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thesis entitled

A STUDY OF MICHIGAN DAIRYMEN'S ATTITUDES
TOWARD THE DAIRY HERD IMPROVEMENT ASSOCIATION'S
PRODUCTION TESTING PROGRAMS

presented by

Ronald E . Wallace

has been accepted towards fulfillment
of the requirements for

Master's degreein Communication

 

Major professor

Date July 15, 1980

0-7639

 

 

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A STUDY OF MICHIGAN DAIRYMEN'S ATTITUDES
TOWARD THE DAIRY HERD IMPROVEMENT ASSOCIATION'S
PRODUCTION TESTING PROGRAMS

BY
Ronald E. Wallace

A THESIS

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

MASTER OF ARTS

Department of Communication

1980

p‘ :r In -‘"" q“ 3"
”'3' [f/ (In (ur' i) (:1

Accepted by the faculty of the Department of
Communication, College of Communication Arts and Sciences,
Michigan State University, in partial fulfillment of the

requirements for the Master of Arts degree.

 

Director 0 Thesis

Guidance Committee: Z: 23M , Chairman

S:§,HC2,J3’(“/‘
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d

 

ABSTRACT
A STUDY OF MICHIGAN DAIRYMEN'S ATTITUDES

TOWARD THE DAIRY HERD IMPROVEMENT ASSOCIATION'S
PRODUCTION TESTING PROGRAMS

BY
Ronald E. Wallace

This study investigates the attitudinal space of
Michigan dairymen in relation to the Dairy Herd Improvement
Association, as measured with metric multidimensional scal-
ing techniques. The purpose is to find a practical solution
to the low adoption of DHIA's production testing programs.

In addressing this problem, classical diffusion theory
and metric multidimensional scaling techniques are used,
including four Significant Other queries.

Demographically, annual production levels were the
most significant indications of ad0ption of the innovation.

The best persuasive message produced by the analysis
was: Accurate Information, Convenient, and Profit; when
attributed to DHIA this message will produce the maximum
motion in the space causing QEIA and £22 to move toward
each other.

The significant other probes found that promotion of
DHIA by local bankers, Production Credit Associations, other
farmers and county agents of the Extension Service, would be
the best interpersonal channels to utilize in diffusing

DHIA's innovation.

ACKNOWLEDGMENTS

To Dr. L. E. Sarbaugh, Dr. J. D. Woelfel, and Dr.

C. Meadows for their expertise, patience, and encouragement.

To my parents, John W. and Jane K. Wallace, without
whom none of this would have been possible.

Special thanks to the staff of the office of the Dean
of the College of Communication Arts & Sciences for all
their work, encouragement and coffee.

To my wife Shelley Iva Jacobs without her love,
encouragement and understanding I may well have never

finished this work.

ii

TABLE OF CONTENTS

Chapter

I INTRODUCTION . . . . . . . .
Theory . . . . . . . .

II SAMPLING INFORMATION . . . . . .
Methodology . . . . . . .

Instrument Construction . . . .

III RESULTS . . . . . . . . .
Perceptions of DHIA . . . . .

Message Strategies . . . . .

IV DISCUSSION . . . . . . . .

APPENDIX A: THE PRODUCT
l-A The BARN SHEET (data received by
the dairyman from DHIA) for the
M.S.U. Kellogg Guernsey herd .

B: QUESTIONNAIRES

A- 1977 Galileo Questionnaire
(complete with cover letter) .

B- 1977 Semantic Differential
Questionnaire (cover letter
same as A above) . . . .

C- 1978 Galileo Questionnaire
(complete with cover letter) .

C: STATISTICS

A- Discriminate Analysis
(stepwise procedure) . . .
8- Galileo Means, Standard

Deviations, Standard Errors,
Skewness, Kurtosis, Count,
MinimumrMaximum Values,

Percent Error, Galileo Means
Matrix . . . . . . .

iii

Page

16

20
22

29

32
39

46

50

51

58
65

73

74

APPENDIX C (cont'd.)

REFERENCES.

Page

Coordinates for the Multidimen-

sional Space for Data Sets I, II

and II as well as Data Set Three

Split Three Ways by Adopter

Category . . . . . . . 74

iv

Table

2.1

- 3.2

LIST OF TABLES

Cross Tabulation of Data Sets by Adopter
Category. Presents Number of Responses
for Each Data set by Adopter Category .

Return Rates for All Data Sets . . .

Sampling Strategy for Non-directive
Interviews. Ss=36, Counties Chosen

for Sampling are from Regions in Michigan
with the Greatest Concentration of
Dairymen . . . . . . . .

Sampling Plan for 36 Non-directive
Personal Interviews. Regions by Adopter
Categories . . . . . . . .

Mailing Schedule for All Data Sets . .

Significant Other Data. Including All
Responses which Totaled Ten Percent or
More of Returns. Numbers Alone Represent
Count, Numbers in Parentheses are the
Percent of the Total Number of Question-
naires Returned with Significant Other
Data. Total Questionnaires Returned with
8.0. Data = 239 . . . . . .

Self-Concept and DHIA Vector Lengths with
Their Respective Percent Error Below in
Parentheses. . . . . . . .

Self-Concept and DHIA Vector Lengths for
1978 Data, Split by Adopter Category.
Percent Error in Parentheses . . .

Standard Deviation as a Measure of

Heterophily. Vector Lengths in Parentheses,

1978 Data Split by Adopter Category. .

Page

l7

19

22

23
27

31

34

36

38

Figure

1.1

LIST OF FIGURES

DHIA Diffusion Curve. Adoption Rate
of DHIA Production Testing Presented
as the Percent of Cows Enrolled in

the Program, 1906, 1926, April 1978.

The Adoption Rate (percent on test,
brokenline) Compared with the Number
of Herds on Test from 1965 through
April 1978 . . . . . . .
The Plot, or Map, of the First Three
Principal planes for the 1978 Data,
Data Set Three . . . . . .
Plot of x-y Plane for 1978 Data. .
Plot of y-z Plane for 1978 Data.

Plot of x-z Plane for 1978 Data. .

vi

Page

10

12

40
41
42
43

CHAPTER I

INTRODUCTION

"The Dairy Herd Improvement Association and the Co-
operative Extension Service of Michigan wish to increase
the rate of adoption of DHIA's Production Testing Service."

DHIA is a non-profit association of Michigan dairy-
men. The service they provide is a complete set of produc-
tion records for each individual animal in a dairyman's
herd. This allows herd management decisions to be made on
a per cow basis as Opposed to the antiquated rolling herd-
average approach. This information makes computing individ-
ual animals' income contribution possible from production
and feed ration data. There are three primary benefits
claimed for DHIA records; (1) proving sires; (2) improving
herd quality; and (3) accurate feed ration data. These
three factors, used in conjunction, will increase production
and therefore profits for any dairy herd not now using a
testing device.

The information which is input as raw data into the
record computation and is collected monthly is of five
types: (1) weights from two successive milkings for each
cow in production, (2) a representative milk sample from
each cow for the butterfat content analysis; (3) pertinent

1

2
dates (new fresh dates, dry dates, out of herd dates, and
new breeding dates); (4) amount of grain fed to each cow;
and (5) roughages fed on a herd basis. From these data,
the production records are calculated to provide the dairy-
man with the following (12) pieces of information: (1) pro-
duction (milk in 1bs., fat content); (2) age; (3) gross
income per cow; (4) income over feed costs; (5) days in pro-
duction; (6) recommended grain ration; (7) expected dollar
income for 305 days of production; (8) breeding date; (9)
fresh date; (10) suggested breeding date; (11) pregnancy;
check date; and (12) suggested dry date. An example of the
record set for Michigan State University's Kellogg-Guernsey
herd will be found in Appendix A.

DHIA provides 12 record-keeping programs for Michigan
dairymen. Three are classified "official" and the results
are used by USDA in the Sire Summary program; nine are Sun-
official" and these sets are provided only for the dairyman
enrolled.

The goal of DHIA and of the Extension Service is to
have all Michigan dairymen with 40 or more cows in milk
production--rough1y 70% of all herds in Michigan--on some
form of testing program. This research addresses the prob-
lem of finding some practical means of positively affecting

the rate of adaption of DHIA.

Theory

This is a communication problem with the variables of
classiCal diffusion theory as expressed by Rogers (1971).
Most terms and definitions here are taken from his work.

Two changes must take place with Michigan dairymen in
order to affect the diffusion rate of DHIA: (1) attitudin-
al and (2) behavioral; e.g., Michigan dairymen changing
their attitudes toward DHIA in a positive way and as a re-
sult enrolling in some DHIA program, behavioral change.

Rogers defines social change as "any alteration in
the structure and function of a social system," with social
system defined as "a collectivity of functionally differ-
entiated individuals engaged in joint problem-solving with
respect to some goal."

Because social change takes place within a social
system, the social structure, the various statuses, norms
and values of that system, will either impede or accelerate
the rate of diffusion through "system effects." These
system effects arise from the interaction, communication
and behavior between the individuals who maintain those
various statuses, norms and values of the social system.
Indeed, it is this very interaction that establishes the
social system, its structure and the feedback networks
utilized to sort information for the decision to adopt or

reject any innovation.

4

The strategy used in this study is to generate per-
suasive messages to be input into this information network
at various strategic points as defined by the respondents
themselves.

Rogers further defines change as either imminent or
contact change; imminent change is internally created and
developed, while contact change occurs when a source extern-
al to the social system introduces the innovation. Directed
contact change occurs when the external source seeks to in-
troduce an innovation to achieve some goal they have defined.
Since directed contact change requires that information be
passed from a source to the members of some social system,

a communication process is involved by definition. This
process is a special case of communication, the diffusion
of innovations--the spread and adoption of new ideas and
practices. Because diffusion of innovations requires be-
havior changes on the part of the people who make up the
social system, risks are involved for the receiver of the
information, as well as for the source. The risks for the
receiver could be social rejection, economic loss, or both.
The risks to the information source could be reduced credi-
bility in the eyes of the members of the social system, or
possible rejection by the entire community. This rejection
is most usually explained by a heterophilic relationship

existing between the information source and the receivers.

5

Heterophilly as an aspect of diffusion is functional-
ly related to the source and receiver. Rogers defines heter-
ophily as, "the degree to which pairs of individuals who
interact are different in social attributes, statuses, norms,
and values." Here we shall expand this definition to a sys-
tems level, and shall define heterOphily as the degree to
which social systems that interact differ in social attrib-
utes. Since heterophilic interaction is likely to cause
cognative dissonance, when it occurs in the diffusion of in-
novations it can create negative attitudinal percepts toward
the innovation and the change agency, presenting barriers to
adoption.

Rogers lists one main element in the diffusion of in-
novations, the innovation itself, and its five constituent
attributes. The five attributes are: (1) relative advan-
tage, economic and social, as perceived by the adopters;

(2) compatibility, to what extent adopters perceive the in-
novation as being consistent with their values, needs, and
experiences; (3) complexity, the degree to which the innova—
tion is perceived as being difficult to understand and to
use; (4) trialibility, the extent to which the innovation
may be experimented with on a limited basis; and (5) observ-
ability, the degree to which the results of the innovation
are visible to the ad0pter. The more visible the results

of an innovation, the more likely a client is to adopt it.

6

Two communications channels are available to any
agency: (1) the mass media and (2) interpersonal channels.
The effect of these two channels are (1) the mass media are
the most effective in creating knowledge-awareness of the
innovation, while (2) interpersonal communication is most
effective when trying to change people's attitudes and be-
havior.

"Time is the key to diffusion research" (Katz et a1.,
1963). All human interaction takes place in a time refer—
ent. Rogers identifies three variables related to time;
they are: (1) the decision process, which has four compon-
ents ( (a) knowledge, (b) persuasion, (c) decision, and (d)
confirmation); (2) innovativeness, the relative time at
which adoption occurs compared to others in the social sys-
tem; and (3) the rate of adoption, a quantifiable measure
of the acceleration of adoption.

Rogers discusses two final variables in diffusion to
be considered here: (1) the change agency, an organization
attempting to influence innovation-decisions in a given
direction; and (2) opinion leadership, an individual's abil-
ity to influence the attitudes and behavior of others with
relative frequency.

The problem now needs to be restated in terms of
Rogers' theory and the previous research addressing these

specific variables: (1) heterophily; (2) relative advantage;

 

(3) compatibility; (4) complexity; (5) trialibility; (6)

7
observability; (7) opinion leadership; (8) innovativeness;
(9) the change agency; (10) rate of adoption; and (11) the
decision process.

Heterophily has not been previously researched for
Michigan dairymen and DHIA. From Rogers' theory it may be
expected that a homophilic relationship exists between the
adopters of the innovation and DHIA. Concommitantly, a
heterophilic relationship exists between DHIA and those
Michigan dairymen who either have discontinued use of the
innovation or never adopted.

The relative advantage of DHIA is economic as well as

 

social. Economically, Meadows and Knisely (1976) have shown
in their study of Kent County Michigan dairy herds that the
average 140-cow-herd's dollar income per year for those
herds "on test" (enrolled in DHIA production testing) was
$51,600 higher than those herds not on test. The net re-
turn, after deduction of test and feed costs, was $29,800
greater per year than from herds of comparable size.

They conclude:

Dairymen with production records have a
definite competitive advantage over those
not testing. . . . Large commercial dairy
farms (100 cows or more) are not likely to
survive (in the long term) without records.

Hillman and Logan (1976) demonstrate the genetic po-

tential of a dairy herd:

Genetic potential is the inherited ability
of a cow, or herd, to produce more milk when

8
other management factors are similar. The
genetic potential is increased through (1)
use of DHIA records on each cow as a basis
for culling, breeding, and feeding, (2)

breeding to sires that produce superior
offspring.

Socio-cultural advantages accompanying these advan-
tages are: increased social status, esteem, prestige, con-
venience in record keeping, and knowledge that one's work
returns increasing rewards. Also, there are awards given
for producing above certain levels.

The variables complexity and compatibility have been

 

 

shown by the work of Houghaboom (1963) and Kucker (1970) to
be closely related and will be discussed concurrently.
Houghaboom (in Vermont) and Kucker (in Michigan) found that
discontinuance was most often caused by the farmers' inabil-
ity to realize the value of testing, due to an inability to
understand the computerized results of the test; they found
similarly for non-adopters. Additionally, some Michigan
dairymen cited that their cows were not good enough to be
on test; hence embarrassment by comparison would be a com-
patibility factor.

Kivlin (1960) found complexity of farm innovations to

 

be negatively related to the rate of adoption more than any
other variable except relative advantage. Given the findings
of Houghaboom and Kucker, complexity would seem to be one of

the key factors in the slow rate of diffusion of DHIA.

9

Trialibility demands are met in the case of DHIA as

 

the programs are easily enrolled in and costs are low.

Observability of the effects of the innovation on

 

milk production is problematic. If the adopting farmer
utilizes the feed measurement option, then his milk produc-
tion should begin to increase in the short term, say one to
three months. But, if the adopting dairyman does not con-
sider the feed variable in his milk production, then in-
creased milk production will depend upon increasing the
quality of his herd through breeding techniques and will be
long term in nature. As average turnover of a dairy herd in
Michigan is seven years, observability is lost and the value
of the testing program obscured.

Opinion leaders at the community-system level for

 

Michigan dairymen have not been shown through previous re—
search.

Innovativeness among Michigan dairymen and adoption

 

of DHIA production testing is bound to the heterophily phe-
nomenon discussed in Rogers' theory. Again it must be
pointed out that Michigan dairymen are not laggards where
adoption of useful innovations are concerned; DHIA was found-
ed in Michigan in 1904. Where profitability has been shown,
dairymen are quick to adopt; but where advantages are clouded
with misunderstanding, adoption suffers.

The rate of diffusion is the elemental time variable

 

discussed in this work. Figure 1.1 presents the diffusion

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Figure 1.1.

10

11g411441

35 40 45 50 55 60 65 70 75 8

DHIA Diffusion Curve. Adoption Rate of DHIA
Production Testing Presented as the Percent
of Cows Enrolled in the Program, 1906, 1926-
April 1978.

ll

curve for DHIA and demonstrates the exponentiality of the
diffusion process. From 1906 to 1958 the rate of diffusion
was one-tenth of one percent, (0.1%); from 1958 through
1978 the rate of diffusion increased to one percent per
year, (l%/yr.). The effect, historically, of various macro-
economic phenomena upon the rate of diffusion is also
demonstrated.

Figure 1.2 is a presentation of the adoption rate as
a percentage of all herds in Michigan as compared to the
number of herds on test from 1965 to April 1978. The prob-
lem, as posed by this presentation of the data, is that
while DHIA has consistently increased the number of cows on
test, the number of herds on test has not varied signifi-
cantly. Over this same period the average herd size in
Michigan increased from approximately 40 animals per herd
to 75 animals per herd. One may conclude from this that
the problem of heterophily may be greater than otherwise
indicated.

The final variable to be discussed is that of the

change agency, DHIA; no previous research has been accom-

 

plished to indicate how Michigan dairymen perceive DHIA in
relation to themselves. Priess (1954) found that the suc-
cess of Michigan Cooperative Extension Service agents was
positively related to their disregard for the expectations
of the Michigan Cooperative Service, in favor of their

clients' expectations.

12

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13

The problem now needs to be restated in terms of the
theory. There are three variables which require attention;
they are (l) heterophily, (2) community opinion leaders and
(3) the rate of adoption. To affect the slow rate of adOp-
tion and reduce the level of heterophily between this large
group of non-users and DHIA, a measurement of the cognitive
space of Michigan dairymen in relation to DHIA needs to be
taken. Additionally, the opinion leaders at the community
level need to be identified for specific issues related to
the dairy industry. With this measurement of the cognitive
space of the dairymen, a practical solution to the problem
of slow rates of adoption of DHIA can be reached. Using
the Galileo methodology (Woelfel & Gillham, 1975), the major
contributions of this study will be to generate persuasive,
client-oriented messages, to reduce the degree of existing
heterophily and to increase the rate of adoption.

Linear force aggregation theory (Woelfel, 1969) states
that the position of any individual will move toward the
mean of all messages he/she receives. Therefore, when one
is surrounded by similar attitudes, his/her attitudes are at
or about the mean and this position is stable. It becomes
obvious then that, given the measurement of an attitude, the
degree of heterophily is given by the standard deviation of

the mean measure of that attitude toward any concept.

14

Ht = s.d.
Where Ht = heterophily
s.d. = standard deviation

It follows then that linear force aggregation theory pre-
dicts that communication increases homophily. Additionally,
given any two groups, each group's mean attitude will move
toward the combined mean for both groups as communication
increases between the two groups.

To measure the cognitive space of Michigan dairy
farmers, the Galileo system of measurement will be used.
The Galileo procedures are designed to give us a precise
map of the cognitive space of any group of people. The
Galileo system is a theoretical variation of classical metric

multidimensional scaling. Woelfel explains it this way;

All MMDS procedures begin with the
assumption that any element of cog-
nition, whether it may be a word, be-
lief, idea, value or any other cognitive
object, is defined in terms of its rela-
tionships to other elements of cognition;
and, further, that these relationships
take the form of dissimilarities or
distances.

In the case of Galileo these distances are estimated direct-
ly by the re3pondents. They are asked to make these judg-
ments given some criterion, such as: if x and y are n units
apart, how far apart are a and b? While this system of
measure may be unreliable at the individual level, due to

the complexity of judgments required and lack of structure

15

of scale, in the aggregate any level of reliability may be
achieved by simply increasing the number of respondents,
given the law of large numbers. Thus, the scale presents
itself as a fully metric, unbounded, continuous ratio scale.

The scaling procedure described above will be used
with a pair-comparison questionnaire to determine precisely
and accurately the cognitive space of Michigan dairymen for

the time period covered.

CHAPTER II

SAMPLING INFORMATION

To fulfill the objectives of this study, data were
collected from a random sample of all grade "A" milk pro-
ducers in Michigan. The sample was drawn by DHIA as
follows; the sample for data set one (March 1977 through
June 1977) consisted of 1996 grade A producers from a total
of 7,400. Data set two (May 1977) included all grade A
producers in the Lansing and Grand Rapids telephone direc-
tories. This was accomplished by cross referencing the
list of grade A producers and the respective telephone
directories. Data set three (January 1978 through June
1978) consisted of 2,003 grade Aproducers of 7,100 total.

A total of 421 instruments were completed and returned.
The response rates for the specific data sets by adopter
categories is presented in Table 2.1 below. Row totals
represent the adopter categories' totals as count and per-
cent of total respondents. The column totals show their
corresponding counts and percent for the specific data sets.
Adopter categories are self-eXplanatory: adopters were
those enrolled in some sort of milk testing program at the
time of response; discontinuers, were those who indicated

16

17

 

 

 

 

 

 

 

 

 

 

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18
they had been enrolled at one time and subsequently dropped
out of the program; and non-adopters were those respondents
who indicated they had never enrolled in a milk testing
program. As can be seen from Table 2.1 the sample is pre-
dominated by the return rate for adopters 61.3%, non-adopters
21.1% and discontinuers 17.6%. Adapters make up only 30%
of all Michigan dairymen.

In descriptive terms the sample was characterized by
the following statistics: the average respondent was age
45, married, with a high school education; 21.2% of all re-
spondents report more than 12 years of education. The
sample was further represented by dairymen whose annual
average production was 14,000 pounds of milk. Their average
herd size was 62 head of cattle in milk production; the
average number of acres operated was 416. These facts
represent a rather well educated, above the norm respondent,
in terms of the average dairymen as reported in the 1978
Michigan Agricultural Statistics. In this report, the
average dairyman milked 34 cows, yielding an average annual
production of 11,893 pounds of milk per cow. These figures
for 1978 included all Michigan dairymen, marketing grades
A and B milk. This sample only recorded figures for grade
A producers.

There were 155 instruments returned with either
address problems or insufficient information to warrant key-

punching. Of these 155 instruments, 81 were from data set

19
one, 4 from data set two, and 70 from data set three.
Table 2.2 presents the return rates for all DHIA data sets

in the present survey.

Table 2.2. Return Rates for All Data Sets

 

SNMEE TUBE. REHEN [NUQEEE USMEE ADMEHED

 

 

DNHXSET EHZE FESRmfifil IQHE RERHEB IEHUWTS IEHE
l 1796 265 14.8% 81 184 10.7%
2 164 59 36.0% 4 55 34.4%
3 2003 252 12.6% 70 182 9.4%
TOTALS 3963 576 14.5% 155 421 10.6%

 

A drop of 1.3% in the return rate from data set one to data
set three was recorded, but is not considered large enough

to be significant. The difference between the two data

sets is an additional 25 items that appear on the 1978 in-
struments. The data set one instrument was split four ways
to reduce respondent fatigue and, thereby to increase the re—
sponse rate. The 1978 instrument, data set three, was

split two ways and the resultant decrease in the return rate,
1.3%, is offset by the wealth of data added to the 1978
responses.

In May 1977 there were 200 questionnaires mailed in
the random sample using a seven point semantic differential
scale to compare the return rates with the last 200 Galileo
instruments. This would give us an idea of the difference

in difficulty of completing an instrument with seemingly

20
antiquated, semantic differential scale and the more accu-
rate and precise Galileo ratio scale. The return rates for
the two groups revealed that (l) the semantic differential
instruments had total returns of 35 completed, or a 17.5%
return rate, (2) the Galileo scale questionnaires, for the
same period and number of mailings, had total returns of 33
completed instruments or a return rate of 16.5%. No sig-
nificant difference is evident. Due to the four way split
in the 1977 instruments, there was insufficient data on the
nine complete semantic differential questionnaires to

warrant keypunching or data anlysis.

Methodology

 

The Galileo method of measuring the cognitive space of
any group of people is a set of metric multidimensional
scaling procedures which allow ratio measurements of the
differences, or distances, between concepts held by any
group toward a given phenomenon. By using ratio scaling
techniques, a precise and accurate mental map of the sample
of Michigan dairymen's cognitions regarding DHIA may be con-
structed. This was accomplished by conducting 30-50 non-
directive personal interviews with a stratified sample of
the population, so as to include the greatest amount of vari-
ation of opinions held by the various members of the popula—
tion. These personal interviews produce a list of concepts,
which are arrived at via a content analysis of the responses

to the nondirective interviews. Typically the list contains

21
10-20 concepts. To this list was added a self-concept,
such as you, me, myself, and the object concept, in this
study, DHIA. From the completed list a pair-comparison
questionnaire was constructed, which included each concept
matched with every other concept, creating a questionnaire
with 2(221) items.

Tie respondent was then asked to measure the differ—
ence, or distance, between concepts in the instrument using
the Galileo scale. The Galileo scale offers a criterion
measure and asks respondents to estimate the distances be-
tween concepts based upon this criterion. The typical cri—
terion would ask: if x and y are n units apart, how far
apart are a and b? The scale was further defined such that
identity is equal to zero; and if two items are perceived
as being twice as far apart as x and y, the respondent
writes 2n, and so on. We see that this constitutes an un-
bounded and continuous ratio scale associated with typical
measures in the physical sciences.

The present work describes the procedures used to
identify those concepts held by Michigan dairymen toward
DHIA, to map that cognitive space, to take dynamic measures
of the sample to insure accuracy, and to generate persuasive
messages to reduce the amount of heterophily between

Michigan dairymen and DHIA.

22

Instrument Construction

 

The method requires a pair-comparison type question-
naire made up from the concepts which the farmers themselves
use to define DHIA. These concepts were discovered by con-
ducting 29 confidential, nondirective personal interviews
with Michigan dairymen from a sample of 36. The sampling
procedure is listed below, in Tables 2.3 and 2.4. It is
designed to include the maximum attitudinal variation in the
population. Meadows' long experience and expertise was the
criterion for selection of the above areas. As can be seen
from Table 2.3, the criteria for the stratification of the
sample were geographic region, ethnic group and herd size.
Table 2.3. Sampling Strategy for Non-directive

Interviews. Ss=36, Counties Chosen for

Sampling are from Regions in Michigan with
the Greatest Concentration of Dairymen

 

REGION ETHNIC GROUP HERD SIZE

 

THUMB, counties

Tuscola, Huron,
Sanilac Polish, Irish Average
(50-100)

CENTRAL, counties
Ingham, Clinton,
Shiawassee Mixed European Average to
‘ Large
(70—120)

WESTERN, counties
Allegan, Kent,
Barry Dutch Small — Average
(0-70)

 

23

Table 2.4. Sampling Plan for 36 Non-directive Personal
Interviews. Regions by Adopter Categories

 

 

 

REGIONS
ADOPTER _
CATEGORIES Thumb Centrali Western Totals
Long term adopter
(1 year +) 2 (1) 2 (1) 2 (1) 6
New adopter
(less than 1 year) 2 (l) 2 (l) 2 (1) 6
Discontinuer 2 (1) 2 (1) 2 (l) 6
Non-adopter 6 (3) 6 (3) 6 (3) 18
Totals 12 12 12 36

 

In Table 2.4, the sampling plan is presented and shows us
that twelve prospective respondents were chosen from each
region, with four coming from each county within a region.
The interview sample was also stratified as to adopter cate-
gory: six each were chosen from the first three categories:
long term adopter (greater than one year); short term
adopter (less than one year); discontinuers; and non-adopt-
ers. Adopter categories were based on information available
from DHIA. DHIA did not have adopter category information
for more than one year previous to the date requested,
January 1, 1977.

During the personal interviews each respondent was
asked simply to talk at length about how he felt about DHIA
and the reasons for their particular attitudes. The con-

cepts were recorded as they were described by the respondents.

24
The interviews were conducted to the point of redundancy.
A content analysis of the interviews reveals that of the
many words and phrases that the dairymen use to describe
DHIA the list was soon reduced to twelve concepts. To com-
plete this list, the self-concept "you" and the target con-
cept "DHIA Production testing" were added to the twelve

original concepts; the complete list follows:

01 Accurate Information 08 Measuring Production

02 You 09 Necessary

03 Good 10 Profit

04 Convenient ll Inexpensive

05 Keeping Records 12 Computers

06 Culling 13 Useful

07 Breeding l4 DHIA Production Testing

These fourteen concepts produce a 91 item pair-comparison
questionnaire, n(n-l).

The criterign pair chosen was Dairy Farming and Crop
Farming; the comparable distance was 100 units, i.e. Dairy
Farming and Crop Farming are 100 units apart. The rational
for this criterion pair also resulted from the personal
interviews, many farmers pointed out that dairy farming and
crop farming were very different.

Four significant other queries (Haller and Woelfel,
1972 and 1975) were placed at the end of each 1977 question-

naire, to identify the Opinion leaders for each of the four

information categories chosen at the community level. The

25

four categories are: (1) farming in general; (2) herd pro-
duction; (3) keeping records; and (4) money and finances.
These significant other probes appear on all 1977 instru-
ments only.

The demographic package was taken from Kucker (1970)
and includes items numbered 1-3, 5-9, 20, 21, 23, and 24
from page 114 and items 1-3, 21 and 23 from page 115.
These items are listed on the demographic sheet of all
questionnaires, page two (see Appendix B). In the 1977
random sample, 1,796 Galileo and 200 semantic differential
instruments were split four ways to reduce respondent fa-
tigue and to increase the response rate. The Galileo ques-
tionnaires are identified as Q-type 1—4, and the semantic
differential questionnaires are identified as Q-type 5-8.
The designation of the questionnaires as to Q-type is only
for the convenience of identification. Therefore question-
naires designated as Q-type l, Q-type 2, and Q-type 3, as
well as Q-types 5, 6 and 7, each contain 36 pair-comparison
responses; while Q-type 4 and Q-type 8 each contain 41 items.

There were also 164 Galileo instruments mailed in the
Lansing and Grand Rapids areas, with the sample being drawn
from the respective telephone books. Each dairyman in the
areas listed above was called and asked to fill out a ques-
tionnaire; those who responded positively were mailed an
instrument. After an interval of two weeks had expired,

each of the 164 dairymen were called back and asked if they

26
had received the instrument, and if so would they please
fill out the questionnaire and return it. This instrument
was split two ways, and designated as Q-types 9 and 0; Q-
type 9 contained 59 pair-comparison items and Q-type 0 con-
tained 64 items.

The first five pairs on all 1977 instruments included
the criterion pair followed by four practice items: The
questionnaire begins with item 6. Additionally, pair 13—14,
useful and DHIA, were inadvertently not included on any 1977
instrument.

The 1978 questionnaires were an improvement of the
1977 Q-types 9 and 0. These instruments were precoded for
keypunching, and have the appropriate addressors in the ex-
treme left hand columns. The 1978 instruments were desig-
nated as Q-type l and 2 and contain 64 and 67 pair items,
respectively.

The main wave sample for 1977 was drawn from the 1976
list of 7400 grade A producers by DHIA and included 1996
names and addresses printed on address labels. The 1978
sample was similarly drawn and included 2003 names and
addresses from the 1977 list of 7100 grade A producers in
Michigan. Table 2.5 presents the mailing schedule for all
three samples.

The demographic data were analyzed by the SPSS ver-
sion 7.0 computer program. The Galileo scale data were

analyzed by the Galileo version 3.95 computer program.

27

Table 2.5. Mailing Schedule for All Data Sets

 

DATA SET I

 

GALILEO INSTRUMENTS

 

lOO/day for 8 days March 28,
40/day for 22 days April 11,
80/day for 1 day April 27,
36/day for 1 day April 28,

SEMANTIC DIFFERENTIAL INSTRUMENTS

 

40/day for 1 day April 26,

80/day for 2 days
1996 total

1977
1977
1977
1977

1977

April 27 & 28,

— April 8, 1977

- April 26,

1977

1977

 

DATA SET II

 

GALILEO INSTRUMENTS

 

LANSING AREA
45/day for 1 day May 5,

GRAND RAPIDS AREA

63/day for 1 day May 10,
56/day for 1 day May 11,
164 total

1977

1977

1977

 

DATA SET III

 

GALILEO INSTRUMENTS

 

lO/day for 10 days January 19,

20/day for 95 days February 2,
2003 total

1978 - January 31,

1978

June 16,

1978

1978

 

28
The significant other data were compiled and analyzed by
the author.

The Galileo analysis produced a precise and accurate
mental map Of the cognitive space of Michigan dairymen's
attitudes toward DHIA, as well as a set Of persuasive mes-
sages designed tO reduce the heterOphily between the dairy-
men Of Michigan and the change agency, DHIA. Additionally,
the significant other data will allow the agency to channel
persuasive messages through the Opinion leaders Of the
community, as well as through the mass media channels and
interpersonal typically used by the change agency, DHIA,

and the Michigan Cooperative Extension Service.

CHAPTER III

RESULTS

In Chapter I, the Objective stated was to find a
practical solution to the low rate Of diffusion of DHIA by
designing persuasion which, if utilized, would function to
change Michigan dairymen's attitudes toward DHIA. In this
chapter the relevant findings Of the Galileo surveys and
their respective sample demographics and significant other
data, taken from three samples of dairymen over two years,
will be presented.

From February 14 through February 23, 1977, 29 non-
directive personal interviews with representative members Of
the population Of Michigan dairymen were conducted (for
sampling data see Chapter II). Respondents were asked to
discuss in detail DHIA and their attitudes toward the
innovation.

A content analysis Of the responses from these twenty-
nine people showed that over 80% of all statements about
DHIA made reference to only twelve concepts. Those twelve

concepts were:

01 Accurate Information 04 Keeping Records
02 Good 05 Culling

03 Convenient 06 Breeding
29

30
07 Measuring Production 10 Inexpensive
08 Necessary 11 Computers

09 Profit 12 Useful

This number of'concepts,twelve, is consistent with past re-
search experience in diffuse topic areas.
TO these twelve concepts were added the Object, or

target, concept DHIA Production Testing and the self-concept

 

Zgu_which were included in a Galileo type pair-comparison
questionnaire (see Appendix B). The Galileo type question-
naire asked respondents to measure the distance between con-
cepts using the Galileo scale, a simple, but accurate and
precise, continuous, ratio scale. The greater the perceived
difference, or distance, between two concepts, the greater
the number reported by the respondent. Pairs perceived as
identical (no difference) are assigned zeros. The question-
naires were administered by mail in three data sets.

Table 3.1 lists the major responses to the four Sig-
nificant Other probes. Those responses which did not con-
stitute at least 10% Of all responses were eliminated from
Table 3.1.

These responses may be viewed as indicative of those
who comprise the dairymen's information networks for the
four areas in question. They therefore indicate the commun-
ication channels through which some interpersonal messages
may be successfully transmitted to the dairymen not present-

ly enrolled in any production testing program. These

31

 

 

 

 

 

 

 

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32
"significant others" are the most talked to, and should
prove tO be important in persuading those dairymen who are
recalcitrant towards DHIA to change their behavior and
enroll in some sort Of testing program.

The Galileo and demographic responses Of the 421 com-
pleted instruments were keypunched onto computer cards and
input into the Galileo version 3.95 computer program and the
SPSS version 7.0 computer program, respectively. These an-
alyses may be found in Appendix C, demographics, and
Appendix C, Galileo. The analyses yielded three primary re-
sults: (l) a precise and accurate "map" Of the way Michigan
dairymen perceive DHIA; (2) an accurate description Of the
demographic variables concerning those dairymen who respond-
ed; and (3) a number of alternative strategies for improving

the position of DHIA with Michigan dairymen.

Perceptions Of DHIA

 

In Galileo studies the attitudes toward any concept
are measured by the distance between the aggregate self-
concept "you" and any other concept. The greater the dis-
tance between the self-concept and any other concept in the
multidimensional space, the less favorable the attitude to-
ward that concept. Therefore, groups with unfavorable atti-
tudes toward DHIA will report greater distances between
themselves and DHIA, while groups holding favorable attitudes

will report smaller distances.

33

The distances measured between the self-concept, DHIA,
and all other concepts are presented in Table 3.2 for data
sets one, two and three. Row one Of Table 3.2 presents the
distances between the "you" and all other concepts for data
set one; row two (parenthesis) details the percent error Of
measurement for the distances found in row one. Row three
presents the distances between DHIA and all other concepts
in the space, while row four represents the concommitant
percent error. These numbers cannot be judged as high or
low in and of themselves; the criterion for these judgments
was that "dairy farming and crOp farming is 100 units
apart." This was the criterion used by the dairymen to
make the initial measurements which appear here in the ag—
gregate. The next to the last entry in Table 3.2, row one,
shows that the distance between "you" and "computers" is
133 units, the largest distance in data set one. This is
greater than the distance between dairy farming and crOp
farming and is considered to represent a strong negative
attitude toward computers by those dairymen responding.
Indeed, since the criterion pair compares what is really two
different types Of farming, any distance greater than the
criterion distance Of 100 units could be seen as perceived
by the dairy farmers as not related to dairy farming. Thus,
all Michigan dairymen discern the distance from themselves
to DHIA as being 106 units, plus or minus 15% (or a 95% c.i.

between 90 and 122 units). Rows five through eight present

34

 

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35
the same data for data set two, rows nine through twelve
the data in data set three.

Comparing columns two and twelve over time, reading
downward across rows, we discover that while the distance
between "you" and "DHIA" is decreasing the distance between
"you" and "computers" is changing only slightly. The motion
Observed in the space between "you" and "DHIA" could be a re-
flection of the diffusion campaign of October and November,
1977, conducted by DHIA, but not related to this research.
There is the additional factor that approximately 2,400 of
the 7,400 dairymen in Michigan producing grade A milk were
contacted in the spring of 1977 and asked to respond with
their attitudes toward DHIA; i.e. given the above factor, DHIA
had been much discussed within the target population for the
entire year preceding the 1978 data collection. At the same
instant the distance between "DHIA" and "computers" is almost
doubling, while other distances remain relatively stable.
Notice also the motion of the concept "profit" over the time
span of the three data sets. There was little, if any, real
improvement in the attitudes Of dairymen toward the concept
"computers"; the distance remains large, and any perceived
change that seems present may only resemble Brownian move-
ment in the measured attitudes of the population, caused by
conflicting messages about the concept.

Table 3.3 contains the distances for the categories

adopters, discontinuers, and non-adopters for the 1978

36

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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37

data--data set three (these distances appear in Appendix C
in their entirety in the mean distance matrix). Specifical-
ly, note should be taken of the distances between DHIA and
all concepts across adopter categories. This supports the
contention that the Galileo scale is based upon peoples'
viewing those concepts with which they have experience, or
have received increased amounts of communication about, as
being closer to themselves and to their own position, than
those things which are either foreign or objects they have
received very little communication about.

It was stated in Chapter I that the standard deviation
is a quantitative measure of heterophily. Accordingly, .
Table 3.4 lists the vector lengths and their respective
standard deviations for the self-concept "you" and the
target concept "DHIA" for the 1978 data split by adOpter
category. As is indicated in Table 3.4, most self-concept
vectors are stable and reflect a degree of homophily. with
the specific exceptions being the vectors representing the
distances between "you" and "computers." This distance is
large for all categories, increasing in the categories
"discontinuers" and "non-adopter." It should be noted that
the self-concept vectors for all adopter categories, ex-
cepting "you" and "computers," are stable and exhibit a
degree of homophily. Yet, the vectors representing DHIA
and all other concepts for the categories discontinuers and

non-adopters demonstrate a large degree of heterophily.

38

 

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39
Other concept vectors remain stable and exhibit homophilic
relationships. Some exceptions for the aggregate 1978 data
are "profit" and "accurate information," vector length 64
units and S.D. = 126; "inexpensive" and "accurate information,"
vector length 77 units and S.D. = 108. Additionally, most
vectors relating computers and all other concepts (see mean
distance matrix Appendix C) exhibit a high degree of hetero-

phily. Heterophily is the status quo for DHIA in the aggre-

gate for 1978 data as may be predicted from Table 3.4.

Message Strategies

 

The Galileo mean distances may be presented in a map,
but due to the multidimensionality of the map, it is highly
complex and not like the common physical maps most of us are
accustomed to using, e.g., road maps. Thus, the accompanying
plots, maps, are physical presentations of the data and will
only represent the first three principal planes of the space
and, therefore, are only approximate. Figure 3.1, 3.2, 3.3,
and 3.4 represent the first three principal planes of data
sets one, two and three respectively. A perusal of these
maps will add to the comprehensibility of the space and make
the measurements more meaningful.

The strategy is to decrease the distance (in the map)
between the target and the self-concept, DHIA and you. This
requires moving the concepts toward each other in the space.
The Automatic Message Generator (AMG) provides a system to

sort all possible combinations of concepts in the Space,

 

 

 

'£‘>

0V

 

(I

FIGURE 3.1.

The Plot, or map, of the First Three Principal Planes
for the 1978 Data, Data Set Three. (see page 45 for
concept list)

 

 

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019
3.0

Q."°

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FIGURE 3.2.

6
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50

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2?

 

 

 

 

ho

059
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44

taken one, two, three and four at a time, to find those
messages, combinations of concepts in the map, that will move
DHIA toward the concept you in the space.

Table 3.5 lists the best one, two and three concept
messages for each data set. The four-concept messages were
generated but did not produce any results which were superi-
or to the best three-concept messages and so were not listed.
This has been the experience of many researchers on diffuse
topic areas. By reading through the table from left to
right it may be seen how the messages' strategies have
changed over time.

When two concept messages are considered, the best
message for 1978 data is "accurate information" and "con-
venient" which could theoretically reduce the distance from
68 units to 29 units. Yet, here we find that all best
messages include the concept "accurate information" and the
distances are all similar. These two-concept messages all
represent very good strategies.

Now consider the three-concept messages. Again the
concept "accurate information" is included in all the best
three-concept messages. Within the three-concept messages,
two are best for the 1978 data: (1) accurate information,
convenient and proft; (2) accurate information, inexpensive
and useful. Both these messages are excellent messages and
would be capable of greatly improving the attitudes of

Michigan dairymen toward DHIA, reducing the theoretical

45
distance from 68 units to 23 units (Table 3.5). As may be

gleaned from Table 3.5, there are six three-concept messages
which are better than any of the one- or two-concept

messages generated.

Concept List for Interpreting

Figures and Tables

01 ACCURATE INFORMATION - ACC INFO
02 YOU - YOU

03 GOOD - GOOD

O4 CONVENIENT - CONV

05 KEEPING RECORDS - KEP REC

06 CULLING - CUL

07 BREEDING - BREE

08 MEASURING PRODUCTION - MEAS PROD
09 NECESSARY - NBC

10 PROFIT - PROFIT

ll INEXPENSIVE - INEX

12 COMPUTERS - CMPTRS

13 USEFUL -USFL

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n 8.4.8: mm amusing mp2: m .85 maps mA xwfinvoum
6%: 02 mToAA Axum $88 SA A 3.2.85 84 AA A
3A5 mm Ammsuuafi 3A5 o 85 BE: om um» 8%
:85 8m SAAA $80.85 8a A A A $80.03 cum m A A
mug mm uflmoumjgoo 280
:85 8« SA A 3A5 A 82.35.58 m A A 325 m 88:85 8a A A A
macaw“: 9880 En.
@8950 958 8&8: 958.8 E828 gm? @6050 E828 8&8:

 

mags

mafia

H5495

703.293 m.m manna.

CHAPTER IV

DISCUSSION

These analyses show that DHIA is in a difficult posi-
tion within the two groups, discontinuers and non-adopters.
While adopters see DHIA as close to their own position, they
see computers as very far from themselves. It seems that
much of the dissatisfaction with DHIA is tied to the com-
puterization of the record-keeping device. This study, and
previous research by Houghaboom and Kucker, suggests that
the reason for this dissatisfaction is a lack of the dairy-
men’s understanding of exactly what information is available
from the computerized print-out they receive as a result
of enrolling in the testing program. The impersonal, even
intimidating, appearance of a computer print-out to an
individual who has never seen a print-out before, or has
never had the print-out explained in detail, could in and
of itself be a strong barrier to adoption. As discussed in
Chapter I, previous research has shown that the computerized
results of the test has caused many farmers to discontinue
enrollment in DHIA programs.

Much of the dissatisfaction with DHIA seems to stem
from concerns with accurate information and convenience.

If the information is in a form which is inconvenient,
48

49
confusing or incomprehensible, then any series of messages

which attempts to convince dairymen that the opposite is
true will only prove to further entrench the already nega-
tiveattitudes held toward DHIA.

Since economic factors frequently turned up, i.e. the
concepts profit and inexpensive, these concepts were in-
cluded in most of the best three concept messages. The
hard facts of doing business in a highly competitive market
demands that any innovation which may be adopted be profit-
able and/or inexpensive. These two concepts are very
similar in light of the impact of an innovation upon a small
business. This is so much the case that an innovation that
presents itself to the operator, if not comprehensible in the
short term, will not be adopted in the long term.

An advertising prospectus was presented to Meadows and
DHIA in the spring of 1978. The prospectus included two
taped radio editorials, three advertisements, six print
editorials, direct mail brochures and milk check stuffers
(brochures).

While many other considerations not within the scope
of this research must enter the agency's final decision, the
data collected in this research indicate that in addition
to the persuasive messages in the advertising prospectus
above, the best strategy which comes out of the findings has
three requirements. (1) Either the computer print-out must

be changed in some way to make it more personal; (2) an

so
extensive educational campaign needs to be initiated to

educate the dairymen of Michigan as to how the information

on the print-out can be used and what additional informa-
tion may be gleaned from a perusal of the data available;

and (3) an extensive interpersonal and media campaign

should be launched, to alert Michigan dairymen of the ef-
forts and results of the efforts to make DHIA more convenient
and profitable, and to provide the dairymen with the ac-
curate information that is required for the successful
operation of his farm.

Furthermore, since the record keeping innovation must
be computerized for the processing of large amounts of re-
dundant information, it may be necessary to generate mes-
sages to reduce the distance or amount of heterophily,
between the dairymen of Michigan and computers.

As analysis of the significant other data indicates a
further recommendation of informing local bank loan officers,
if not bank presidents, of the obvious economic advantages
of enrollment in DHIA. The above strategies should increase
enrollment in DHIA, by decreasing heterophily between DHIA
and Michigan's dairymen. The three requirements outlined
in Chapter I have been met; to find a practical solution to
the heterophilic relationship, identify opinion leaders,
and as a result of the above two resolutions increase the

rate of adoption of DHIA.

51
It is also recommended that annual measurements be

taken of the dairymen's attitudes toward the innovation,
concurrently and following any campaign to attract new
adopters. This should be accomplished with two goals in
mind: (1) to add to the body of knowledge being accumulated
of how social and sub-cultural groups respond to communica-
tion regarding any innovation; and (2) to be able to discern
any changes in the attitudes of Michigan dairymen toward
DHIA and the subsequent change in message strategies which
would result in the continued increase of adoption of this
important innovation. Such an effort as outlined above
will have an almost guaranteed likelihood of success at
this time.

Finally, as to further research, this researcher
feels it is imperative to continue the Galileo type metric
multidimensional scaling research in the area of American
agriculture, as the most feasible way of collecting data on
diffusing agricultural innovations to the American farming
community. Any tool which enables a greater amount of pro-
duction with a reduced energy input will tend toward a

better situation for the farmer and the consumer.

APPENDIX A: THE PRODUCT

l-A The BARN SHEET (data received by
the dairyman from DHIA) for the
M.S.U. Kellogg Guernsey herd

52

 

5 5.5:. . I... 55
g .25 _ _- 5......5...5.. . . 5 5fiwqafir: 55:155....5 .5555:

.~._ Z<O.ZU_2..: . ; 3.”...5. -_...¢.5..._.._. . ._.=.........__.. 3:28: : _r§s53o._duul3w_.

I: l .13.}.5... l..£l...ll!x..7!

.

APPENDIX B: QUESTIONNAIRES

‘1977 Galileo Questionnaire

(complete with cover letter)

1977 Semantic Differential Questionnaire
(cover letter same as A above)

1978 Galileo Questionnaire
(complete with cover letter)

523

COOPERATIVE EXTENSION SERVICE
ammo.“ sun L'xn'rusny and
LS 031’“me or AGRICL‘lTL‘RE coopuunxc.

 

DIPARl\II\1 OF DAIR) SCIhSC! EASI LAVSI\C ' MICHIGAN ' 48824

March 25. 1977

Dear Sir:

The Department of Dairy Science at Michigan State University spends most of its
research time investigating methods of improving profit on the dairy farm. When
useful information is found it must be brought to you for application.

A major problem for us has been to communicate with you. To improve our system of
communication we have enlisted the help of other departments on the campus. The en-
closed questionnaire is part of the effort.

We would appreciate very much if you would complete the enclosed questionnaire. The
information will assist us in doing a better job for you.

Sincerely,

flat?- W5

Clinton E. Meadows
Extension Specialist

CEH/lb
Enc.

 

54

March 22

1. What is your age?

2. Are you3married?

3. What was the last year of school you completed?
4. How many years have ng_been farming?

5. How many years have !Qg_been dairy farming?

6. Did you grow up on a dairy farm?

7. Have you always famed in Michigan?

8. What is the total number of acres you operate?
9. How do you market your milk"

10. What percent of your labor is hired?

11. How would you describe your dairy operation?
Circle those which suit your operation.

12. What was the approximate average production per
cow last year in pounds of milk?

13. How many cows do you milk?
14. Has your herd ever been on test?
15. Is your herd on a milk test program now?

16. which testing program are you now enrolled in?
Circle one.

17. How long has your herd been on test?

13. Have you always been on the present form of
testing?

7 .
. /{"n. 5 )lt/zv'I/IJL‘,
ClThron E. Meadows

Extension Specialist

 

sab‘é‘ . '
h #5-“

 

 

 

 

 

 

 

 

Grade "AV Grade "B"

 

O‘U'ibblNH
e e e e e e

Stanchion barn

Stanchion 8 free stalls

Stanchion 6 loose housing
tanchion 6 parlor

Parlor 6 free stalls

Parlor 5 loose housing

 

 

 

 

U‘bblNl-J

DHIA

DKIR

Owner sampler
Tri-monthly testing
Private test

 

 

55

We would like you to give us Eur inion about some ideas related to dairy farm-
ing. You can help by c arin ese 1' Eas to each other to tell 1‘9! different
or $3}; art they are. or example, we might ask, "How different are 351' 11 farm-
€31; an m far-mine?" You could answer with a numb_e£. If the two ideas are very
ferent, You wo write a large number. If the? are very similar, you woul

“

write a small nunber. If they are i' d'e'nti' cal, you would write zero (no difference).

 

To help you judge how lar .e the differences are. we'll say that the amount 9:
difference between15iry gaming and c farming is _1_9_0_ units. Try to Feep this
erence in mind when compaFing the 0 er pairs of words. If two words are
further apart than crop farming and dairy farming, write a number lar'ver than 100.
If they are twice as far apart, write 239, and so on. YOU MAY WRI ~ NUTIBEQ

YOU WANT. Renter-Be. r, there are no wrong answers, only your 92inion.

1. How far apart are DAIRY FARMING and CROP FARE-1126? 100 units
2. How far apart are the EX'I‘EJSION SERVICE and MICHIGAN STATE U.? units
3. How far apart are the 31345102: SERVICE and YOU? units
4. How far apart are IIICHIGA’C STATE UNIVEFSI‘I'Y and YW? units
5. How far apart are the AVERAGE FAR: ER and YOU? units
6. How far apart are ACCURATE INFOR-l-‘\TICY§ and YOU? units
7. How far apart are GOOD and YOU? units
8. How far apart are COT-NESIEIH‘ and YOU? units
9. How far apart are KEEPING RECORDS and YOU? units
10. How far apart are CULLlI-IC and YOU? units
11. How far apart are BREEDING and YOU? units
12. How far apart are MEASURING PRODUCT IO.\' and YOU? units
13. How far apart are I‘CECIESEMRY and Y. '? units
14. How far apart are PROFIT and Y U? units
15. How far apart are INEXPDISIVE and Y0]? units
16. How far apart are C(IerTEI‘S and YOU? units
17. How far apart are USEFUL and YOU? units

18. How far apart are DHIA PRODUCTION TESTING SERVICE
and YOU? units

56

QType 1

19. How far apart are ACCURATE INPORl-MTION and (DOD? units
20. How far apart are ACCURATE INTORHATION and oomrENIEmCE? units
21. How far apart are ACCURATE INFORIATION and KEEPING RECORDS? ____units
22. How far apart are ACCURATE INFORMATICT: and CULLING? __units
23. How far apart are ACCURATE INFORMATIOJ and BREEDING? units

24. How far apart are ACQIRATE INWRI‘IATIM and IEASURING PMDJCI‘ION? units

25. How far apart are ACCURATE INFORMATION and NECESSARY? ___units
26. How far apart are ACCURATE INPOR' IATION and PROFIT? ___mits
27. How far apart are ACCURATE 133mm SATION and INDIPDISNE? ____units
28. How far apart are ACCURATE ream-mow and commas? ____units
29. HON far apart are ACCURATE INFORM-film and USEFUL? __units
30. How far apart are ACCURATE HIDEZATION and DHIA PRODUCTION

TESTII‘JG SERVICE? 5mm
31. How far apart are GOOD and common? __units
32. “0): far apart are GOOD and mm RECORDS? ___units
33. How far apart are GOOD and CULLING? 5mits
34. How far apart are coon and BREEDING? 5mm
35. How far apart are GOOD and MEASURING PRODUCTIOTC? ____units
36. not far apart are moo and NECESSARY? 5mm

37. Who do you USUALLY SPEAK WITH when you need INER‘ IATION ABGJT FARl-IING?
Name 6 Address

 

 

38. Wno do you USUALLY SPEAK WITH when you need INFORE 'ATION ABOUT HERD PRODUCT IO???

 

 

39. who do you USUALLY SPE‘J: 1.1T}! when you need 1211303? 'ATIOIx' ABOUT KEEPING RECORDS?

 

 

40. I'r'ho do you USUALLY SPEAK WITH when you need INFOIURTION ABCUT Inf-IEY 5 FIXA‘ICES?

 

 

 

57

 

 

 

 

 

 

QType 2
19. How far apart are (Inn and PROFIT? units
20. How far apart are (XIII) and IWENSIVE? units
21. How far apart are (DOD and WINTERS? units
22. How far apart are (DOD and USEFUL? _____units
23. How far apart are MID and mm PRODUCTION TESTING SERVICE? “MT-5
24. How far apart are CCNVENIEMZE and KEEPING RECORDS? units
25. How far apart are CONVENIENCE and CULLING? units
26. How far apart are CONVENIENCE and BREEDING? units
27. How far apart are CONVEJIENCE and MEASURING PRODUCT ION? units
28. How far apart are COhl'E‘iIENCE and NECESSARY? units
29. a: far apart are (DINETTE-DICE and PROFIT? units
30. How far apart are COAT’BIIEVCE and IlmE‘JSIVE? units
31. How far apart are (INVE‘EIE‘JCE and (IIIPUTERS? units
32. How far apart are COIR'ENIEVCE and USEFUL? units
33. How far apart are COIWEJEECCE and DHIA PRODUCTION TESTING SERVICE? units
34. How far apart are KEEPING RECORDS and CULLING? Wilts
35. How far apart are KEEPING RECORDS and BREEDING? units
36. How far apart are KEEPING RECORDS and l—IEASURING PRODUCTION? units
37 Who do you USUALLY SPEAK WITH when you need IIJFORl-IATION ABGJT FAR‘IING?

Name 5 Address
38. mo do you USUALLY SPEAK WITH when you need INFORMATION ABOUT HERD PROIUCT 10X?
39. Rho do you USUALLY SPEAK WITH when you need IATOIC-MTIOI.‘ ABOUT KEEPING RECORDS?
4'3 ‘.'.ho do you USUALLY SPEAK WITH when you need 12m HTION ABOUT TDXEY 5 FIXRVCE?

 

 

 

58

(Dyna 3
19. How far apart are KEEPING RECORDS and NECESSARY? ____units
20. How far apart are KEEPING RECORDS and PROFIT? ___.\mits
21. How far apart are KEEPING RECORDS and INEIC>ENSIVE? ____units
22. How far apart are KEEPING RECORDS and COIPUIERS? ___units
23. How far apart are KEEPING RECORDS and USEFUL? 5mit5
24. How far apart are KEEPING RECDRDS and mm PROTECTION TESTING

SERVICE? 5mm
25. How far apart are CULLING and BREEDING? 51mm?
20. How far apart are CULLING and MEASURING PROUICIIOR? ___units
27. How far apart are CULLIRG and NECESSARY? ___Unit5
28. now far apart are CULLING and PROFIT? 55mm
29. How far apart are CULLING and INEJU’BISNE? ____units
so. How far apart are CULLING and CQIPUTERS? ___unit5
31. How far apart are CULLING and USEFUL? ___units
32. How far apart are CULLING and DHIA PROIIJCTION TESTING SERVICE? ___units
33. How far apart are BREEDING and IEASLRING PRODUCTION? __units
34. How far apart are BREEDING and NECESSARY? ___units
35. How far apart are BREEDING and PROFIT? __units
36. HOT! far apart are BREEDIRG and INEIIPENSIVE? ____units

37. Who do you USUALLY SPEAK WITH when you need INFORMATION ABWT PAR-1136?
Name 8 Address

 

 

38. Lho do you USUALLY SPEAK WITH when you need mFOICEATlas' ABOJT HERD PROWCTIO???

 

 

U!
‘9

‘32'330 do you USILAILY SPEAK WITH when you need LN'POMIATION ABOUT KEEPIRG RECORDS?

 

 

40. Who do you USUALLY SPEAK WITH when you need IIIFOR‘IATION ABCUT IDICEY 5 FIRANCE?

 

 

 

59

073/99 4

 

 

 

 

 

 

19. How far apart are BREEDING and COTPUTERS? units
20. How far apart are BREEDING and USEFUL? __m115
21. How far apart are BREEDING and DHIA PRODUCTION TESTING SERVICE? ___units
22. How far apart are MEASURING PRODUCTION and NECESSARY? ___units
23. How far apart are MEASURING PK‘wCIION and PROFIT? ' ___units
24. How far apart are MEASURING PRODUCTION and INEXPENSIVE? ___units
25. How far apart are FEASURING PRODUCTION and COMITERS? _____units
26. rm far apart are MEASURING PRODUCTION and USEFUL? ___mits
27. How far apart are MEASURING PRODUCTION and DHIA PRODUCTION TESTING
SERVICE? ___mit5
28. How far apart axe NECESSARY and PROFIT? _____unit5
29. How far apart are NECESSARY and LNEXPENSNE? ”units
30. How far apart are NECESSARY and COTPUTERS? _____unit5
31. How far apart are NECESSARY and USEFUL? units
32. How far apart are NECESSARY and DHIA TRODUCIION TESTING SERVICE? units
33. How far apart are PROFIT and INEIOJENSIVE? units
34. How far apart are PROFIT and CG-‘PUI'ERS? units
35. How far apart are PROFIT and USEFUL? ___‘mits
36. How far apart are PROFIT and DHIA PRODUCTION TESTING SERVIa? ___units
37. How far apart are IrmENSIVE and COTPUTERS? ___unit5
38. How far apart are INEXPENISIVE and USEFUL? units
39. How far apart are INEG’E‘JSIVE and DHIA PRODUCTION TESTING SERVICEL____mit5
40. How far apart are COFL'IERS and USEFUL? units
uTuts

 

 

41. How far apart are CORNERS and Him PRODUCTION TESTING SERVICE?

42. who do you USUALLY SPEAK WITH when you need INFOMATION ABQJT FARIING?
Name 5 Address

 

 

43. Who do you USUALLY SPEAK WITH when you need INFOMIATION ABCUI' HERD PRODUCTION?

 

 

44. Who do you USUALLY SPEAK WITH when you need II‘IFOR‘LATION ABUJT KEEPING RECORDS?

 

 

45. Who do you USUALLY SPEAK WITH when you need INFORMATION ABUJT IDNEY E FINAVCES'.’

 

 

 

6()

March 22

. What is your age?

--.-.-

 

Are you married?

 

 

1
2.
3. What was the last year of school you coupleted?
4.

Ho»: many years have Y_Ou been farming?

 

 

5. How many years have .Yfl‘. been dairy farminn?
C

Did you grow up on a dairy farm?

 

Have you always farmed in Michigan?

‘3

 

‘.~"nat is the total number of acres you operate?

 

9. How do you market your milk? Grade "A" Grade "B"—

10. 1'.th percent of your labcr is hired?

 

11. How would you describe your dairy operation?

Stanchion barn

Stanchion 8 free stalls
Stanchion 6 loose housing
Stanchion 6 parlor
Parlor & free stalls
Parlor 6 loose housing

 

 

 

 

Circle those which suit your operation. 1.
2.
3.
4.
S.
6.
12. {That was the approximate average production per
cox: last year in pounds of milk?
13. How many cows do you milk?
14. Has your herd ever been on test?
15. Is your herd on a mill; test program now?
16. lfhich testing program are you now enrolled in?
Circle one. 1.
2.
3.
4C
5.

17. How Ion; has your herd been on test?

DHIA

3121?.

Owner samler .
Tri-monthly testing,
Private test

 

13. Have you always been on the present form of
testing?

 

, //.'IL I70' 5 )I'L/f'l/pb‘,

ClThton E. 19adows
Extension Specialist

 

61

We would like you to give us your opinion about some ideas related to Dairy
farming. You can help by compari_fi these ideas to each other to tell how different
or similar they are. For example, we might ask, "How different. or similar, are
Dairy Parmigg_and‘%F22_§gggigg?” If the two ideas are very different then you
could check or 3 t e space to the extreme right, if they are very similar check
the space at the extreme left. If they are between very similar or very diffeeeent
check the appropriate space. The example- is .belov. ' -

CROP PARLIIIIG AND DAIRY FRANING - ' I I I I I I x I
very similar very different

 

1. XIII:SIOII SERVICE AND IIICHIGAI: STATE U.?I I I I I I I I

very similar

very different

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2. EXIEZISIOLI SERVICE AXD YOU? .I I I I ' I I I
very similar very different
3. :JCITIGAI: STATE UNIVERSITY A::D YOU? I I I I I I I
very similar very different
4. AVERAGE PATTI}: AND YOU? I I I I I I
very similar very different
5. ACCURATE Iz'FerczAzIOt-t AND YOU? I I I / I I
very similar very different
a. GOOD AITD YOU? I I I I / I
very similar very different
7. CC1.vE'.:IE::T AID YOU? I I I I I I I
very similar very different
3. RESPI; 3 RECORD: A::D YOL? / I I I I I
very similar very different
9. CULLING AIID YOU? I I I I I I
very similar very different
10. BRIEI'DLZG AITD YOU? I I I I I I I
very similar very different
11. :LEASUIING PRODUCTIOZ? Az'D YOU? I I I_,___ / I I
very similar very different
12. I?£C'~LSF.‘C.‘L’ A'D YOU? / I I____I I I I
very similar very different
:3. PRCIIT A7.) YOU? I I I I -I I
very similar very different
14. I::E:<FE.‘?IVI: AND YOU? I_I I I I I /
very similar very different
15. co: UTILS AITD YOU? I I I I I I I
very similar very different
16. USEFUL A‘fD YOU? I I I I I I I
very similar very different
17. DHIA PRODUCTIOU TESTING SERVICE AND / l / I I /

YOU?

 

very similar

very different

QTYPES

18.

19.

20.

21.

23.

24.

25.

30.

31.

33.

34.

35.

ACCURATE IZIFORIIATION
ACCURATE INFORMATION
ACCURATE INFORI'ZATION
RECORDS?

ACCURATE INFORMATIOIJ

. ACCURATE INFORMATION

ACCURATE IRI’CRIZLTICN
FRODUCTI ON ?

ACCURATE INFOR? IATION

ACCURATE IIIFOREIATIOII

. ACCURATE. IIIFOREZATIOIJ

ACCURATE IZIFORIIATIOI‘I

. ACCURATE INFORMATION

. ACCURATL IZIFORI-EATIOI'E

TION TESTIIYG SERVICE?

GOOD A211) CON‘TNII'NCE?

GOOD AI’D KEEPING RLCORDS?

. GOOD AND CULLING?

GOOD Alt") BREEDII‘YG?

AND

AND

AND

AHD

AND

AND

AND

AND

AND

GOOD?

COZNEIIEZZCE?

KEEPING

CULLING ?

BRECDING?

MEASURING

NECESSARY 7

PROFIT?

INEXPENSIVE?

COMPUTERS ?

USEFUL?

DHIA PRODUC-

GOOD AZID IZLASURINC PRODUCTIOlE?

GOOD I‘d-ID NECESSAR'?

62

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

I_I I I I I I
very similar very different

I /__/ II I I I I
very similar very different
/ I I I I I I I
very similar very different
I I I I I I I I
very similar very different
I I I I I I I I
very similar very different
I I I I I I I I
very similar very different
I I / I / I / I
very similar very different
I I I I I I I I
very similar very different
I I I I I I I I
very similar very different
I I I I I I I I
very similar very different
I I I I I
very similar very different
I I I I I I I /
very similar . very different
I I I I I I I I
very similar very different
I I I I I I / I
very similar very different
I I I I I I / I
very similar very different
I / I I I I I I
very similar very different
I I I I I I I I
very similar very different
/ / I I I / / I

 

very similar

very different

QTYPE 6

19.
50.
21.
22.
23.

24.
25.
26.
27.
28.
29.
30.
31.
32.
33.

GOOD AND PROFIT

GOOD AND INEXPENSIVE

GOOD AND COMPUTERS

GOOD AND USEFUL

GOOD AND DHIA PRODUCTION TEST

SERVICE

CONVENIENCE AND
CONVENIENCE AND
CONVENIENCE AND
CONVENIENCE AND
CONVENIENCE AND
CONVENIENCE AND
CONVENIENCE AND
CONVENIENCE AND
COINEI‘31EZICE AND

cozwmtxtrztt m:
rasrrwc SERVICE

KEEPIJC RLCOFC‘.
KEEPI L TC RECORDS

KEEPING RECORDS
PRODUCTION

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

63
very vvry
similar different
I I I I I I I I
try similar very different
I I I I I I I I
very similar very different
I I I I I I I
very similar very different
I I I I I I I
very similar very different
26 / I I I I I /
very similar very different
KEEPING RECORDS I I I I I I /
very similar very different
CULLING I I I I I I /
very similar very different
Bantams / I I I I I I
very similar very different
:zmscmtm PRODUCTION I / I I I
very similar very different
NECESSAR' I I I I
very similar very different
PROFIT I I I I I I I
very similar very different
IFEIPEXSIVE /____I I I I / I
very similar very different
00:31:79.: I I I I I I I
very similar very different
USEFUL I I I /
very similar very different
mm. mower: :: I I I I I I
very similar very ifferent
Ian CULLING I I. _ _I I I I I
very similar very different
A2?!) BREEDILIG I___I____,I I
very similar very different
AND MEASURIZG I I I I I I I

 

64

 

 

 

 

 

Q TYPE 8
var, similar
55. BREEDING AND COID’UTERS / Ivery/différent/
very similar very different
56. ammo AND USEFUL I I I I I I
very similar very different
57. DREEDING AND DHIA PRODUCTION / I I I I
TESTING SERVICE
very similar very different
58. MEASURING PRODUCTION AND UBC- ' I I / I I I I
ESSARY
very similar very different
59. nasuamc nomenon AND mom I I I I I I I
very similar very different

60. msmsc PRODUCTION AND nmmsxv: I I I I I I I

61.
62.
63.

66.
65.
66.
67.
68.

69.
70.
71.
72.

73:
74.
75.

76.
77.

MEASURING PRODUCTION
MEASURING PRODUCTION

MEASURING PRODUCTION AND DHIA
PRODUCTION TESTING SERVICE

NECESSARY AND PROFIT
NECESSARY AND INEXPENSIVE
NECESSARY AND COIQUTERS
NECESSARY AND USEFUL

NECESSARY AND DHIA PRODUCTION
TESTING.SERVICE

PROFIT AND INEXPENSIVE
PROFIT AND COMPUTERS
PROFIT AND USEFUL

PROFIT AND DHIA PRODUCTION
TESTING SERVICE

INEXPENS IVE AND COMPUTERS

INEKPENSIVE AND USEFUL

INEXPENSIVE AND DBIA PRODUCTION

TESTING SERVICE
COMPUTERS AND USEFUL

COXZPUTERS AND DHIA PRODUCT ION
TESTING SERVICE

AND COMPUTERS

AND USEFUL

very similar

very different
I I

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

I I I I
very similar very different]
I I
very similar very different
I I I I I
very similar very different
I I I I__ I I
very similar v y i
I I I I I I
very similar very different
I I I I I I I
very iimdlar very different
I I I I I I I
very similar very different
I I I I I I I
very similar very different
I I I I I I I
very similar very different
I / I I I I
very similar very different
I I I I I I
very similar very different
I I I I I I
very similar very different
I I I I
very similar very different
I I I I I
very similar very different,
I
very similar very different
I I
very similar very different
I I / I

 

65

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Q TYPE 7
very similar very different
37. KEEPING RECORDS AND NECESSARY / I I I I I I
very similar very different
38. REEPIz-zr: RECORDS AND PROPII I___I I I I
very similar very different
39. KEEPING RECORDS AND INEXPENSIVE I I I I I I I
very similar very different
40. KEEPING RECORDS AND COMPUTERS I I I I I / I
very similar very different
1.1. KEEPING RECORDS AND USEFUL I I I I I I I
very similar very different
4.2. KEEPIN RECORDS AND DHIA PRODUCTION I I I I I I I
TESTING
- very similar very different
43. CL'LLI‘IG AND EREEDI;:5 I I I I I I
very similar very different
(.4. CULLI‘SG AND IEASURILIG PRODUCIIczz I I I I I I I
very similar very different
45. CULLING um NECESSARY I I I I I I
very similar very different
46. cause AND PncrII I I I I I I I
very aifiilar very different
47. CULLIRC AND INEXPE‘.:SI'~'E I I I I / I /
very similar very different
45. CULLIEZG 54.- GUI-{PETERS I I I I I I I
very similar very different
(.9. CULLIJG 1.1m USEI‘L'L I I I I I
very similar very different
so. CULLING AND DHIA PRODUCTIO? IESI- I I I I I I I
INC SERUIC:
very similar very different
51. DREEDIRO A133 :msunzxc PRODL-C'rmn I I I I I I
very similar very different
52. DEEEDIEO AND NECESSARY I I I / I I
very similar very diffeICEE'
53. BREED-111C. 1m) PRCTIT I,_._I____I I I I___I
very sinilar very different
5;. ERELDI;:C AND IzcExPE;:SI'cE I I I I I I I

 

66

41. who do y... USUALLY SPEAK Rm: when you need INFORMATION ABOUT EARNING?
Name 8 address

 

 

42. Who do you USUALLY SPEAK VITH Vhen you mchL‘JPORKTION ABOUTNERD PRODUCTION?

 

 

1.3.Nho do you USALLY SPEAK WIT}! when you need INP'K.‘-iATIOI-z ABOUT KEEPING RECORDS?

 

 

44. Who do you USUALLY SPEAK WITH when you need INEOI’MION ABOUT MCNEY 6 FINANCES

 

 

67

COOPERATIVE EXTENSION SERVICE
MICHIGAS STATE UNIVERSITY and
Us. DEPARTMENT or AGRICULTURE COOPERATING

 

IMPAIRMENT a DUI? SCIENCE EAST LANSING ' HKHICAN ' ‘34

"December 1, 1977

Dear Sir:

The Department of Dairy Science at Michigan State University spends most of its
research time investigating methods of improving profit on the dairy farm. When
useful information is fbund it must be brought to you for application.

A major problem fer us has been to communicate with you. To improve our system of
communication we have enlisted the help of other departments on the campus. The en-
closed questionnaire is part of the effort.

We would appreciate very much if you would complete the enclosed questionnaire. The
information will assist us in doing a better job for you.

Sincerely.

W. W...-

Clinton E. Meadows
Extension Specialist

GEM/lb
Enc.

 

10.

ll.

12.

13.

1“.

15.

16.

17.

18.

19.

68

Today's Date

 

what is your age?

 

Are you married?

 

What was the last year of school you completed?

 

How many years have ng_been farming?

 

How many years have :22 been dairy farming?

 

Did you grow up on a dairy farm?

 

have you always farmed in Michigan?

 

What is the total number of acres you operate?

 

How do you market your milk? Grade "A" Grade "BU_____

What percent of your labor is hired?

 

How would you describe your dairy operation?

Circle those which suit your operation. I.
2.
3.

6.

what was the approximate average production
per cow last year in pounds of milk?

Stanchion barn

Stanchion 8 free stalls
Stanchion 8 loose housing
Stanchion 8 parlor
Parlor 6 free stalls
Parlor 8 loose housing

 

How many cows do you milk?

 

Has your herd ever been on test?

 

Is your herd on a milk test program now?

 

which testing program are you now enrolled
in? Circle one. 1-
2.

how long has your herd been on test?

DHIA

DHIR

Owner sampler
Tri-monthly testing
Private test

 

Have you always been on the present form
of testing?

 

69

INSTRUCTIONS

He would like you to give us your opinion about some ideas related to dairy
farming. You can help by comparing these ideas to each other to tell he:
Sifiiggggg or §§£_§2351 they are. For example, we might ask, "How different
are dairy farming and crop farming?" You could answer with a number. If

the two ideas are very different, you would write a large number. If they

 

are very similar, you would write a small number. If they are identical,

you would write zero (no difference).

To help you judge how large the differences are,.we'll say that the amount

of difference between dairy farming and crop farming is 100 units. Try to

 

 

keep this difference in mind when comparing the other pairs of words. If

two words are further apart than crop farming and dairy farming, write a

 

number larger than 100. If they are twice as far apart, write 200, and so
on. YOU MAY WRITE ANY NUXBER YOU WANT. Remember, there are no wrong answers,

only your opinion.

ID”

 

CARD #00 7-8

2021
2223
2202
2902

IDh

0102
0103
010a
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0105
0107

12?
C123

1-6

09-17
15-26
27-35
as-uu

1-6

#01 7-8

09-17
16-26
27-35
36-uu
«5-53
Su-62
63—71
72-80

Duplicate 1-6

How far apart are

7()

Dairy Farming and Crop Farming

Extension Service and Nichigan State Univ.

Extension Service and You
The Average Farmer and You

Units

100

 

How far apart are

Accurate
Accurate
Accurate
Accurate
Accurate
Accurate
Accurate
Accurate

Information
Information
Information
Information
Information
Information
Information
Information

You
Good

Convenient
Keeping Records

Culling
Breeding

Measuring Production
Necessary

Units

 

 

 

CARE 502 7-8

0110
0111
0112
0113
Ollu

0203
020a
0205

09-17
18-26
27-35
36-uu
M5-53

5H-62
63-71
72-80

How far apart are

Accurate
Accurate
Accurate
Accurate
Accurate

Testing

Information
Information
Information
Information
Information

You and Good
You and Convenient
You and Keeping Records

Profit

Inexpensive
Computers

Useful

DHIA Production

Units

 

REMEMBER:

ID# l-6
CARD #03 7-8
0206 09-17
0207 16-26
0208 27-35
0209 36-uu
0210 k5-53
0211 5H-62
0212 63-71
0213 72-80

Duplicate 1-6

71

-2-

DAIRY FARMING AND CROP EARNING ARE 100 UNITS APART.

How fargapart are units

You
You
You
You
You
You
You
You

and
and
and
and
and
and
and
and

Culling

Breeding

Measuring Production
Necessary

Profit

Inexpensive
Computers

Useful

 

 

CARD #ou 7-8

021k
030k
0305
0306
0307
0306
0309
0310

Duplicate 1-6

09-17
15—26
27-35
ao-au
u5-53
54-62
63-71
72-80

H

or f

ar apart are Units

Ycu and LKIA Production Testing

Good
Good
Good
Good
Good
Good
Good

and
and
and
and
and
and

and

Convenient

keeping Records
Culling

Breeding

Heasuring Production
Necessary

Profit

 

 

CARD #05 7-8

0311
0312
0313
031%
0305
0H06
0&07
0H08

09-17
18-25
27-35
36-uu
HS-53
Su-62
03-71
72-80

How far apart are

Good and Inexpensive
Good and Computers
Good and Useful

Units

Good and DHIA Production Testing
Convenient and Keeping Records
Convenient and Culling

Convenient and Breeding

Convenient and Measuring Production

'72

 

 

 

Q Type l

REHEMBER: DAIRY FARMING AND CROP FARMING ARE 100 UNITS APART.
IDfi 1-6
CARD $06 7-8

How far apart are Units
0u09 09-17 Convenient and Necessary ________
Oulo 18-26 Convenient and Profit ________
Dull 27-35 Convenient and Inexpensive _________
0A1? 36-uu Convenient and Computers ________
0kl3 #5-53 Convenient and Useful _________
Oulu Su-62 Convenient and DHIA Production Testing _________
0506 53-71 Keeping Records and Culling ‘________
C507 72-80 Keeping Records and Breeding '________
Duplicate 1-6
CARE #37 7-8

How far apart are Units

Duplicate 1-6

09-17
18-26
27-35
35-1“;
U‘-S3
5h-62
63-71
72-80

 

Keeping
Keeping
Keeping
Keeping
Keeping
Keeping
Keeping
Culling

Records
Records
Records
Records
Records
Records
Records

Measuring Production
Necessary

Profit

Inexpensive

Computers

Useful

DHIA Production Testing

and Breeding

lllllH

 

 

CARD MOS 7-8
053: 09-17
0609 19-26
06l0 27-35
0011 3L-uu
0612 u5-53
0613 5H-62
OGlu 63-71
0708 72-80

How far apart are

Culling
Culling
Culling
Culling
Culling
Culling
Culling

and

measuring Production

and Necessary
and Profit

and
and
and

ul

Inexpensive
Computers
Usef

and DHIA Production Testing
Breeding and Measuring Production

Units

 

REMEMBER:

ID #

CARD #09 7-8

DAIRY FARMING AND CROP FARMING ARE 100 urms APART: Q Type

1-6

73

How far apart are

Breeding and Necessary

Breeding and Profit

Breeding and Inexpensive

Breeding and Computers

Breeding and Useful

Breeding and DHIA Production Testing
Measuring Production and necessary
Measuring Production and Profit

2

Units

| !

 

 

0709 09-17
0710 18-26
0711 27-35
0712 36-MU
0713 u5-53
071M 5h-62
0809 63-71
0810 72-80
Duplicate 1-6
CARD #10 7-8
0811 09-17
Cd12 18-26
08l3 27-35
081“ 36-Qk
0910 45-53
0911 Sk-C2
0912 63-71
0913 72-80

Duplicate 1-6

How far apart are

Measuring Production and Inexpensive
Measuring Production and Computers
Measuring Production and Useful

Measuring Production and DHIA Production

Testing
Necessary and Profit
Necessary and Inexpensive
Necessary and Computers
Necessary and Useful

Units

 

 

CARD #11 7-3
091a 09-17
1911 18-26
1012 27-35
1013 36-uu
101“ “5-53
1112 Su-62
1113 63—71
lllu 72-80

How far apart are

Necessary and DHIA Production Testing
Profit and Inexpensive

Profit and Computers

Profit and Useful

Profit and DNIA Production Testing
Inexpensive and Computers

Inexpensive and Useful

Inexpensive and DHIA Production Testing

Units

llHlll

 

74

Q Type 2-Cont.

Duplicate 1-6

CARD r12 7-8

 

How far apart are Units
1213 09-17 Computers and Useful _________
121“ lb-26 Computers and DHIA Production Testing
1314 27-35 Useful and DHIA Production Testing _________

 

An

B—

APPENDIX C: STATISTICS

Discriminate Analysis
(stepwise procedure)

Galileo Means, Standard Deviations,
Standard Errors, Skewness, Kurtosis,
Count, Minimum-Maximum Values,
Percent Error, Galileo Means Matrix,
Coordinates for the Multidimensional
Space for Data Sets I, II and III

as well as Data Set Three Split Three
Ways by Adopter Category

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