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Attitude-behavior relationships: A comparison ofthe Fishbein-Ajzen and Bentler-Speckart modelsArlene J. FredericksUniversity of Nebraska at Omaha

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ATTITUDE-BEHAVIOR RELATIONSHIPS: A COMPARISON OF THE FISHBEIN-AJZEN

AND BENTLER-SPECKART MODELS

A Thesis

Presented to the

Department of Psychology

and the

Faculty of the Graduate College

University of Nebraska

In Partial Fulfillment

of the Requirements for the Degree

Master of Arts

University of Nebraska at Omaha

by

Arlene J. Fredricks

August 1981

UMI Number: EP72742

All rights reserved

INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted.

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a note will indicate the deletion.

Published by ProQuest LLC (2015). Copyright in the Dissertation held by the Author.

UMI EP72742

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THESIS ACCEPTANCE

Accepted for the faculty of the Graduate College, University of

Nebraska, in partial fulfillment of the requirements for the degree

Master of Arts, University of Nebraska at Omaha.

Thesis CommitteeDepartmentame

Chairman

Date

ii

ACKNOWLEDGEMENTS

The author is indebted to D. Kenneth Spenner of the Boys Town

Center for Youth Development for not only making the LISREL computer

program available, but for his invaluable instruction and guidance that

made use of it possible; Dr. Dennis Dossett for innumerable suggestions,

guidance, editorial assistance, and encouragement; the Committee--

Dr. J. Brad Chapman, Dr. Carl Greenberg, Dr. Gaylon Oswalt, for reading

and helpful comments; Mr. Patrick Jordan for coding data; Instructors

who collected daily attendance data and permitted the use of class time

for questionnaire administration--Mr. David Arnold, Dr. Carl Greenberg,

Mr. William Grisham, Mr. Gary Kinstlinger, Dr. Barbara Manning, Dr. C.

Raymond Millimet, and Mr. Edward Ward; Instructors who permitted use of

class time for pilot sample data collection--Dr. Kenneth Deffenbacher,

Dr. Joseph LaVoie, Mr. Jack Leon, Mr. Russell Montgomery, Dr. Gaylon

Oswalt; and Mr. Jim Smith of the UNO Computer Network for much appre­

ciated technical assistance.

TABLE OF CONTENTS

Page

T.i st. of T a b l e s . ........................................................... vi

List of F i g u r e s ..............................................................vii

Abstract.................................................................... viii

The Attitude-Behavior Relationship....................................... 1

The Fishbein-Aj zen Model.......................................... 3

Conceptual Framework of the Fishbein-Aj zen Model.............. 5

The Fishbein-Aj zen Model of Attitude Formation................ 7

Fishbein and Ajzen’s Specifications for Measurement ......... 9

The Bentler and Speckart Model........................................... 12

Method....................................................................... 17

Subjects..................... 17

P r o c e d u r e ........................................................... 18

Measures............................................................. 20

Analysis............................................................. 22

R e s u l t s ..................................................................... 24

Pilot and Primary Study Sample Comparisons..................... 24

Measurement M o d e l .................................................. 24

Structural Model.................................................... 25

Model C o m p a r i s o n s .................................................. 25

Chi-Square Goodness of Fit Tests................ 34

Chi-Square Difference Tests ...................................... 37

Discussion.................................................................. 40

iv

Page

Reference Note................ 46

References.................................................................. 47

Appendix A--Questionnaire for Pilot Study ............................. 50

Appendix B--Questionnaire for Primary Study ........................... 55

Appendix C--Tables of Results............................................ 69

Appendix D--Discussion of Path Analysis .............................. 82

v

LIST OF TABLES

Table Page

1* Structural Model Specifications.................................... 29

2. Path Comparisons between Models, t-Values, . . . ............. 31

3. Chi-Square Goodness-of-Fit Tests ..................... 35

4. Chi-Square Difference Tests for Model Comparisons................ 38

vi

LIST OF FIGURES

Figure Page

1. Schematic Representation of Fishbein-AjzenModel of Attitude-Behavior Relationship....................... 6

2. Schematic Representation of Fishbein-Ajzen Modeland of the Bentler-Speckart Modification . . . ...............14

3. Fishbein-Ajzen Model (Prior Behavior Excluded)with Paths Labeled for LISREL Analysis ....................... 26

4. Bentler-Speckart Model (Prior Behavior Included)with Paths Labeled for LISREL Analysis ....................... 27

vii

ABSTRACT

This study compared the Fishbein-Ajzen (1975) model of attitude-behavior

relationships with Bentler-Speckart's (1979) modifications of the model.

Subjects were 236 undergraduate college students and the measures of

behavior were repeated self-reports of class attendance. An analysis of

linear structural relationships, using multiple indicators for each under­

lying construct, supported the Bentler-Speckart addition to the Fishbein-

Aj zen model of prior behavior as a direct causal influence on both sub­

sequent behavior and behavioral intentions. However, consistent with the

original Fishbein-Ajzen model, a direct causal path from attitude to sub­

sequent behavioral intentions was not found. Directions for future studies

and respecification of the model were discussed.

viii

1

The Attitude-Behavior Relationship

That attitude can sometimes predict behavior has been documented

and reported in recent reviews of the attitude-behavior relationship

literature (Ajzen § Fishbein, 1977; Calder § Ross, 1977; Eagly £

Himmelfarb, 1978; Kelman, 1974). However, discovery of the conditions

and processes that permit predictions of behavior remains a challenge

for behavioral scientists. This research question must necessarily be

partitioned into a conceptualization of attitudes on the one hand and

behavior on the other. Since behavior is more easily observed and

measured, it is the attitude construct that has attracted the greatest

amount of methodological attention.

Numerous definitions of attitude have been promulgated. However,

the present discussion will be limited to only relatively recent

approaches. For example, Rokeach defined attitude as "an organization

of interrelated beliefs around a common object, with certain aspects of

the object being at the focus of attention" (1968, p. 116).

Triandis (1971) presented a definition which he felt included many

previously developed central ideas as follows: "An attitude is an idea

charged with emotion which predisposes a class of actions to a particu­

lar class of social situations" (p. 2). This definition references

three components of attitude: (a) the cognitive or "idea" component;

(b) the affective or emotional component; and (c) the behavioral or

predisposition to action component. In this context predisposition to

action does not necessarily imply actual behavior. Triandis (1971),

in discussing the attitude-behavior relationship, notes that attitudes

involve:

2

What people think about, feel about, and how they would like

to behave toward an attitude object. Behavior is not only

determined by what people would like to do but also by what

they think they should do, that is, social norms, by what they

have usually done, that is, habits, and by the expected

consequences of the behavior. (p. 14)

Implicit in this definition is the concept of the determination of

attitudes by the cognitive component, beliefs.

Triandis, in a more recent discussion (1979), points out a basic

source of controversy in social psychology; that operationalization and

measurement of a construct is dependent on how the construct is defined.

If attitude is linked to behavior by definition; then, the behavioral

scientists concern is to explore the conditions under which either a

strong or weak relationship between verbal attitudes and behavior are

likely to be observed.

Calder and Ross (1976), in considering the psychological founda­

tions of attitudes, conceive of attitudes as evaluative summaries of

underlying beliefs. According to this view, in order to understand

attitudes, it is first necessary to understand the information structures

or belief systems which underlie attitudes. Secondly, it is necessary

to understand how these beliefs are processed or integrated to produce

the evaluative summary called an attitude.

Some of the major contributions relevant to these issues include

the cognitive consistency approach of Heider's (1946) balance theory

which stresses the function of the perception of consistency in attitude

formation and change. Along the same line of reasoning was Festinger's

3

(1957) theory of cognitive dissonance which examined perceptual incon­

sistencies and modes of reducing the resulting dissonance. Bern (1967,

1972), in an alternate approach, proposed a theory of self-perception

which suggested that behavior might well be an antecedent rather than

a result of attitude in that individuals infer what their attitudes must

be from observation of what their behavior is. Thus, there is a vast

complexity of attitude constructs and attitude-behavior relationships

and a multiplicity of approaches taken in attempting to gain an under­

standing of attitudinal components and their processes.

The theoretical approach focused upon in this study is that

developed by Fishbein (1967) and elaborated by Fishbein and Ajzen (1975).

This theoretical framework has served to integrate much of the currently

accepted attitude-behavior knowledge into a theory that is explicit,

testable, and widely generalizable. It is the application of this

theory in the area of attitudes toward attendance/absenteeism that is

the subject of this study.

The Fishbein-Ajzen Model

Rather than providing a simple definition of attitude, Fishbein

and Ajzen (1975) proposed a conceptual framework systematically integrat­

ing theoretical attitude components and their underlying processes.

They note that various interpretations or definitions of "attitude"

have in the past implied different measurement procedures which conse­

quently produced different results in attitude studies. The subsequent

confusion as to exactly what "attitude" is has been the logical result.

Fishbein and Ajzen (1975) prefer to define attitude by its generally

agreed upon most essential component which is the major characteristic

4

that distinguishes attitude from other constructs, that is, its evalu­

ative or affective nature.

According to this definition, a distinction must be made between

belief and attitude. This distinction implies the testable proposition

that beliefs and attitudes have different determinants and that changes

in them can lead to different consequences. Accordingly, Fishbein and

Ajzen (1975) use the term, "attitude,” to refer to affect, i.e., feelings

toward or evaluation of an attitude object, and the term "belief,” to

represent cognition or knowledge about the object, specifically the

linkage of an object to some attribute. This definition, while basically

the same as Rokeach’s (1968) definition of attitude, emphasizes the

separation of the concepts of attitude and belief.

The third generally recognized component of attitude, the behavior­

al component, is divided in the Fishbein-Ajzen model to refer to both

behavioral intentions and actions with respect to or in the presence of

the attitude object. This distinction between behavioral intentions

and actual behavior is made since most theorists agree that attitudes

are concerned with predispositions to behave rather than with the

behavior itself.

The Fishbein-Ajzen (1975) model, then, is a descriptive framework

of the relationship between four broad categories: affect (feelings,

evaluation), cognition (opinions, beliefs), conation (behavioral inten­

tions), and behavior (observed overt acts). The term "attitude" is

reserved for only one of these categories, affect.

5

Conceptual Framework .of the Fishbein-Ajzen Model

The conceptual framework of the Fishbein-Ajzen model for the rela­

tionship of attitudes to behavior can perhaps best be presented

schematically (see Figure 1}. According to this framework, the perform­

ance or nonperformance of a specific behavior is determined by the

intention to perform that behavior. Consequently, the prediction of

behavior toward an object from knowledge of attitude toward that same

object is accurate only insofar as that attitude influences the intention

to perform the behavior. This behavioral intention is a function of

beliefs, not about the object of the behavior, but instead beliefs con­

cerned with the behavior itself.

A person’s attitude toward performing a given behavior is represented

as a function of two types of beliefs. One of these is that performing

the behavior will lead to certain consequences along with his/her evalu­

ation of these consequences. The other relevant beliefs, labeled sub­

jective norms since they are normative in nature, are beliefs that

certain relevant others think that the person should or should not

perform the behavior in question. Subjective norms are combined multi-

plicatively with the subject’s motivation to comply with these norms.

According to this conceptual structure, beliefs are the fundamental

building blocks, an informational base that is the ultimate determinant

of attitudes, intentions, and behavior. The formation of attitudes,

then, is viewed in terms of an information processing approach wherein

a person’s salient set of beliefs about the object determines his/her

attitude toward that object. Applied to behavior, it is the set of

beliefs as a whole, including behavioral intentions, which are viewed

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as a special case of beliefs, that is the determinant of attitudes in

the attitude-behavior relationship.

The Fishbein-Ajzen Model of Attitude Formation

Fishbein and Ajzen (1975) suggest an expectancy-value model of

attitude formation. The prediction of behavioral intentions is a

function of the weighted sum of two variables, the attitude toward

performing the behavior and the subjective norm as follows:

B 'v I = (A ) + (SN)B''w1 v 'w2

where B is the behavior, I is the intention to perform the behavior,

A is the attitude toward performing the behavior B, SN is the subjective

norm, and w^ and w^ are empirically determined weights. The attitude

toward performing a specific behavior is proposed to be a function of

the perceived consequences of performing that behavior and of the

person's evaluations of those consequences:

nA = L b.e. B . . 1 1 i=l

where b is the belief that performing behavior B leads to consequence

or outcome, i_, e_ is the person's evaluation of outcome i , and n is the

number of beliefs the persons hold about performing behavior B.

The normative component, SN, deals with the influence of the social

environment on behavior. The subjective norm is the person's perception

that people who are important to him/her think he/she should or should

not perform the behavior in question. According to Fishbein and Ajzen

8

(1975), the general subjective norm (SN) is determined by the perceived

expectations of specific referent individuals or groups, and by the

person's motivation to comply with those expectations:

nSN = I b . m .

1-1 1 1

where b^ is the normative belief, m^ is the motivation to comply with

referent jl, and n. is the number of relevant referents.

These two major determinants of behavioral intentions are given

empirical weights in the prediction equation proportional to their

relative importance. Since adequate estimates of these weights for

each individual are not generally available, the accepted practice has

been to use multiple regression techniques and standardized regression

coefficients as estimates of the weights for the theory components.

The present version of the theory, in the form of a multiple regression

equation i s :

i = (3b)ab ♦ ceSN)SN

where I is the behavioral intention.

The component of motivation to comply can be expressed as the

person's intention to comply with the referent in question. The

determinants of this intention are the same as those of any other

behavioral intention and can be expressed in equation form,

m ^ I « (A„) i (SN)— C v CJ w^ v 'w2

9

where m is the motivation to comply with the referent, 1 , is the

intention to comply with the referent , A is the attitude toward comply-Vj

ing with the referent, SN is the subjective norm concerning compliance

with the referent, and w^ and are weights.

The Fishbein-Ajzen (1975) theory accounts for the influence on

intentions of additional variables external to the model only through

their indirect influence on either of the two components (attitude and

subjective norms) or on the relative weights of these components.

Accordingly, the attitude toward the target object or person will be

unrelated to the behavioral intention itself if it is not related to

either the attitudinal or normative component of behavioral intention.

Only if the component variable in question carries a significant weight

in the regression equation predicting behavioral intention will attitude

toward the object be related to or predictive of intentions. It is the

behavioral intention which is considered to be the determinant of overt

volitional behavior. A number of studies are cited by Ajzen and Fishbein

(1977) and Fishbein and Ajzen (1975) in support of this theoretical

formulation.

Fishbein and Ajzen’s Specifications for Measurement

A major factor in the prediction of overt behavior from behavioral

intentions is the necessity for correspondence with respect to the

level of specificity between intentions and behavior and also between

intentions and the components of intentions. Fishbein and Ajzen (1975),

in their development of a framework for linking attitudes and behaviors,

are highly specific as to the measurement methods that they consider

appropriate. Consequently, any study designed to investigate this model

10

risks the possibility of testing constructs other than those designated

if other methods of measurement are employed. The procedure recommended

by Fishbein and Ajzen is to measure attitude "by a procedure which

locates the subject on a bipolar affective or evaluative dimension

vis-a-vis a given object" (Fishbein § Ajzen, 1975, p. 11). This procedure

is an exact definition of the semantic-differential scale (Osgood, Suci,

§ Tannenbaum, 1957).

In contrast to the evaluative nature of attitudes, beliefs represent

information. Differences among individuals in this respect are defined

in terms of belief strength or the perceived likelihood that an object

has or is associated with a particular attribute. The recommended

procedure for the measurement of belief strength, then, places the sub­

ject along a dimension of subjective probability involving an object and

some related attribute. For example, the more money a person is per­

ceived to possess, the higher should be the subjective probability that

the person is wealthy.

Since behavioral intention is conceptualized as a special case of

beliefs, the strength of a behavioral intention is appropriately

measured by a procedure which places the subject along a subjective

probability dimension involving a relation between himself/herself and

some action. For example, the strength of an intention to attend church

on Sunday would be measured by the subject’s probability rating of the

concept, "I will attend church Sunday,” on scales anchored by "probable-

improbable” or "agree-disagree."

According to Fishbein and Ajzen (1975) only a limited number of

salient beliefs can serve as determinants of attitude at any given time.

11

Therefore, measures of beliefs should also include assessment of their

saliency in the subject's belief hierarchy. This can be accomplished

by considering as salient only the first few responses elicited in a

free-response format when subjects are asked for a listing of charac­

teristics, qualities, and attributes possessed by an object or for the

consequences of performing a behavior.

This procedure is similar to content analysis. When a modal set

of beliefs for a population is to be identified, the responses obtained

from a representative sample are first grouped according to similarity

and the frequency of each similar belief is counted. It is necessarily

a matter of judgment as to whether or not similar-appearing beliefs are

to be considered identical. The set of beliefs to be included in the

modal set is then arbitrarily set as the number of beliefs sufficient

to account for a stated percentage, such as 75%, of all beliefs emitted

by the sample. This procedure is described by Ajzen and Fishbein (1980).

By definition, overt behaviors are observable acts. Fishbein and

Ajzen (1975) view behavior as consisting of four elements: behavior,

target, situation, and time. They make a distinction between different

types of behavioral criteria in terms of the variance of the criteria

with respect to one or more of these elements.

A single-act criterion, i.e., the single observation of a single

act, is always specific with respect to the four elements of behavior

as it involves a directly observable response to a specific target, in

a given situation, at a given point in time. A repeated-observation

criterion is an index of behavior derived from repeated observations of

the same behavior, such as observations across several trials in an

12

experiment. Such criteria can represent generalizations across targets,

across situations, or across time. A multiple-act criterion represents

a behavioral index computed from observations of different behaviors

with respect to a given target, in a given situation, at approximately

the same point in time. For example, withdrawal behavior in a social

situation can be measured by the degree of conversational participation,

eye contact, physical distance, and the amount of time spent with a

group. A combination of repeated observations of more than one behavior

would be considered a multiple-act, repeated-observation criterion.

Since behavioral observations are data, such observations can be

subject to the same problems of unreliability and invalidity as any other

form of data. For this reason, Fishbein and Ajzen (1975) note that

rigorous analyses of behavioral data are essential for an understanding

of the relation between attitude and behavior and that inconsistent

research findings from attitude-behavior studies may be due to the use

of inappropriate behavioral measures. They conclude that, in regard to

single-act criteria:

Not every behavior with respect to some object is related to

the attitude toward that object [and that] multiple-act and

repeated-observation criteria . . . when properly constructed

on the basis of standard scaling procedure . . . can serve as

indicants of attitude. (Fishbein § Ajzen, 1975, pp. 356-357)

The Bentler and Speckart Model

Bcntlcr and Speckart (1979) proposed and tested a modification and

extension of the Fishbein and Ajzen model. In the Bentler-Speckart

model, which they consider to be "the most theoretically adequate

13

causal-predictive system relevant to a variety of behavioral domains"

(1979, p. 455), affect (attitude) has a direct effect on behavior in

addition to its indirect influence on behavior by means of its influ­

ence on intention. A second modification is the addition of previous

behavior to the model. This previous behavior is postulated to have an

effect on both current intentions and on future behavior that cannot be

accounted for by the original Fishbein and Ajzen model. A schematic

of these two approaches is presented in Figure 2.

Bentler and Speckart (1979) reason that, since behavioral intention

is conscious and thereby cognitive in nature, the Fishbein-Ajzen theory

which proposes that affect impacts behavior only by means of the regu­

lation of intention or premeditation (conation-cognition) is counter­

intuitive in most domains of behavior, less accurate and generalizable,

and has less predictive power than their conceptualization.

They also propose that the role of previous behavior in accounting

for future behavior is inadequately modeled in the Fishbein-Ajzen

approach of indirect influence through attitudes and subjective norms.

Bentler and Speckart state that previous behavior may "circumnavigate

these factors in its causation of subsequent behavior in the same way

that attitudes circumnavigate intentions" (1979, p. 454). This the­

oretical formulation, whereby behavior has an independent role in the

prediction of future behavior, is consistent with other theoretical

formulations such as Bern's (1967, 1972) self-perception theory in which

attitudes may be generated from self-perceptions of behavior. Other

research findings indicating a relationship between past and subsequent

behavior would appear to be consistent with both the direct and indirect

Schematic Representation of Fishbein-Ajzen Model14

Attitudetoward

Behavior

SubjectiveNorms

BehavioralIntentions

TargetBehavior

Schematic Representation of Bentler-Speckart Modification

PreviousBehavior

BehaviorIntentions

TargetBehavior

Attitudetoward

Behavior

Subjective Norms

Figure 2

15

previous-behavior influence models.

In the Bentler-Speckart (1979) study, three models were tested with

the same data: the two schematically represented in Figure 2 and an

intermediate modification of the Fishbein-Ajzen model. Bentler and

Speckart (1979) collected their data following Fishbein and Ajzen1s

recommended approach of using semantic-differential scales. A sample of

228 college students were asked three questions on each of the constructs

of attitudes, subjective norms, and intentions at one point in time.

Behavior was measured twice within a two-week period. To obtain replica­

tions of the tests for each model, these five variables were assessed

for each of three attitudinal domains: alcohol, marijuana, and hard

drug use. The behavior measures were not observations of behavior but

rather questionnaire responses of self-report of behavior for the two-

week period prior to the time of data collection. The first behavior

measure was taken at the time of the complete questionnaire administra­

tion; the second two weeks later.

The first analysis was a comparison of the Fishbein-Ajzen model as

shown in the upper half of Figure 2 with a modification which added

only a direct path from attitude to behavior to the Fishbein-Ajzen

model. Thus, this modified model did not include prior behavior.

Attitudes and subjective norms were the exogenous or independent vari­

ables; intentions and subsequent behavior were dependent or endogenous

variables. Endogenous variables are defined as variables whose causes

are completely determined by variables included in the causal model;

exogenous variables are determined by causes lying outside the model.

For the additional comparison of models with the fully-expanded

16

Bentler-Speckart model, as presented in the lower half of Figure 2; the

measures of previous behavior were included. Thus, prior behavior is

conceived as a latent variable which was included in the causal model.

A factor analysis of the data supported the conclusion that the

theoretical constructs hypothesized as latent factors were adequately

assessed and were reasonably indicated by the observed variables.

Parameters for each causal model (Fishbein-Ajzen model, Bentler-Speckart

first modified model, and Bentler-Speckart fully-expanded model) were

estimated by a computer program, LISREL IV (Joreskog § Sorbom, 1978).

Hierarchical models were generated by adding parameters (causal paths)

to or removing them from the models being tested. Chi-square difference

tests of the null hypothesis that each parameter in question is not

present in the proposed causal structure in the population were used to

compare competing models. Results of the statistical analysis supported

Bentler and Speckart's (1979) hypothesis that the addition of three

structural parameters, i.e., direct paths from attitude to subsequent

behavior and from previous behavior to both intention and subsequent

behavior, is necessary for the causal model to successfully reproduce

the data.

The Bentler-Speckart (1979) study was carefully conceived and

executed; however, some comments on it appear to be in order. The five

latent variables assessed in their study (attitude, subjective norms,

prior behavior, intention, and target behavior) were each assessed by

an overall measure rather than by separate measures of their components.

Attitude was measured as the evaluative component only and did not

include beliefs and belief strength. Subjective norms were measured by

17

belief strength but did not encompass the motivation to comply.

Behavior was measured by self-report rather than by observations of

overt behavior. The element of response bias inherent in self-reports

of socially-censured or potentially illegal acts should be considered

since the target behaviors of the study were the use of alcohol,

marijuana, and hard drugs. Thus, the measurement procedure did not

correspond to Fishbein and Ajzen's recommendations and may have unduly

biased the results against the Fishbein and Ajzen model.

The study reported here is a comparison between the Fishbein-Ajzen

model and the Bentler-Speckart fully-expanded model, testing for dif­

ferences in predictive power between them. If the expanded model were

to show no significant increase in prediction over the basic Fishbein-

Aj zen model and if the additional causal paths hypothesized in the

Bentler-Speckart model do not demonstrate significant structural path

coefficients, then support for the Fishbein-Ajzen model would seem war­

ranted on the basis of the most parsimonious explanation of the data

and the relationships represented.

Method

Subjects

Subjects were 259 college students, of both sexes, who were

enrolled in the 1980 summer session at the University of Nebraska at

Omaha in psychology classes which met five days per week. Extra credit

toward the student's course grade was given for completion of the

questionnaire. For the final data analysis, 23 subjects, comprising

one class, were eliminated due to insufficient behavioral attendance

data. This left a final sample of 236 for the primary study sample.

18

Procedure

Demographic data were collected on each subject in a pilot sample

and in the primary study sample. These data included information on

sex, age, marital status, student status (number of hours in which

currently enrolled and expected to be enrolled in the coming fall

semester), expected date of graduation, employment status (number of

hours worked per week currently and expected in the fall), family

income, U.S. citizenship, and race. The purpose of this data collection

was to assess the representativeness of the pilot sample relative to

the population from which the primary study sample was also drawn.

Data on expected fall semester school/work status and on the expected

date of graduation were collected because of the possibility that these

variables might serve a moderating function.

A pilot sample of 123 summer school students in psychology classes

was used to identify modal beliefs of the consequences of the target

behaviors (class attendance/absenteeism) and the significant others in

relation to these behaviors. The procedure outlined by Ajzen and

Fishbein (1980) and previously described was followed. Subjects were

asked in a free-response format to list the beliefs that came to mind

as possible consequences of the behavior of attending or of being absent

from class. They were also asked to identify individuals or groups

whose opinions with respect to these behaviors are important to them.

See Appendix A for the pilot sample questions.

Tabulation of results from the 123 returned questionnaires yielded

1,093 responses to the beliefs (behavioral consequences) questions.

Of these, 90 responses were directed at the consequences of attending

19

summer school per se and an additional 97 responses were "none" or

blanks. Elimination of these null or nonrelevant responses left 906

attendance/absence behavioral consequences which were then grouped and

tabulated as categories. Eleven response categories represented 698

responses or 77% of total relevant responses elicited. An arbitrary

designation of 75% of total responses had been established as the

criteria for salient modal responses to be retained for the primary

study questionnaire. These eleven response categories were then used

in formulating corresponding questions to be rated by subjects in the

primary study (see Appendix B for the primary study questionnaire).

Question 6 of the pilot study questionnaire was designed to elicit

modal referents for the population. The total number of responses to

this question was 260. Of these, 67 (25.77%) were "myself" or "myself

alone" and 4 were "no one." Of the remaining 189 responses, five

categories included 157 (83%) of the total. These were the response

categories used in the primary study as modal referents.

The primary study used students in nine, five-week summer school

classes. Complete data were obtained from 236 subjects out of 295

students originally registered for these classes. Of these 295 students,

25 either provided no attendance data or provided none after the first

week of attendance data collection. If the assumption is made that

these students dropped out of the classes, the volunteer rate was 87.4

for participation in the study.

The target behavior or behavior of interest was class attendance/

absence which was assessed by passing attendance sheets for students

to sign. Students were uniformly told at the beginning of the session

20

that class attendance was not a factor in grading, but that attendance

sheets would be passed for "administrative purposes." In order to avoid

confounding regular class attendance with the special case of attending

class on a test day, no attendance was collected on test days. In each

class there were some days, unsystematically distributed, on which

attendance was not collected. Because of the unequal number of data

collection days between classes, the measures of behavior were the ratios

of days attending to the number of days in that time period.

Prior behavior was the attendance/absence data collected for two

weeks before the collection of attitude, subjective norms, and intention

data. The only explanation given for collection of these attendance

data was that they were "for administrative purposes." At the midpoint

of the summer school session, (i.e., between weeks 3 and 4), a semantic-

differential questionnaire was administered to all subjects to assess

attitudes toward the target behavior, subjective norms, and behavioral

intentions. Target behavior was the attendance/absence data obtained

after collection of the questionnaire data and until the end of the

summer school class session, the final two weeks of the session. The

first week of the five-week session was omitted from the study as that

is typically a week of instability in which many students disenroll

from their courses.

Measures

Multiple indicators (measures) were used to assess the reliability

of measurement for each latent variable (construct) and to remove

measurement error from the relationships among the latent variables.

Thus, behavioral measures were combined into several pre- and

21

post-intention periods. The eleven days of class prior to and including

the day of questionnaire administration were designated as prior behavior

measures and divided into three periods. Likewise, the post-questionnaire

period of eight days was designated as target behavior and divided into

two periods of four days each. As stated earlier, these measures were

the ratio of days attended to days attendance was taken.

Intention was assessed by two questions, "I intend to attend this

class every session . . .," and "I intend to be absent from this class

some times . . ." Scales were anchored with "likely" and "unlikely" at

their respective endpoints with reversed scoring for the intention to

be absent question.

Two measures of the attitude toward the behavior were used. One

was a scale score derived from the summation of seven items (item 3

in the questionnaire in Appendix B) evaluating the behavior of class

attendance on seven-point scales with endpoints anchored by "important-

unimportant," "worthless-valuable," "good-bad," "rewarding-punishing,"

and so forth. The other measure of attitude was derived from the rating

of each of eleven consequences of the behavior (items 5 through 15 of

the questionnaire in Appendix B ) . These consequences were obtained from

the pilot sample; the rating given to each consequence on the seven-;

point scale was multiplied by a specific evaluation of it (items 1

through 11 in the evaluation section of the questionnaire). These eleven

products of belief strength and evaluation were summed to form a score.

Subjective norms were also assessed by two measures. The first

was a general subjective norm measure, "Most people who are important

to me think I should attend this class every day during the summer

22

session.” The rating given to this question was multiplied by a rating

of motivation to comply with this perceived belief which was obtained

by asking, "Generally speaking, how much do you want to do what others

who are important to you think you should do?” The other subjective

norm measure was obtained from ratings given to perceived beliefs of

specific significant others, such as parents, friends, instructors, and

so forth obtained from the pilot sample (items 3 through 7 in the ques­

tionnaire section on '-how you think other people would like you to

behave”). These ratings were then multiplied by the subject's motivation

to comply with these perceived beliefs (items 16 through 20 on the last

page of the questionnaire) and the products summed.

Analysis

The method of data analysis used in this study was an extension of

path analysis developed by Joreskog and Sorbom (1978) in an effort to

combine the efficacy of path analysis in explicating underlying rela­

tionships with a confirmatory factor analytic approach used to identify

the factors (latent variables) in such relationships. While a path

analytic approach is appropriate for theory testing and clarification

(Billings § Wroten, 1978; Cook $ Campbell, 1979; Kerlinger § Pedhazur,

1973; Li, 1975; Namboodiri, Carter, § Blalock, 1975), path analysis has

certain limitations and shortcomings (see Appendix D for a general dis­

cussion of path analysis). The technique developed by Joreskog and

Sorbom (1978) eliminates or avoids many of these problems and has been

used in recent studies exploring causal models (e.g., Maruyama §

McGarvey, 1980; Pedhazur, Note 1). Also, this method was deemed

especially appropriate since it was used by Bentler and Speckart (1979,

23

1981) in their tests of modifications of the Fishbein-Ajzen model.

This method is an analysis of linear structural relationships by the

method of maximum likelihood operationalized in the computer program

LISREL IV. Multiple observed indicators of unobserved latent constructs

are used to infer relationships between the latent, unmeasured variables.

This analysis provides a measurement model and a causal model. The

inclusion of measurement error or unique variance as explicit parameters

in the model permits causal regression parameters to be estimated with­

out the influence of measurement error.

The Fishbein-Ajzen and Bentler-Speckart models were compared by

estimating the various structural parameters of a saturated model, i.e.,

a model in which all paths possible were estimated, and then computing

estimates for parameters of nested modifications of this saturated model.

This was accomplished by setting various parameters equal to zero, that

is, removing paths from the model.

Two comparative indices were used. First, the critical ratio of a

specific parameter gives a significance test for that parameter. The

critical ratio of each causal parameter reported as a t-value is the

ratio of the unstandardized LISREL parameter estimate divided by its

standard error. These ratios, due to the large sample size, are inter­

preted as standard normal deviates and represent levels of significance

for the parameter. At an alpha level of .05, a critical ratio of less

than 1.96 would indicate the non-significance or expendability of the

specific parameter.

In addition, a chi-square difference test between the hierarchical

models tested the null hypothesis that the specific parameter omitted

24

in the nested model is not present in tbe causal structure in the popula­

tion. The rationale for the chi-square difference test (Bentler § Bonett,

1980; Bentler § Speckart, 1979, 1981) is based on goodness-of-fit chi-

square tests and the associated degrees of freedom for each model compared.

In the case of parameter nesting, the models to be compared differ only in

that the parameter vector of the more restricted model is a special case

of the parameter vector of the less restricted model, with certain para­

meters constrained to equality or known constants. The null hypothesis is

of model equivalence and the difference between chi-squares with degrees

of freedom equal to the difference in parameters estimated provides a

statistical test of that null hypothesis and of the statistical necessity

of the parameters that differentiate the models.

Results

Pilot and Primary Study Sample Comparisons

Analyses of demographic data obtained from subjects in the pilot

and primary study samples supported the hypothesis that the two samples

were drawn from the same population. Only two of the fifteen variables

measured differed at the .05 level of significance; report of family

income and expected year of graduation. The primary study sample

reported an earlier date of graduation and a greater proportion reported

lower family income. All of the comparisons are reported in Table 1 in

Appendix C.

Measurement Model

The measurement model presents the standardized factor loadings of

the measured variables on the latent factors. These loadings, or

lambdas (A), may be interpreted as validity coefficients reflecting the

25

degree to which the observed variables adequately measure the specified

underlying construct. These parameters range from .609 to 1.0, a range

reported by Bentler and Speckart (1979) as adequate. Unique variance

represents the proportion of the variables' variance that is not

accounted for by the factors and includes measurement error. This is

given by epsilons (e) and deltas (6 ) in the schematic representations

(Figures 3 and 4). The top half of Tables C2 and Cll in Appendix C

presents the measurement model for each causal structure tested.

Structural Model

The structural or causal model estimates parameters of the rela­

tionships between latent, unmeasured variables with error of measurement

removed. Gammas (y) are interpreted as path coefficients from exogenous

to endogenous variables; betas (3 ) are interpreted as path coefficients

between endogenous variables. The relationships between latent exogenous

variables are given by the phis ($). Residuals of latent endogenous

variables are represented by zis (£). The bottom half of Tables C2

through Cll presents the structural model for each causal structure

tested. The following summary of results is based on the data presented

in these tables in Appendix C.

Model Comparisons

Figure 3 presents the structural models of relationships between

attitudes, subjective norms, intentions, and target behavior. Prior

behavior is not included. Circles represent latent, unmeasured vari­

ables and rectangles represent the observed, measured variables.

Double-headed arrows represent covariance and single-headed arrows

represent hypothesized causal paths. Figure 4 presents the structural

26

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models tested with prior behavior included.

Exogenous variables, defined as variables whose causes lie outside

of the hypothesized model, are attitude (A), subjective norms (SN), and

prior behavior (PB) which is added in Figure 4. Endogenous variables,

defined as those whose causes are completely determined within the

hypothesized model, are intention (INT) and target behavior (TB) .

The models corresponding to the Fishbein-Ajzen (1975) formulation,

i.e., those which did not include prior behavior data are labeled FA-1,

FA-2, and FA-3 (see Table 1). The parameters are schematically

presented in Figure 3. These models are differentiated as follows:

Model FA-1, saturated model (all possible paths estimated);

Model FA-2, deletion of path from SN to TB (^22 *

Model FA-3, deletion of path from SN to TB (y22) an( P ath

from A to TB (y2i) •

In the analyses of these models the path from attitude to intention

(y-1) was consistently significant. Also consistent with expectations, * 1 1the path from intention to target behavior {$21) was beyond the .0005

probability level in its critical value. When a path from attitude to

target behavior (y2 i) was tested in Models FA-1 and FA-2, this parameter

was not significant. This finding supports the Fishbein-Ajzen model in

contrast to Bentler and Speckart’s modification.

Contrary to the predictions of both theoretical approaches, the

path from subjective norms to intention (y ^ ) did not achieve a sig­

nificant level. However, the relationship (covariation) between sub­

jective norms and attitude (^j) was significant (t-values from 5.199

to 5.278, all ps < .00006).

29

Table 1

Structural Model Specifications

Fishbein-Ajzen Models (Figure 3): k, SN^ INT^ TB

FA-1:®21} Y i r Y 1 2 * Y 2 1 9 Y 22

FA-2:^21’ Y n > y 12’ Y 21

FA-3: 3 21/ Y n > Y 12

Bentler-Speckart Models (Figure 4): A,, SN, PB, INT, TB

BS-1: 321j Yll^ y 12’ Y13* Y23BS-2: 321' Yi r y12’ Y13j Y23BS-3: 321* Yi r y 12’ Y13j Y23BS-4: 321> y 11# y12’ Y13BS-5: 321’ yn j Y12BS-6: 321’ Yn * yl2} Y13* Y21BS-7: 321' Yi r Y12j Y21

30

Prior behavior data (PB) were included in Models BS-1 through

BS-7. The parameters of these models are presented schematically in

Figure 4 and are differentiated (see Table 1) as follows:

Model BS-1, saturated model (all possible paths estimated);

Model BS-2, deletion of path from SN to TB

Model BS-3, deletion of paths from SN and A to TB (Y22 and Y 21^ ’

Model BS-4, deletion of paths from A, SN, and PB to TB

^21* Y 22* and Y 23^;Model BS-5, deletion of paths from A, SN, and PB to TB;

and of path from PB to INT (y2 i> y 22’ y 23* Y 13^ ’

Model BS-6 , deletion of paths from SN and PB to TB (y22 and Y 23^'

Model BS-7, deletion of paths from SN and PB to TB, and of

path from PB to INT (y 2 2 > Y 23> and Y 13) •

Tables C5 through Cll in Appendix C present the measurement and

causal model parameter estimates for these BS-series models. In order

to facilitate comparisons of the significance levels of the parameter

estimates between models, t-values of estimated paths and of the

estimated residuals of intention and target behavior for all models

tested are presented in Table 2.

Of the models in which a path from attitude to target behavior

(y 2 i) was tested, a significant parameter estimate was attained only

once and that was in a model which excluded all paths from prior

behavior (Model BS-7). This finding supports the Fishbein-Ajzen model

as does the finding that the path from attitude to intention (Y-q) was

significant (maximum £ - .0250) for all models tested.

Where paths from prior behavior were estimated, parameter estimates

31

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32

were consistently significant both to intention (jd < .00006) and to

target behavior (£ < .00006). These findings support one of Bentler

and Speckart’s hypothesized modifications.

No path was hypothesized from subjective norms to target behavior,

therefore the finding of nonsignificance for this parameter estimate

^22^ saturatec* m odels was expected. However, contrary to expecta­

tions hypothesized by both the Bentler-Speckart and Fishbein-Ajzen

models, the path from subjective norms to intentions (Y-j achieved

significance (£ < .05) only in Models BS-5 and BS-7. These were models

which excluded all paths from prior behavior, a model specification

consistent with Fishbein and Ajzen's formulation. Interestingly, this

path (Y-j^p was n°t significant in Models FA-1, FA-2, or FA-3 which did

not include prior behavior data.

Model BS-5 was a formulation of the Fishbein-Ajzen model which

included prior behavior data in the measurement model only. That is,

the paths estimated were from attitude and subjective norms to intention

only, and just from intention to target behavior. In Model BS-5 all

causal paths estimated were significant; but in looking at the relative

strengths of the paths, it is apparent that the path from subjective

norms to intention (Y-j^* t-value = -2.984, £ = .0028) did not achieve

the level of significance reached by the path from attitude to intention

(Yu * t-value = 6.302, p < .00006) or by the path from intention to

target behavior t-value = 6.480, £ < .00006) (see Table C8 ) .

Model BS-7 differed from Model BS-5 only in the addition of a

path from attitude to target behavior (Y2 j) anc was a test of Bentler

and Speckart's (1979) first model modification. In this model also,

33

comparison of t-values and their associated probabilities for the

estimated path parameters (presented in Tables CIO and Cll) shows that

the path from subjective norms to intention ^ _va^ue = "2.086,

£ = .0366) did not achieve a level of significance as high as that

reached by the path from attitude to intentions (Y-^* t> value = 6.409,

£ < .00006) or the path from attitude to target behavior il2 1 * Ji”value =

3.145, £ = .0016). However, a strong relationship between subjective

norms and attitude ( ^i^ was demonstrated in all models (t-values >

4.956, £ < .00006). Also, in Model BS-7, when attitude was allowed a

direct path to target behavior (Y2i)> t le P at^ from intention to target

behavior (f^P failed to reach the .05 level of significance (t>value =

1.770, £ = .0768).

The relationship between attitude and prior behavior ($3^) and

between subjective norms and attitude (^2 1 was uniformly high in all

models Qp < .00006). The relationship between prior behavior and

subjective norms (^3 2 was nonsignificant (£ > .05).

Estimates of the path from intentions to target behavior (^2 1

were extremely variable. In those models which did not include prior

behavior data (Models FA-1, FA-2, and FA-3), this path was consistently

significant (£ < .002). However, when alternate paths to target

behavior were tested, this parameter decreased in value. In the models

which included prior behavior data (BS series models), the parameter

estimate for this path (f^ ) reached significance (t-values > 4.00,

p < .00006) only in models which deleted all other paths to target

behavior (Models BS-4 and BS-5) and in Model BS-6 which included a path

from attitude to target behavior (Y2 P * Apparently, the value of this

34

parameter is inversely related to the availability of alternate paths

to target behavior and the inclusion of prior behavior. In other words,

when other variables are included in the model, intention is a less

influential determinant of target behavior than the other variables.

In summary, when the t-values of estimated path parameters are

compared, the only paths demonstrating consistent significance are

those from prior behavior to intention Cy-^* £ < .00006), from prior

behavior to target behavior (Y2 3 > £ < *00006), and from attitude to

intention (y^ j £ < .023). Also consistent was the finding that the

path from subjective norms to target behavior (Y2 2) was nonsignificant

(£ > .05) whenever it was tested. All other paths varied in levels of

significance depending on the inclusion or exclusion of other variables

in the model. This instability suggests a need for model re-specification

to attain consistency of results.

Inspection of the t-values for the residuals of target behavior

reveals that when prior behavior is not permitted a direct path' to target

behavior (Models BS-4, BS-5, BS-6 , and BS-7), this residual increases

from a nonsignificant 1.27 or 1.53 to a significant level (£s < .003).

The addition of a path from attitude to target behavior without a path

from prior behavior to target behavior (Models BS-6 and BS-7) did not

result in an insignificant ^t-value for this residual. The t-values for

the residual of intention were consistently significant (all £s < .003).

Chi-Square Goodness-of-Fit Tests

The chi-square Goodness-of-Fit test is a test of the model's fit

to the observed data, that is, the variance unaccounted for by the model.

Table 3 presents the chi-squares, degrees of freedom, associated

Chi-

Squa

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Good

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Fit

Test

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36

probabilities, and ratios of chi-square divided by the degrees of

freedom for each model.

The x 2 statistic provides a test of the proposed model

against the general alternative that the MVs (measured

variables) are simply correlated to an arbitrary extent.

If the x 2 is large compared to degrees of freedom, one

concludes that the model does not appropriately mirror

the causal process that generated the data. (Bentler,

1980, p. 428)

A nonsignificant chi-square value, then, supports the hypothesis that

the model provides a plausible representation of the causal process,

that is, the chi-square indicates whether or not the factors specified

in the confirmatory factor analysis extract sufficient variance so that

the residuals are nonsignificant. The ratio of chi-squared divided by

its degrees of freedom is also an index of goodness-of-fit, with a

better fit being indicated by a smaller ratio. There is no associated

significance test for this ratio, however.

A problem that arises from the exclusive use of the chi-square

Goodness-of-Fit test for the evaluation of a model's fit to the data is

that the chi-square variate is a direct function of sample size and the

number of parameters estimated. Consequently, with large samples (this

analysis is not appropriate for small samples) and a large number of

parameters to be estimated, this statistical test would result in the

rejection of virtually all models. That is to say that the appropriate

statistical conclusion would be that the residual matrix contains sig­

nificant additional information that could be explained by a better model.

37

While the value of x 2 depends on sample size, the associated degrees

of freedom are only determined by the number of variables and

hypothesized factors in the model.

The results reported in Table 3 show that the chi-squares for"'all

FA series models attained a similar nonsignificant level of probability

(.20 < £ < .10). The chi-squares for all BS series models, which

included more variables along with their associated parameters and

consequently additional measurement error, were at less than the .005

level of significance while the sample size was the same for both series.

According to Bentler and Bonett (1980), one method of addressing

this problem is to inspect the absolute values of residuals which

provide an estimate of the amount of statistical information extracted

from the data. (This information on residuals is presented in Table 2.)

However, a "key ingredient" in appropriate statistical methodology for

comparisons of causal models in their view is the use of hierarchical

(nested) models to provide a chi-square difference test between models.

The primary use of the information presented in Table 3 is for the

computation of these chi-square difference tests between hypothesized

models. The results of these difference tests are presented in Table 4.

Chi-Square Difference Tests

The chi-square difference test is based upon the rationale that the

difference between chi-squares is also distributed as chi-square with

degrees of freedom equal to the difference between the number of para­

meters estimated by each model. This statistic is used to test the

importance of the parameters that differentiate the models and to assess

the relative adequacy of the models in explaining the observed data.

38

Table 4

Chi-Square Difference Tests for Model Comparisons

Fishbein-Ajzen Models

FA-1/FA-2

FA-1/FA-3 1

FA-2/FA-3 1

Bentler-Speckart Models

BS-l/BS-2 1

BS-2/BS-3

BS-l/BS-3 1

BS-3/BS-4 50

BS-4/BS-5 20

BS-4/BS-6

BS-6/BS-7 13

BS-3/BS-5 71

BS-3/BS-7 64

xi df £

.7983 1 ' .50 < £ < .30

.9527 2 .50 < £ < .30

.1544 1 .30 < £ < .20

xi df £

.3830 1 .30 < £ < .20

.0582 1 .80 < £ < .70

.4412 2 .70 < £ < .50

.9726 1 £ < .001

.3198 1 £ < .001

.1995 1 .70 < £ < .50

.8699 1 . £ < .001

.2924 2 £ < .001

.6430 1 £ < .001

39

The null hypothesis appropriate for these tests is one of model

equivalence. The chi-square differences, degrees of freedom, and

associated probabilities for the model comparisons are reported in

Table 4.

For the models which did not include prior behavior data (FA

series, see Table 4), the null hypothesis of model equivalence cannot

be rejected. Essentially, this means that the model with a path to

target behavior from intention only (Model FA-3) is equivalent to

models with additional paths to target behavior (Models FA-1 and FA-2).

Thus, with prior behavior data excluded, the Fishbein-Ajzen model is

supported as the most parsimonious and adequate explanation of the

observed data.

When prior behavior data are included (BS series models, see

Table 4), the null hypothesis of model equivalence cannot be rejected

for Models BS-1 (the saturated model), BS-2 (path from SN to TB, y

deleted), and BS-3 (path from A to TB, y a l s o deleted). These results

support the model with paths to target behavior from prior behavior and

intention, but not from attitude and subjective norms to target behavior

as the best fit to the observed data. Comparison of Model BS-3 with

Model BS-2 which includes the attitude to target behavior path (Bentler-

Speckart’ s expanded model) does not support the Bentler-Speckart (1979)

model in that the attitude to target behavior path does not fit the

observed data significantly better than the model with this path omitted./

Comparison of Model BS-3 with Model BS-4 (paths to target behavior

deleted from all variables with the exception of intention, results

in rejection of the null hypothesis of model equivalence. This hypothesis

40

must also be rejected for comparisons of Model BS-3 with subsequent

BS series models as well, indicating the necessity of paths from prior

behavior to both intention and to target behavior (y-j^ and Y 2 3) • The

lower ratio for this model of y2 to degrees of freedom (1.5456), as

compared to the value of that ratio for the other models, supports the

conclusion that this model provides the best fit to the observed data

of the models tested. These findings support the Bentler-Speckart

(1979) modification calling for paths from prior behavior to both

intention and target behavior.

Because of an equal number of degrees of freedom, Models BS-3 and

BS-6 (no path from PB to TB, Y 2 3 > ^ut inclusion of a path from A to TB,

Y 2 1 ) cannot be directly compared by a y2 difference test. However,

comparison of the x2/df ratios of these models supports the better fit

of Model BS-3 to the data.

Discussion

Before further discussion of the results of this study, one caveat

is in order. It must be borne in mind that this study concerned only

a single behavioral content domain, class attendance, and a single

population, university summer school students. Replication, utilizing

other behavioral domains and different populations of subjects, is a

necessary prerequisite to hypothesizing changes in structural equation

models. This present discussion of results is directed toward future

directions for additional study in this area. In considering behavioral

domains, it should be noted that Bentler and Speckart, in their most

recently published study in this area (1981), found different results

for different behavioral domains among the same subjects.

41

To address the initial research question posed by this study,

comparison of the Fishbein-Ajzen (1975) model with Bentler-Speckart’s

(1979) modifications, the results of this study lend support to the

Bentler-Speckart hypothesis of direct paths from prior behavior to both

intention and target behavior. However, the results do not support

their model modification of a direct path from attitude to target

behavior.

Another area for additional research suggested by these findings

is a test of a respecification of the causal model with subjective norms

and prior behavior acting upon (causal to) attitudes along with

hypothesized direct effects of prior behavior on the other endogenous

variables of intention and target behavior. The finding in this study

that the path from subjective norms to intention (y ^ ) achieved the .05

level of significance in only two of the models tested was contrary to

both the Fishbein-Ajzen (1975) and Bentler-Speckart (1979) model

predictions. The Fishbein and Ajzen formulation does allow the weights

of attitude and subjective norms to vary with the type of behavior,

,with the context or situation in which the behavior is to be performed,

with the target, and with individual differences between actors. But,

while the hypothesized path (causal relationship, was nonsignificant,

subjective norms and attitude exhibited a strong relationship (cor-4

relation) in all models (minimum t-value of cj^ of 4.956). This

covariation suggests the possibility of a respecification of the model

based upon the notion that social influences are indirect determinants

of intention through their effect on attitudes. Thus, a reasonable

respecification of the model would be a test of subjective norms prior

42

to attitude in the causal model specifications.

The most recently published Bentler-Speckart (1981) study found

support for a model which placed intention as an equal determinant of

target behavior along with attitude, subjective norms, and prior

behavior rather than as a mediating variable. While this modification

of the original model is different from the hypothesis suggested above,

it too supports the need for additional research on variants of the

Fishbein-Ajzen model.

Likewise, the relationship between prior behavior and attitude

^ 3 1 ^ was extremely strong in all models which included prior behavior.

A reasonable respecification would be the placement of prior behavior

causal to attitudes as well as to intention and target behavior in a

model respecification. As mentioned earlier, the instability of some

path parameter estimates, notably the path from intention to target

behavior seems to indicate the lack of satisfactory model

specifications. In model BS-3 which demonstrated the best overall

goodness-of-fit (y2/df), the t>value for the estimate of this path

^21^ WaS nons Snificaivt •Another possible explanation of the nonsignificant subjective norm

path might lie in the behavioral domain of the target behavior, class

attendance during a five-week summer session. The development of

group identification and group cohesiveness with classmates for such a

brief period is obviously minimal. This leaves as primary sources of

social influence significant others not directly involved in the target

behavior itself, such as parents, spouse, and friends. It is con­

ceivable that under such conditions, these social influences may have

43

generalized to a broad spectrum of attitudes toward academic behavior

in general. Ajzen and Fishbein (1980) presented consistent findings

derived from a variety of target behaviors which included voter

behavior, consumer behavior, and family planning behavior. However,

Bentler and Speckart (1981) found that in the same sample, three

behavioral domains, e.g., exercise, studying, and dating, led to dif­

ferent results with respect to path significance. Again, additional

studies across behavioral domains are indicated.

A third possible explanation might reside in the samples used in

the pilot and primary studies. The "significant others" of the study

questionnaire were determined by means of a pilot study on a sample of

summer school students at the same university who attended the session

immediately preceding the session attended by the subjects of the

primary study. These two samples did not differ significantly on 13

out of 15 demographic variables (see Table Cl in Appendix C ) . The two

variables in which significant differences were observed were year of

graduation and economic status as measured by parent's income. The

primary study sample was composed of students who reported an earlier

date of graduation and a greater proportion of whose parents had lower

income levels. Possibly, students who are closer to graduation per­

ceive the instrumentality of class attendance in attaining the goal of

graduation differently from students for whom the expectancy of gradu­

ation is not so immediate. For them, the social influence might be

overridden by the perceived instrumentality reflected in attitude which

was measured by the consequences of the behavior multiplied by an

evaluation of these consequences. Also, the difference in economic

44

status between samples might have led to the omission of relevant

"significant others" for consideration in the questionnaire used in

the primary study.

The question of generalizability requires attention at this point,

for the value of theory lies in its generalizability. If separate

theoretical formulations were required for each behavioral domain

investigated, the usefulness of such theory would be questionable.

It is the ultimate goal of theory-testing not only to provide empirical

support or disproof, but to define the limits of generalizability.

The behavior of interest in this study was classroom attendance,

a behavior that in its own right is of proper concern to the educational

community. Attendance behavior is likewise of prime interest to

organizations in the industrial, governmental, or service communities,

where successful and efficient operation depends heavily upon the

presence of organization members or employees.

The practical significance of identification of those variables

which can ultimately affect this behavior is obvious. For example, if

group norms were significant predictors of this behavior, then organi­

zational interventions targeted at the development of group cohesiveness

and desirable norms might be very appropriate. If prior behavior were

found to be most significant, as this study indicates, interventions

such as new-employee supervision, indoctrination, and prompt attention

to the first indications of attendance problems might forestall the

establishment of a pattern of poor attendance behavior.

A population of students with class attendance as a target

behavior is not the same as a population of workers with work attendance

45

as target behavior. However, the basic interrelationships between vari­

ables hypothesized by Fishbein and Ajzen have been found to possess

wide generalizability. So it seems that an initial study applying this

model and variants of it to the target behavior of attendance could

serve as a reasonable preliminary step toward the more general applica­

tion of the model to this behavioral domain.

46

1. Pedhazur, E.

Reference Note

J. Personal communication* December 8, 1980.

47

References

Ajzen, I., § Fishbein, M. Attitude-behavior relations: A theoretical

analysis and review of empirical research. Psychological Bulletin,

1977, 84, 888-918.

Ajzen, I., § Fishbein, M. Understanding attitudes and predicting

social behavior. Englewood Cliffs, N.J.: Prentice-Hall, 1980.

Bern, D. J. Self-perception: An alternative interpretation of cognitive

dissonance phenomena. Psychological Review, 1967, _74, 183-200.

Bern, D. J. Self-perception theory. In L. Berkowitz (Ed.), Advances in

experimental social psychology. New York: Academic Press, 1972.

Bentler, P. M. Multivariate analysis with latent variables: Causal

modeling. Annual Review of Psychology, 1980, _31, 419-456.*

Bentler, P. M . , § Bonett, D. G. Significance tests and goodness-of-fit

in the analysis of covariance structures. Psychological Builetin,

1980, 88, 588-606.

Bentler, P. M., § Speckart G. Models of attitude-behavior relations.

Psychological Review, 1979, 86 , 452-464.

Bentler, P. M., § Speckart, G. Attitudes "cause" behaviors: A

structural equation analysis. Journal of Personality and Social

Psychology, 1981, 40 , 226-238.

Billings, R. S., § Wroten, S. P. Use of path analysis in industrial/

organizational psychology: Criticisms and suggestions. Journal of

Applied Psychology, 1978, ^3, 677-688.

Calder, B. J., § Ross, M. Attitudes and behavior. Morristown, N.J.:

General Learning Press, 1973.

48

Calder, B. J., § Ross, M. Attitudes: Theories and issues. In J. W.

Thibaut, J. T. Spence, § R. C. Carson (Eds.), Contemporary topics

in social psychology. Morristown, N.J.: General Learning Press,

1976.

Cook, T. D., § Campbell, D. T. Quasi-experimentation design 5 analysis

issues for field settings. Chicago: Rand McNally College

Publishing Co., 1979.

Eagly, A. H., fi Himmelfarb, S. Attitudes and opinion's. Annual Review

of Psychology, 1978, 29, 517-554.

Festinger, L. A theory of cognitive dissonance. Evanston, 111.: Row,

Peterson, 1957.

Fishbein, M. Attitude and the prediction of behavior. In M. Fishbein

(Ed.), Readings in attitude theory and measurement. New York:

Wiley, 1967.

Fishbein, M . , £ Ajzen, I. Belief, attitude, intention and behavior:

An introduction to theory and research. Reading, Mass.: Addison-

Wesley, 1975.

Heider, F. Attitudes and cognitive organization. Journal of

Psychology, 1946, 21_, 107-112.

Joreskog, K. G., £ Sorbom, D. LISREL IV: Estimation of linear

structural equation systems by maximum likelihood methods. Chicago:

National Educational Resources, Inc., 1978.

Kelman, H. C. Attitudes are alive and well and gainfully employed in

the sphere of action. American Psychologist, 1974, 29, 310-324.

Kerlinger, F. N., § Pedhazur, E. J. Multiple regression in behavioral

research. New York: Holt, Rinehart and Winston, 1973.

49

Li, C. C. Path analysis: A primer. Pacific Grove, Calif.: Boswood

Press, 1975.

Maruyama, G., § McGarvey, B. Evaluating causal models: An application

of maximum-likelihood analysis of structural equations. Psychological

Bulletin, 1980, 87_, 502-512.

Namboodiri, N. K., Carter, L. F., $ Blalock, H. M., Jr. Applied

multivariate analysis and experimental designs. New York: McGraw-

Hill, 1975.

Osgood, C. E., Suci, G. J., § Tannenbaum, P. H. The measurement of

meaning. Urbana, 111.: University of Illinois Press, 1957.

Rokeach, M. Beliefs, attitudes, and values. San Francisco: Jossey-

Bass, 1968.

Triandis, H. C. Attitude and attitude change. New York: John Wiley

§ Sons, 1971.

Triandis, H. C. Values, attitudes, and interpersonal behavior. In

H. E. Howe, Jr., § M. M. Page (Eds.), Nebraska Symposium on Motivation

(Vol. 27). Lincoln: University of Nebraska Press, 1980.

Appendix A

Questionnaire for Pilot Study

51

Informed Consent for Participation in a Research Project

Your participation in a research study of student attitudes being

conducted by Arlene Fredricks of the UNO psychology department is

requested. Participation involves filling out a questionnaire during a

class meeting on the subject of some of your attitudes and providing

some personal information about yourself.

All information will be confidential and the anonymity of youri

responses will be guaranteed. Your responses will not be identified to

your instructor/professor.

Your decision on whether or not to participate in this study or to

withdraw from the study at any time will in no way prejudice your

relationship with the instructor or the university.

Your signature on this consent form indicates your willingness to

participate in this study and authorizes the use of the information

collected along with classroom data for research purposes only. There

are no hidden conditions or manipulations involved and you are free to

withdraw from the study at any time. I will be happy to answer any

questions you might have on this project and appreciate your cooperation.

Arlene Fredricks, 554-2704 or334-1177

Signature__________________________________________________________________________

Date

Investigator

Social Security Number __________________

Sex _________________ Age

Marital Status: Single ___Married ___Separated ___Divorced ___Widowed

Student Status:Number of credit hours you are currently taking. ____

Number of credit hours you expect to take in the fall.

Expected date of graduation (if you are in a degree program).

Check here if you are not in a degree program. ______

Employment Status:Average number of hours that you work per week currently. __

Average number of hours that you expect to work per week this fall.

Race: CaucasianBlack SpanishAmerican Indian Asian

Parents’ Approximate Annual Income (please check your best estimate)Don’t know________________________Less than $10,000 ___Between $10,000 and $14,999 ___Between $15,000 and $25,000 ___Over $25,000 ___

Expected grade in this class:

A > 0 , F

GPA: 4.003.90-3.993.75-3.893.50-3.743.00-3.492.50-2.992.00-2.491.50-1.99 Less than 1.50

Citizenship: U.S.

Other

53

Briefly list the beliefs that come to mind when you are asked the following questions. If you run out of space for your answers, use the back of the sheet and indicate the question number.

1. What do you believe are the advantages of attending your summer school class(es) every day for the summer session?

2. What do you believe are the disadvantages of your attending your summer school class(es) every day for the summer session?

3. What do you believe are the advantages of your being absent fromyour summer school class(es) once? Several times? One day a week? Twice a week? More than twice a week?

54

4. What do you believe are the disadvantages of your being absent from your summer school class(es) once? Several times? One day a week? Twice a week? More than twice a week?

5. What else do you associate with your attendance and/or absence from this summer school class?

6. Who are the individuals or groups of people whose opinions orinfluence is important to you with respect to your attendance and/or absence from your summer school class(es)?

Appendix B

56

Informed Consent for Participation in a Research Project

Your participation in a research study of student attitudes being conducted by Arlene Fredricks of the UNO Psychology Department is requested. Participation involves filling out a questionnaire either during a class meeting or at home on the subject of some of your attitudes, beliefs, and intentions about attending or not attending classes and also in providing some personal information about yourself.

All information will be confidential and the anonymity of your responses will be guaranteed. Your responses will NOT be identified to your instructor/professor. They will be used for research purposes only.

Your decision on whether or not to participate in this study or to withdraw from the study at any time will in no way prejudice your rela­tionship with the instructor or the university.

Your signature on this consent form indicates your willingness to participate in this study and authorizes the use of the information collected along with classroom data for research purposes only. You are free to withdraw from the study at any time. You are also free to omit any questions that you do not desire to answer, but it would be appre­ciated if you would answer all questions on the information sheet and on the attitude questionnaire.

After collection of all the data, the entire study and how the data that you have provided is to be interpreted and used will be explained to you.

I will be happy to answer any questions that you might have on this project and appreciate your cooperation.

Arlene Fredricks, 554-2704 or(home) 334-1177

Psychology Dept., 554-2592

Signature

Date

Investigator

57

INSTRUCTIONS FOR QUESTIONNAIRE ADMINISTRATION

You are being asked to participate in a research study which involves student attitudes and opinions about attending or not attending class.As the Informed Consent Form indicates, participation involves providing some information about yourself, filling out a questionnaire about your beliefs and intentions concerning attending or not attending class, and authorizing the use of classroom data about yourself. If there are any questions that you do not wish to answer, you may leave them blank. However, it is important to the study to collect as much of this requested information as possible and it would be very much appreciated if you did answer all the questions.

It is vital to the study that your responses be identified by your Social Security Number. However, your responses will NOT be identified to your instructor and will be used for the research purposes of this study ONLY.

After all the data have been collected, you will be informed of the entire scope of the study and the methods used and of how the data that you have supplied will be interpreted and used.

I will be happy to answer any questions that you might have on this study. My phone number is on the Informed Consent Form (334-1177) or you may contact me through the UNO Psychology Department at 554-2592.

Thank you very much.

Arlene Fredricks

PLEASE NOTE: IF ANY OF THE QUESTIONS ABOUT OTHER PEOPLE'S OPINIONS ORYOUR DESIRE TO COMPLY WITH THE OPINIONS OF THESE OTHERS (SUCH AS HUSBAND, WIFE, OR FIANCE) DO NOT APPLY TO YOU, PLEASE LEAVE THE RATING SCALE BLANK FOR THAT QUESTION AND WRITE N/A (NOT APPLICABLE) AFTER THE QUESTION.

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Social Security Number

Sex _________________ Age

Marital Status: Single ___Married ___Separated ___Divorced ___Widowed

Student Status:Number of credit hours you are currently taking.

Number of credit hours you expect to take in the fall. ______

Expected date of graduation (if you are in a degree program).

Check here if you are not in a degree program. ______

Employment Status:Average number of hours that you work per week currently.

Average number of hours that you expect to work per week this fall.

Race: CaucasianBlack SpanishAmerican Indian Asian

Parents1 Approximate Annual Income (please check your best estimate)Don’t know ___Less than $10,000________________Between $10,000 and $14,999 ___Between $15,000 and $25,000 ___Over $25,000 ___

Expected grade in this class:

A ____, B , C , D , E , F

GPA: 4.003.90-3.993.75-3.893.50-3.743.00-3.492.50-2.992.00-2.491.50-1.99 Less than 1.50

Citizenship: U.S.

Other

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12.

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from

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some

time

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the

summer

sess

ion.

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67

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19.

In ge

nera

l, how

much

do you

want

to do

what

your

fami

ly/r

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ives

/chi

ldre

n think

you

should

do?

68

Appendix C

70

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72

Table C2

Model FA-1(Prior behavior data omitted ATT § SN to INT § TB„

all paths estimated saturated model)

Measurement ModelMeasureFactors § Variables

Standardized Factor Loadings (A )

Unique Variance (6 or £) 1-A2 (stand.)

AttitudeXIX2

.708

.754.499.431

Subjective Norms X3 X4

.850

.849.278.278

IntentionY1Y2

.826

.798.317.363

Target Behavior Y3 Y4

.654

.661.572.563

Causal Model ParametersStandardized Parameters

Factor CorrelationsStandardWeights

Critical Ratio (t-values)

^ATT-SN .542 5.278

Y 11 .613 4.886

Y 21 .211 1.334

Y 12 -.012 -.116

Y 22 -.124 -1.120

3 21 .501 3.670

Residual Variances Intention Target Behavior

.632

.6285.1683.392

73

Table C3

Model FA-2(Prior behavior data omitted AT § SN to INT § AT

to target behavior)

Measurement ModelMeasureFactors § Variables

Standardized Factor Loadings (A)

Unique Variance (6 o r e ) 1-A2 (stand.)

AttitudeXIX2

.711

.753.494.434

Subjective Norms X3 X4

.859

.840.261.295

IntentionY1Y2

.824

.801.322.359

Target Behavior Y3 Y4

.671

.644.549.585

Causal Model ParametersStandardized Parameters

Factor CorrelationsStandardWeights

Critical Ratio (t-values)

^ATT-SN .539 5.199

Y 11 .620 4.948

Y 21 .121 .942

Y 12 -.023 -.234

Y 22 DELETED

e21 .516 3.735

Residual Variances Intention Target Behavior

.631

.6435.1513,431

74

Table C4

Model FA-3(Prior behavior data omitted paths ATT § SN to INT not to TB)

Measurement ModelMeasureFactors § Variables

Standardized Factor Loadings (A)

Unique Variance (6 or c) 1-A2 (stand.)

AttitudeXIX2

. 713

.749.492.439

Subjective Norms X3 X4

.859

.840.263.294

IntentionY1Y2

.819

.801.329.358

Target Behavior Y3 Y4

.655

.660.571.565

Causal Model ParametersStandardized Parameters

Factor CorrelationsStandardWeights

Critical Ratio (t-values)

^ATT-SN .543 5.214

Y 11 .633 5.039

Y 21 DELETED

Y 12 -.031 -.310

Y 22 DELETED

321 .603 5.516

Residual Variances Intention Target Behavior

.619

.6365.1523.384

75

Table C5

Critical Ratios of Parameters--Model BS-1 All Paths Estimated-Saturated Model

Measurement ModelMeasureFactors £ Variables

Standardized Factor Loadings (A)

Unique Variance (6 or £ ) 1-A.2 (stand.)

AttitudeXIX2

.716

.746.487.444

Subjective Norms X3 X4

.879

.821.228.325

Prior Behavior X5 X6 X7

.682

.662

.674

.535

.561

.546Intention

Y1Y2

.777

.849.396.279

Target Behavior Y3 Y4

.616

.702.621.507

Causal Model ParametersStandardized Parameters

Factor CorrelationsStandardWeights

Critical Ratio (t-values)

^A-S .331 5.152

^A-PB .223 4.225

^S-PB .015 .323

Y 11 .390 2.268

Y 21 -.077 -.552

y 12 .096 .947

y 22 .115 1.205

y 13 .556 4.252

76

Table C5 (Continued)

Standardized Parameters Factor Correlations

Causal Model ParametersStandardWeights

Critical Ratio (t-values)

Y 23 .934 5.644

321 .059 .550

Residual VariancesIntention .509 4.984Target Behavior .161 1.273

77

Table C6

Model BS-2 (Path SN to TB deleted)

Measurement ModelMeasureFactors § Variables

Standardized Factor Loadings (X)

Unique Variance fo or e ) 1-A2 (stand.)

AttitudeXIX2

.712

.749.493.438

Subjective Norms X3 X4

.862

.837.257.300

Prior Behavior X5 X6 X7

.683

.664

.679

.534

.559

.539Intention

Y1Y2

.778

.847.394.282

Target Behavior Y3 Y4

.611

.708.627.499

Causal Model ParametersStandardized Parameters

Factor CorrelationsStandardWeights

Critical Ratio (t-values)

^A* S .544 5.243

^A-PB .437 4.196

^SN-PB .050 .611

Yll .355 2.824

Y21 .028 .251

Y12 .082 .851

Y 22 DELETED

^13 .433 4.304

78

Table C6 (Continued)

Causal Model ParametersStandardized Parameters Standard Critical Ratio

Factor Correlations Weights Cl-values)

y 23 .833 5.960

21Residual Variances

.086 .674

Intention .510 4.994Target Behavior .190 1.528

79

Table C7

Model BS-3 (Paths SN to TB, ATT to TB deleted)

Measurement ModelMeasureFactors § Variables

Standardized Factor Loadings (A)

Unique Variance (6 or e ) 1-A2 (stand.)

AttitudeXIX2

.713

.749.492,438

Subjective Norms X3 X4

.861

.838.258.298

Prior Behavior X5 X6 X7

.682

. 664

.679

.535

.559

.539Intention

Y1Y2

.779

.846.393.284

Target Behavior Y3 Y4

.609

.710.629.497

Causal Model ParametersStandardized Parameters

Factor CorrelationsStandardWeights

Critical Ratio (t-values)

^AT-SN .543 5.245

^AT-PB .441 4.312

^SN-PB .054 .655

Yll .355 2.812

*21 DELETED

*12 .082 .857

*22 DELETED

*13 .431 4.278

80

Table C7 (Continued)

Causal Model ParametersStandardized Parameters Standard

Factor Corx'elations WeightsCritical Ratio

(t-values)

Y 23

21Residual Variances

Intention Target Behavior

.835

.103

.512

.190

6.003

.943

5.0171.527

81

Table C8

Model BS-4(All paths to TB from exogenous variables deleted)

Measurement ModelMeasureFactors § Variables

Standardized Factor Loadings CO

Unique Variance fc or e ) !-X2 (stand.)

AttitudeXIX2

.715,747

.448

.443Subjective Norms

X3 X4

.875

.825.234.320

Prior Behavior X5 X6 X7

.690

.639

.687

.524

.591

.528Intention

Y1Y2

.745

.825.445.320

Target Behavior Y3 Y4

.643

.673.587.548

Causal Model ParametersStandardized Parameters

Factor CorrelationsStandardWeights

Critical Ratio (t-values)

^ AT • SN .542 5.174

4>AT*PB .444 4.223

SN * PB .029 .340

Y 11 ..304 2.463

Y 21 DELETED

Y 12 .110 1.164

Y 22 DELETED

Y13 .556 5.245

82

Table C8 (Continued)

Causal Model ParametersStandardized Parameters Standard Critical Ratio

Factor Correlations Weights (t-values)

y 23 DELETED

B21 .714 6.783

Residual VariancesIntention .397 4.432Target Behavior .490 3.155

83

Table C9

Model BS-5(F-A Models only paths from ATT § SN to INT § from INT to

target behavior--prior behavior not included except in measurement model)

Measurement ModelMeasureFactors § Variables

Standardized Factor Loadings (X)

Unique Variance (6 or e ) 1-A2 (stand.)

AttitudeXIX2

.615

.637.621.594

Subjective Norms X3 X4

.873,822

.238

.324Prior Behavior

X5 X6 X7

.683

.644

.690

.533

.586

.524Intention

Y1Y2

.762

.824.419.321

Target Behavior Y3 Y4

.646

.669.582.553 /

Causal Model ParametersStandardized Parameters

Factor CorrelationsStandardWeights

Critical Ratio (t-values)

^AT-SN .646 5.481

^AT-PB .607 4.934

^SN-PB .022 .261

■Yll 1.035 6.302

^21 DELETED

^12 -.395 -2.984

y 22 DELETED

y 13DELETED

84

Table C9 (Continued)

Standardized Parameters Factor Correlations

Causal Model ParametersStandardWeights

Critical Rati< (t-values)

y 23 DELETED

321 .687 6.480

Residual VariancesIntention .309 2.995Target Behavior .527 3.254

85

Table CIO

Model BS-6(Prior behavior to INT only ATT to INT £ TB, SN to INT, PB to INT)

Measurement ModelMeasureFactors § Variables

Standardized Factor Loadings a )

Unique Variance (6 or e ) 1 - x 2 (stand.)

AttitudeXIX2

.713

.747.491.442

Subjective Norms X3 X4

.876

.823.282.322

Prior Behavior X5 X6 X7

.689

.639

.688

.525

.592

.527Intention

Y1Y2

.749

.835.439.303

Target Behavior Y3 Y4

.650

.665.577.558

Causal Model ParametersStandardized Parameters

Factor CorrelationsStandardWeights

Critical Ratio (t-values)

^ATT-SN .540 5.159

^ATT-PB .457 4.321

^SN-PB .029 .339

*11 .283 2.239

*21 .080 .654

*12 .116 1.209

*22 DELETED

*13 .548 5.068

86

Table CIO (Continued)

Causal Model ParametersStandardized Parameters Standard Critical Ratio

Factor Correlations Weights (t-values)

y 23 DELETED

321 .649 4.777

Residual VariancesIntention .426 4.554Target Behavior .510 3.238

87

Table Cll

Model BS-7(No paths from prior behavior path from ATT to TB^

Bentler-Speckart Modification 1)

Measurement ModelMeasureFactors § Variables

Standardized Factor Loadings M

Unique Variance (6 or e ) 1-A2 (stand.)

AttitudeXIX2

.610

.634.628.597

Subjective Norms X3 X4

.892

.808.205.347

Prior Behavior X5 X6 X7

.678

.649

.689

.540

.578

.525Intention

Y1Y2

.770

.853.408.273

Target Behavior Y3 Y4

.680

.636.537.596

Causal Model ParametersStandardized Parameters

Factor CorrelationsStandardWeights

Critical Ratio (t-values)

^ATT-SN .575 4.956

^ATT-PB .695 5.416

^SN-PB .025 .296

Y 11 .844 6.409

Y 21 .500 3.145

Y 12 -.217 -2.086

y 22 DELETED

y 13 DELETED

88

Table Cll (Continued)

Standardized Parameters Factor Correlations

Causal Model ParametersStandard 'Weights

Critical Ratio (t-values)

y 23 DELETED

321 * 260 1.770

Residual VariancesIntention .451 4.199Target Behavior .496 3. 249

Appendix

90

Discussion of Path Analysis

Path analysis is the logical precursor of structural analysis.

This discussion is based upon the following references: Billings §

Wroten, 1978; Cook § Campbell, 1979; Kerlinger § Pedhazur, 1973; Li,

1975; and Namboodiri, Carter, § Blalock, 1975.f

In order to use path analysis the theoretical framework being

studied must first be made explicit. The basic technique uses ordinary

least squares regression to derive path coefficients which may be defined

as standardized regression coefficients and which may be interpreted as a

ratio of two standard deviations. These path coefficients between vari­

ables are then used to test proposed causal relationships among a set of

variables. In this manner, path analysis can be used to test an a priori

causal hypothesis against a set of observed correlations. According to

Li (1975) the usefulness of path analysis over simple multiple regression

techniques lies in its ability to extend the single multiple regression

equation treatment to a network of variables involving more than one

equation. The use of path analysis for decomposing a correlation into its

components of direct and indirect effects within a given causal model is

an additional important function of this analytic approach.

Kerlinger and Pedhazur (1973) explain the use of path analysis as

a tool for theory testing as follows:

Path analysis is an important analytic tool for theory

testing. Through its application one can determine whether

or not a pattern of correlations for a set of observations

is consistent with a specific theoretical formulation

. . . . a correlation between two variables can'be expressed

as a composite of the direct and indirect effects of one

variable on the other. Using path coefficients it is there­

fore possible to reproduce the correlation matrix (R) for all

the variables in the system. . . . however, as long as all

variables are connected by paths and all the path coefficients

are employed, the R matrix can be reproduced regardless of

the causal model formulated by the researcher. Consequently,

the reproduction of the R. matrix when all the path coefficients

are used is of no help in testing a specific theoretical model.

What if one were to delete certain paths from the causal model?

This, in effect, will amount to setting certain path coeffi­

cients euqal to zero. The implication is that the researcher

conceives of the correlation between the two variables whose

connecting path is deleted as being due to indirect effects

only. By deleting certain paths the researcher is offering a

more parsimonious causal model. If after the deletion of some

paths, it is possible to reproduce the original R matrix, or

closely approximate it, the conclusion is that the pattern of

correlations in the data is consistent with the more parsimoni­

ous model. . . .

If after the deletion of some paths there are large dis­

crepancies between the original R matrix and the reproduced

one, the conclusion is that, in the light of the relations

among the variables,, the more parsimonious theory is not

tenable. (p. 317)

92

Path analysis requires a distinction between variables labeled

exogenous and endogenous. Exogenous variables are one or more variables

whose causes lie outside the causal system. Variables so labeled may

be correlated with no causal direction specified. Endogenous variables

arc those whose cau3e3 lie within the system* These variables musl be

specifically ordered with respect to hypothesized cause-effect relation­

ships. The variance of an endogenous variable is considered to be

accounted for by the effects of other endogenous and exogenous variables

prior to it in the ordering and by a residual or error term.

For each endogenous variable it is necessary to create a causal

model which is a weighted function of variables prior to (causal to)

that variable and an error term, Actual weights are determined by means

of multiple regression in which each endogenous variable is treated as

the criterion and the variables hypothesized to affect it are treated

as predictors. The resulting standardized beta weights are path coef­

ficients representing the direct effects of the causal variables upon the

criterion variable. An indirect path is computed by multiplying together

all the direct path coefficients which comprise the indirect path.

A basic theorem of path analysis is that "the correlation between

two variables is the sum of all connecting paths between the two vari­

ables" (Lij 1975^ p. 149). Therefore^ indirect effects are the differences

between total effects (the correlation) and direct effects (path coef­

ficients) . In this way^ path analysis may be used to decompose a

correlation into its components of direct and indirect effects within

a causal model.

93

In testing a theory which predicts the absence of one or more

direct paths, those variables hypothesized to have only indirect effects

are deleted from the regression equation for that particular dependent

variable. If the hypothesized paths are statistically or meaningfully

significant (beta weights usually greater than .05) and the model is

able to reproduce the observed correlation matrix, the theory as modeled

is supported.

The major assumptions that must be met in the use of path analysis

are: (a) a priori specification of the causal sequence of the variables;

(b) the specified causal sequence must be a one-way flow (recursive);

(c) the residual of each endogenous variable affects only that specific

variable and is uncorrelated with other system variables or with their

residuals; and (d) the data are linear, additive (no interaction effects),

and on a ratio or interval scale of measurement.

There are, however, some basic shortcomings in the use of path

analysis. The degree of reliability with which latent variables or

constructs are measured by the observed variables is not assessed when

multiple regression equations using one observed indicator for each latent

variable are used to obtain the beta weights (path coefficients). Because

of this, any error in measurement is confounded with estimation of causal

parameters pertaining to the latent variables. Also, the method requires

the assumption of a recursive or unidirectional causal model which may

not always represent reality.