 words", "fill in 10 to 20
words", "earn 30 points", or any others)?

4. If you had a goal, did you change it over time or did it
remain the same throughout the task? (circle the number of
the appropriate response or responses)

1. Changed over time
a. raised
b. lowered
c. raised and lowered

2. remained the same throughout the task.

5. How specific was your goal? In other words, to what
extent was it clear to you how many points you were supposed

to obtain?

It was very It was It was It was It was
ambiguous somewhat neither somewhat very clear
ambiguous clear or clear
ambiguous

6. How difficult was your goal?

1 2 3 4 5
It was very It was It was It was It was
easy somewhat moderate somewhat very

easy difficult difficult

APPENDIX E

Effort Towards Developing Strategies

130

The following items refer to the strategy, or method that
you used to work on the task that you just completed. A
strategy refers to the plan or steps that you followed to
perform your task. For example, if you had a task of
writing a term paper, one possible strategy may have been to
1) pick a topic, 2) go to the library and get books and
articles, 3) write an outline, 4) write the paper, and 5)
type the paper. If you had never written a paper before,
you may have had to spend time thinking up these steps and
deciding the order in which to do them. This process is
called strategy development. The following questionnaires
refer to the strategy and strategy development processes
that you used to attain your goal for the task that you just
completed. Please answer all questions as accurately as you
can.

1. Compared to the entire task time (1/2 hour) how much
time did you spend thinking about ways in which to achieve
your goal? This does not include the time you spent working
on the puzzles or looking for words to fill in.

l _____________ i ............. i ............. l ............. l
1 2 3 4 5

none a little a moderate quite a very
amount bit much

2. About how much time, in minutes, did you spend thinking
about ways in which to achieve your goal?

3. Developing a strategy for this task was
for accomplishing as much as I did.

 

l 2 3 4 5
not at all somewhat moderately very extremely
important important important important important
4. To work on the task I . (circle one)

 

1= just started working

2= thought about one or two methods to work toward my
goal then settled on one

3= took a moment to think about several possible
methods

4= thought about several ways to work toward my goal
and how to try each one

5= carefully planned how to work toward my goal by
considering time pressures and other elements.

131

5. To accomplish the goal assigned to me I was
about the manner in which I tried to finish the whole task.

 

not at all somewhat moderately very extremely
deliberate deliberate deliberate deliberate deliberate

6. How much effort did you expend to develop a strategy for
this task.

i
l
1 2 3 4 5

no effort a little a moderate a lot of an extreme
effort amount of effort amount of
effort effort

7. While working on developing a strategy I found myself

 

concentrating .

i ............. i ............. I ............. I _____________ l

l I l l I

1 2 3 4 5
not hard a little bit moderately very extremely

at all hard hard hard hard

APPENDIX F

Strategies Used

132

The following items refer to the specific strategies
that you used while working on the task. Please mark to
what extent you used each strategy listed. Please respond
to every item. If you do not remember exactly what is meant
by the term strategy, as it is used in this study, refer to
the instructions at the beginning of the questionnaire that
you completed prior to this one.

Please use the computer scored answer sheet (bubble
sheet) that you used for the ability test to respond to
these questions. Start with number 91 on the answer sheet
and answer all questions consecutively. The number in
parentheses refers to the number on the answer sheet that
corresponds to that response.

1. I compared the three different types of puzzles to see
which I could do best.

1 ----------------------- 2 ----------------------- 3

l l i

l l l

I did this very I did this I did this mostly
little or not a moderate or exclusively

at all amount

2. I sorted the puzzles into those I could do and those I
couldn't do.

.1 """""""""""" i """""""""""" 3
I
I i l
I did this very I did this I did this mostly
little or not a moderate or exclusively
at all amount

3. I 'warmed up' on an easy puzzle(s), then worked on the
medium puzzles.

I1 ----------------------- 2 ----------------------- 3
l l
I i l
I did this very I did this I did this mostly
little or not a moderate or exclusively
at all amount

4. I did as many as possible on the easy puzzles, then did
as many as possible on the medium, then did as many as
possible on the hard puzzles.

l ----------------------- 2 ----------------------- 3
l l l
I did this very I did this I did this mostly
little or not a moderate or exclusively

at all amount

133

5. I did as many as possible on the medium puzzles then did
as many as possible on the hard puzzles.

1 """""""""""" i """""""""""" i
i
l l
I did this very I did this I did this mostly
little or not a moderate or exclusively
at all amount

6. I did some hard, some medium, and some easy puzzles, in
no particular order.

1 ----------------------- ¥ ----------------------- ?

I .

I i I

I did this very I did this I did this mostly
little or not a moderate or exclusively

at all amount

7. I did only easy and medium puzzles.

1 """""""""""" i """""""""""" i

I

i i i

I did this very I did this I did this mostly
little or not a moderate or exclusively

at all amount

8. I did the puzzles in the order in which they were given.

1 """""""""""" i """""""""""" 3
i
i l l
I did this very I did this I did this mostly
little or not a moderate or exclusively
at all amount

9. I worked on only easy puzzles.

1 ----------------------- 2 ----------------------- 3

l i I

l I l

I did this very I did this I did this mostly
little or not a moderate or exclusively

at all amount

10. I worked on only medium puzzles.

1 ----------------------- 2 ----------------------- 3

l i i

i I I

I did this very I did this I did this mostly
little or not a moderate or exclusively

at all amount

134

11. I worked on only hard puzzles.

1 ----------------------- 2 ----------------------- 3
l l

l I
I did this very I did this I did this mostly
little or not a moderate or exclusively
at all amount

12. I tried to complete a puzzle, before going on to
others.

1 ----------------------- 2 ----------------------- 3

l I

l l l

I did this very I did this I did this mostly
little or not a moderate or exclusively

at all amount

13. For each puzzle that I worked on, I scanned the clues,
then filled in words mostly at random.

1 ----------------------- 2 ----------------------- 3

l l I

I l I

I did this very I did this I did this mostly
little or not a moderate or exclusively

at all amount

14. I worked on the ‘across' words, then the ‘down' words,
then went back to the ‘across'.

1 ----------------------- 2 ----------------------- 3

i l

i i l

I did this very I did this I did this mostly
little or not a moderate or exclusively

at all amount

15. I did one or a few ‘across' words and one or a few
‘down' and then used those words to find others.

1 ----------------------- 2 ----------------------- 3
i I
i i I
I did this very I did this I did this mostly
little or not a moderate or exclusively
at all amount

16. I did all the ‘across' words that I could, then all the
‘down' that I could, then moved onto another puzzle without
going back.

1 ----------------------- 2 ----------------------- 3

l l l

l l i

I did this very I did this I did this mostly
little or not a moderate or exclusively

at all amount

135

17. I answered the questions I knew first, then the ones I
thought I knew, then I went back to fill in whatever was
left.

1 """""""""""" i """""""""""" i

I

l l i

I did this very I did this I did this mostly
little or not a moderate or exclusively

at all amount

18. Other (please use the space below to fill in any
additional strategies that you used and rate it (them) on a
1 to 3 scale as above).

ENDNOTES

1. Task specific skills, unlike cognitive processes, are
directed only at a specific task or set of tasks and
therefore may have effects on strategy development that are
different from those of general or specific cognitive
abilities. Increased knowledge along with chunking,
hierarchically arranged cognitive structures, and problem
specific cognitive schema may enable expert performers to
bypass some if not all of the strategy development that
novices must proceed to.

Further, Schmidt, Hunter, Outerbridge, and Goff (1988)
researched the relationship between job experience and
cognitive ability with respect to performance for several
military jobs and found that for some jobs as job experience
increased, performance was determined to a lesser degree by
initial ability. While Hunter et al. (1988) found that for
some job criteria the validity of general cognitive ability
tests for job performance neither increased or decreased
with increased job experience other jobs, when the job
criteria used was supervisory ratings, the performance level
of both high and low ability individuals converged over time
as job experience increased. This convergence may indicate
that experience, in some sense, replaces ability for some
jobs.

According to this research, if task specific skills
were considered in Figure 12, it may be found that the
effects of skill level (expertise) directly affect
performance (positively) and moderate the relationship
between strategy development effort and goal setting such
that as goal specificity and difficulty increase,
individuals low on task specific skills will engage in
strategy development increasingly while individuals with
expert skills will not engage in strategy development.

While this will not be empirically addressed in this
study, future research may be aimed at examining these
issues more closely.

2. Copied with permission from The Qell Big 590k of
greseworge 5 Pencil Eegzles #6 (New York: The Dell

Publishing Company, Inc.).

136

 

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