The Theory of Planned Behavior: Some Measurement Issues Concerning Belief-Based Variables

The Theory of Planned Behavior: Some Measurement Issues Concerning Belief-Based Variables

CAMILLE GAGNE AND GASTON GODIN' Research Group on Psychosocial Aspects of Health-Related Behaviors

Laval Universip Sre. -Fay Canada

The theory of planned behavior presents clear operational definitions of attitudes, subjective norm, perceived behavioral control, and each of their corresponding belief-based measures. Theoretically, the direct and indirect measures of a given construct must be closely correlated. Empirical results, however, indicate that this is not always the case. In the present study, 2 of the factors that could be responsible for this situation-namely, the scaling of the variables defining each belief-based construct and the adequacy of using an expectancy-value model within the belief-based measures-were verified among a data set of 16 studies concerned with the application of the theory of planned behavior to the field of health. The results indicate that the scaling method used affected the correlation coefficients between indirect and direct measures. However, the face validity of these scaling methods must be demonstrated. The results also support the idea that, in most cases, using the expectancy-value model is no better than using only one arm of the belief-based measure.

According to the theory of planned behavior, intention is regarded as the immediate determinant of volitional behavior. Perceived behavioral control may also exert a direct influence on behavior, but only when performance of the behavior is not hlly under volitional control. Three variables define behavioral intention: attitude toward the act, subjective norm, and perceived behavioral control. The attitude component (Aact) is defined as the personal evaluation of the behavior to be adopted. Subjective norm (SN) is regarded as the perceived social influence regarding adoption of the behavior. Finally, perceived behavioral control (PBC) reflects personal beliefs of how easy or difficult performing the behavior is likely to be.

Specific kinds of salient beliefs underlie each of these three determinants of intention: behavioral beliefs, normative beliefs, and control beliefs. Modeled on the theory of reasoned action (Ajzen & Fishbein, 1980), the belief-based constructs are defined by an expectancy-value model where salient beliefs are

'Correspondence concerning this article should be addressed to Gaston Godin, Faculty of Nurs- ing Sciences, Pavillon Paul Comtois, Laval University, Ste.-Foy, Quebec GI K 7P4, Canada.

2173

Journal of Applied Social Psychology, 2000, 30, 10, pp. 2173-2193. Copyright 0 2000 by V. H. Winston & Son, Inc. All rights reserved.

2174 GAGNE AND GODIN

multiplied with a valuation part: the products are subsequently summed across all salient beliefs. Each relationship can be represented by the following equations:

Attitude:

n Aact - C (bi x ei)

i = 1

where Aact is the attitude toward the behavior; bi is the belief (i.e., the probability) that performing a specific behavior will result in a specific outcome; ei is the individual’s evaluation of that particular outcome; and n is the number of salient beliefs a given subject holds about performing any specific action.

Subjective norm: n

i = 1 S N - c (nbixmc,) (2)

where SN is the subjective norm: nbi is the individual’s normative belief ( i t . , the probability) that a specific person or group thinks that he or she should or should not perform the behavior; mci is the individual’s motivation to comply or not comply with that particular referent: and n is the number of salient referents.

Perceived behavioral control:

n

i = 1 PBC - C (ci x pi) (3)

where PBC is perceived behavioral control: ci is the control belief that a specific factor will increase or reduce the difficulty of performing the behavior; p , is the perceived power of a particular control factor to facilitate or hamper performance of the behavior; and ni is the number of factors that could facilitate or hamper performance of the behavior.

The theory of planned behavior (TPB) thus allows the development of both direct and indirect measures of attitude, subjective norm, and perceived behavioral control. Theoretically, both measures of the same construct must be closely correlated. Ajzen (1991), however. has observed that this is not always the case and suggested that four factors could be responsible for this situation. First, the use of nonsalient beliefs may attenuate the level of association. Indeed, a correlation coefficient between a direct and an indirect measure is generally higher when salient beliefs are used (Budd, 1986; van der Pligt & de Vries, 1998). Sec- ond, Ajzen (1991) suggested that the type of responses evoked by indirect and corresponding direct variables could explain the correlation of moderate

MEASUREMENT IN THE THEORY OF PLANNED BEHAVIOR 2175

magnitude between these two measures; that is, answering belief-based items requiring relatively reasoned responses compared to a direct measure that evokes a rather automatic reaction. However, no evidence related to this hypothesis was presented within the expectancy-value model.

Third, the scaling method of the variables defining the belief-based constructs was suggested as a factor explaining the relatively low correlation coefficient between a direct and a belief-based measure. In this regard, Schmidt (1973) has demonstrated that when variables in a multiplicative model are not measured on a ratio scale, as in the TPB, a change in the zero point can have marked effects on the magnitude of the correlation between the expectancy-value construct and another variable. Thus. the correlation coefficient was quite dependent on the scales used to measure the variables in the expectancy-value model. It should be mentioned that some approaches have been suggested to control the scaling problem with multiplicative models. For instance, Thurstone and Jones (1957) suggested a method for constructing ratio scales. However, this approach is laborious (Schmidt, 1973), and few researchers have used it (Bagozzi, 1984).

According to Bagozzi (1984) and Schmidt (1973), other techniques of analyses, such as conjoint analysis and functional measurement, could be possible solutions for the scaling problem. These approaches, however, are cumbersome to apply. Others authors have suggested using hierarchical regression analysis to circumvent the scaling problem (e.g., Arnold & Evans, 1979; Evans, 1991). Arnold and Evans indicate that the amount of explained variance and the F ratio are invariant across the linear transformation of the original data (unipolar or bipolar scale). However, there is no agreement concerning the adequacy of this method (Birnaum, 1973, 1974; Busemeyer & Jones, 1983; Schmidt, 1973; Valois, Deshamais, Godin, Perron, & Lecomte, 1993).

One generally neglected aspect is that the scaling problem must be addressed before applying hierarchical regression. Indeed, in terms of influencing the findings of the items analysis (e.g., alpha coefficient, coefficient of correlation between product items associated with a construct), the scaling method can affect the items that will be retained or rejected for the final analysis. Finally, Holbrook (1977) suggested an optimal scaling method, which consists of finding the scaling method leading to the best correlation between a criterion and the sum of product terms not measured on a ratio scale. This method has also been criticized (Bagozzi, 1984; Evans, 1991). In sum, there is no agreement concerning the approach that should be adopted for the problem of scaling.

The last factor suggested by Ajzen (1991) to explain the relatively low correlation between the belief-based measures and the corresponding global constructs is the adequacy of using an expectancy-value model. Is it necessary to adopt such a model to define the belief-based constructs? The potential influence of the last two factors is the focus of the present paper; that is, the scoring method and the adequacy of the expectancy-value model.


At least two scaling methods have been recommended for measurement of each of the belief-based measures o f the TPB. With regard to the attitude construct, two scaling methods were acknowledged: bb x eb and b, x eb (see equation 1). Fishbein and Ajzen (1975) and Ajzen (1991) referred to the definition of the behavioral belief to justify a unipolar scoring for b: Considering that beliefs are to be regarded as subjective probability and that a probability cannot take negative values, unipolar scoring assigning positive scale values to responses should be used forb. However, the same authors also provided reasons for selecting a bipolar scoring method. For instance, in 1980, Ajzen and Fishbein suggested a bipolar scaling method for both b and e in order to ensure that disagreement that a behavior leads to a negative outcome would contribute positively to the attitude toward the behavior.

Empirical findings indicate that the bipolar scaling (-3 to f3) for both b and e most often yields a higher correlation with the global attitude construct than does a bipolar scoring method (-3 to +3) for b and a unipolar scoring method ( f l to +7) fore. For instance, Ajzen (1991) observed that for leisure-time behaviors, the correlation between Aact and X(b x e) was stronger when a bipolar scale was used for both b and e, compared to using a unipolar scale for b. Sparks, Hedder- ley, and Shepherd (1991), in their review of different studies of eating behaviors, also noted that the correlation between both constructs was generally improved when bipolar scales were used, but not always. Sometimes it did not change or it improved the correlation when a unipolar scoring method was adopted forb. In contrast, Hewstone and Young (1988) observed that the b, x eb scoring method yielded a higher correlation coefficient with Aact than the bipolar scoring model bb x eb. Finally, Mellenbergh, Molendijk, de Haan, and ter Horst (1990) observed that different scaling methods may yield higher correlations with Aact, depending on the scale values assigned to translate unipolar (0 to +6, +2 to +8, -6 to 0) and bipolar (-3 to +3, -8 to -2) scoring methods.

With respect to the belief-based construct of subjective norm, two scaling methods have been proposed: nbb x mcb and nbb x mc, (see equation 2). The bipolar scaling for both nb and mc was first suggested so that the belief that a given referent would not approve adoption of the behavior and a negative motivation to comply with that referent would contribute positively to the SN construct. As it is unlikely that people would not be motivated to comply with important referents (Fishbein, 1976), it was later recommended to opt for a unipolar scaling for mc. This approach is largely supported in the literature (Budd, North, & Spencer, 1984; Fishbein & Ajzen, I98 1 ; Loken & Fishbein, 1980).

The data presented by Ajzen (1991) suggest that three scaling methods (i.e., Cb x Pb, c b x pu, and c, x p,; see equation 3) may yield the highest correlation with PBC, the best scoring method varying with the behavior under study. The author gave no theoretical justification to support the choice of either of the seal- ing methods. However, according to Valois et al. (1993), the perceived power

MEASUREMENT IN THE THEORY OF PLANNED BEHAVIOR 21 77

was unlikely to be negative, and they proposed using a cb x p, scaling method. They reported that the best correlation between PBC and C(c x p) was obtained when the belief-based construct was scored as Cb x p, instead of cb x Pb.

As we can see, the appropriate scoring method regarding the belief-based measure of perceived behavioral control has yet to be established. However, there is a trend to use the bb x eb and the nbb x mc, methods to produce higher correlations with the corresponding global attitude and global subjective norm, respectively. It should be emphasized, however, that it is difficult to establish the face validity of these scaling methods (or other scaling approaches) because the contribution of some patterns of responses to the overall construct measured is ambiguous. Consider, for instance, the disbelief in a negative outcome within the belief-based measure of attitude. According to Ajzen and Fishbein (1980) and Ryan and Bonfield (1975), such a pattern of responses translates into a positive attitude. In contrast, Valiquette, Valois, Desharnais, and Godin (1988) consider that it may lead to a meaningless score of attitude, whereas Bagozzi (1984) suggests that it implies a neutral attitude. In fact, when a disbelief is obtained (on b, nb, or c variables), the answer on the valence variable (e, mc, or p variables) may be irrelevant because it does not refer to the belief of the subject. Consequently, it is difficult to infer the influence of patterns of responses for disbeliefs.

The contribution of neutral scores (“neither”) is also ambiguous. Indeed, it is awkward that a neutral score on the expectancy or the valence arms of the belief- based construct does not have the same impact on the overall score when the scoring method is not bipolar for both arms. For instance, a neutral score on a bipolar e scale (0) results in a zero score for b x e, whereas a neutral score on a unipolar b scale (i.e., 4 on a 7-point scale) yields a positive, a neutral, or a negative score for b x e, depending on the e response. More attention should be given to the meaning of a neutral response, especially when the questionnaire does not provide clear directives in this regard. The score that should be attributed to a given response option should vary according to its meaning (e.g., a neutral opinion, a nonopinion, the irrelevance of a question).

Rating the influence of a referent with whom the subject is not motivated to comply also requires additional attention. It is acceptable to postulate that in the decision to adopt or not to adopt a behavior, an individual will not consider referents who are not important to him or her. Consequently, it is debatable whether to assign a positive or a negative score to translate the effect of that referent (as is the case with the nbbx mcb and the nbb x mc, methods). Assigning a neutral score for the rating of a referent with whom a respondent is not motivated to comply is cumbersome because the contribution to the global score of SN could be the same as a neutral score on nb, mc, or both.

Ajzen (199 1) suggested that the expectancy-value model could be inadequate to describe the relation between a belief-based measure and a more global measure of the same construct. Some empirical findings obtained within the


framework of the theory of reasoned action tend to support this hypothesis. For instance, with respect to the attitude construct. Nakanishi and Bettman ( 1974) observed that I(bb x eb) and Cb explained virtually the same proportion of the variance of Aact. In the same perspective, Hom and Hulin (198 1 ) noted that the correlation with Aact was not different when c(bb x eb) or a unit-weighted Cb (that took into account the sign of e) was used. Lechner (1998) observed similar findings using intention instead of Aact as the criterion. She noted that, in most instances, b yielded similar or higher correlation with intention than did e orb, x eb. Furthermore, she observed that. most often, b, x eh could not explain any additional variance in intention over and above the b and e variables. It should be emphasized, however. that each of these studies has used only one of the scaling methods proposed for the belief-based measure of attitude. Different findings could have been obtained with other scaling approaches.

Few researchers have also verified if the correlation between SN and Cnb is equivalent to the correlation between SN and C(nb x mc). The findings of Hom and Hulin (198 1) indicate that when both nb and mc were scored on bipolar scales, the correlation between SN and Cnb was better than was the correlation between SN and C(nbb x mcb). The results, however, are more ambiguous when mc is treated as a unipolar variable. Indeed. Fishbein and Ajzen (198 1 ) showed that the correlation between SN and Znb was lower than the correlation between SN and C(nbb x mc,). In contrast, Budd et al. (1984) demonstrated that the correlation with SN was similar when Cnb instead of C(nbb x mc,) was used. The use of nonsalient beliefs in the data analyzed by Fishbein and Ajzen (1981) may, however, partially explain the low correlation between SN and Cnb. In the present paper, the effect of omitting the mc variable will be verified, but using salient beliefs as recommended by Ajzen and Fishbein (1980).

The effect of omitting mc was also tested using intention as a criterion instead of the global construct of SN. For instance, Ajzen and Fishbein (1969) and Grube, Morgan, and McGree (1986) observed that the inclusion of mc with nb decreased the correlation with intention. The scaling method used for C(nb x mc) was not specified in either study. However, Schlegel, Crawford, and Sanborn (1977) indicated that the inclusion of mc decreased the correlation with intention, regardless of the scaling method applied to mc. Moreover, some authors have observed that the predictive power of C(nb x mc) is not improved when a unipolar scoring method is used for mc (e.g., Budd, 1986; Saltzer, 1981). According to Miniard and Cohen (1981), the level ofspecificity at which mc is measured could influence the pooled simple correlation between x(nbb x mcb) and SN or intention. However, Fishbein and Ajzen (198 1) reanalyzed the data of Miniard and Cohen and demonstrated that, with unadjusted correlations and a unipolar scoring for mc, the correlation with SN or intention was better when C(nbb x mc,) instead of Cnb was used. The findings were observed regardless of the level of measurement for mc.


Regarding the belief-based measure of PBC, no study, to the best of our knowledge, has yet investigated if one of the arms of the indirect measure [C(c x p)] has a higher correlation with the corresponding direct measure (PBC). Our past studies conducted within the framework of TPB suggest, however, that Ep is more strongly related to PBC than is Cc. This is one of the avenues tested in the present paper.

In summary, key questions to be answered in the present study are as follows: (a) Which scoring method, among the methods proposed in the literature, gives the strongest correlation between direct and corresponding belief-based measures? and (b) Are the correlations between Aact, SN, PBC, and their corresponding belief-based measures [C(b x e), C(nb x mc), C(c x p)] lower when only one arm of the expectancy-value model is used (i.e., Cb, Cnb, and Cp)?

Method

Sixteen studies conducted in recent years were used in this research. Each study concerned the adoption of a given behavior in the domain of health and was carried out among different segments of the population. The studies retained were those that included direct and indirect measures of the constructs of the TPB. A brief description of these studies is presented in Table 1.

In each of these studies, the information was obtained by means of a self- administered questionnaire. Also, each questionnaire was developed following Godin and Kok’s (1996) method and applying Ajzen and Fishbein’s (1980) recom- mendations. Essentially, salient beliefs were obtained from a preliminary study conducted with an independent sample of subjects selected from the target population. An open-ended eliciting questionnaire or focus groups were used to collect (a) the list of advantages and disadvantages in the adoption of the behavior studied; (b) the list of persons or groups of persons who would approve or disapprove of one’s performance of the behavior; and (c) the list of perceived factors that could facilitate or hamper performance of the behavior. The beliefs most often listed were selected for each construct. In most cases, 5- or 7-point scales were used.

Item Anulvsis

In order to secure the construct validity of the measures, data of a given subject were retained only if he or she had answered at least 75% of the items of a given construct. Concerning the reliability of the measure, a Cronbach’s alpha value close to .65 was sought. Table 2 depicts the number of items and the Cron- bach’s alphas for direct and belief-based measures in the 16 studies, when constructs were operationalized as currently recommended by Ajzen and Fishbein (1980) and Ajzen (1991); that is, bb x eb (Ajzen & Fishbein, 1980), nbb x mc, (Ajzen & Fishbein, 1980), and cb x Pb (Ajzen, 199 1).

Tabl

e 1

>

z 0

Beha

vior

s St

udie

d an

d Pa

rtic

ipan

ts ’

Cha

ract

eris

tics in

the

16 St

udie

s

No.

B

ehav

ior s

tudi

ed

n Pa

rtic

ipan

ts

Gen

der

0 s

Mag

e M

F

(yea

rs)

z

1 C

ondo

m u

se“

2 C

ondo

m u

se3

3 C

ondo

m u

se”

4 C

ondo

mus

ea

5 C

ondo

m u

se”

6 C

ondo

mus

eb

7 C

ontr

acep

tive

met

hod

use

8 C

ontr

acep

tive

met

hod

use

(birt

h-

377

Engl

ish-

spea

king

Car

ibbe

an (T

oron

to)

362

Latin

Am

eric

an (

Mon

trea

l)

364

Sout

h A

sian

(V

anco

uver

)

154

Ado

lesc

ents

livi

ng in

J.R

.C. (

Que

bec

435

Wom

en u

nive

rsity

stu

dent

s (Q

uebe

c)

134

Wom

en w

ho h

ave

had

an a

bort

ion

(hos

pita

l,

378

Wom

en w

ho h

ave

had

an a

bort

ion

(hos

pita

l,

96 H

IV s

erop

ositi

ve g

ay m

en (

Que

bec a

nd M

ontr

eal)

met

ropo

litan

urb

an a

rea)

(con

dom

)c

Que

bec)

cont

rol p

ills)

c Q

uebe

c)

187

190

27

182

180

30

364

-

31

96

-

36

110

44

16

-

435

23

-

134

28

-

378

23

9 A

pplic

atio

n of

uni

vers

al p

reca

utio

n m

easu

res

(UPM

)a

10 T

o su

gges

t pho

ning

for a

co

mm

unity

dri

ve-h

ome

serv

ice

follo

win

g al

coho

l con

sum

ptio

na

11 T

o ph

one

ones

elf f

or a

com

mun

ity

driv

e-ho

me

serv

ice

follo

win

g al

coho

l con

sum

ptio

na

12 C

ompl

ianc

e w

ith h

yper

tens

ion

cont

rol m

edic

atio

na

13 T

o ea

t foo

d lo

w in

fat?

14 T

o w

rite

clin

ical

inf

orm

atio

n on

pa

tient

sm

art c

ardc

15

To

eat h

ealth

y fo

odc

16 T

o ta

ke h

orm

one

repl

acem

ent a

t m

enop

ause

C

172

Nur

ses

in a

regi

onal

hos

pita

l (R

ivik

re d

u Lo

up,

255

You

ng a

dults

(Que

bec

prov

ince

) Q

uebe

c)

14

115

286

You

ng a

dults

(Q

uebe

c pr

ovin

ce)

135

202

Patie

nts

at a

fam

ily m

edic

ine

clin

ic (L

ava1

421

Patie

nts

at a

fam

ily m

edic

ine

clin

ic (S

t.-Sa

crem

ent

hosp

ital,

Que

bec)

68

hosp

ital,

Que

bec)

17

7 52

39

(Que

bec

met

ropo

litan

urb

an a

rea)

-

72 G

ener

al p

ract

ition

ers (

Rim

ousk

i, Q

uebe

c)

387

Supp

ort s

taff

at U

. L. (

Que

bec)

64

4 W

omen

age

d 40

to 5

0 at

pre

men

opau

sal

stag

e

155

140

151

134

244 20

34

4

644

39

21

21

63

35

38

41

48

Not

e. J.

R.C

. = Ju

veni

le R

ehab

ilita

tion

Cen

ters

; pat

ient

sm

art c

ard

= a

pers

onal

com

pute

r car

d al

low

ing

phys

icia

n to

writ

e cl

inic

al in

for-

mat

ion.

aP

ublis

hed

pape

r. bu

npub

lishe

d th

esis.

CRe

sear

ch re

port

and

pres

ente

d at

a sc

ient

ific c

onfe

renc

e.


Table 2

Number of Items and Cronbach ’s Alpha Values for the Direct and Belief-Based Measures in the 16 Studies

C(nbbX C(CbX

Aact SN PBC x(bbx eb) mcu) Pb) Study

no. ni CL ni a ni a a Hi Q ni CL

1 6 .81 3 .79 3 .63 10 .79 9 .88 F: 7 .79 M: 5 .63

2 6 .79 3 .69 3 .79 9 .72 9 .81 F: 7 .65 M: 6 .59

3 6 .84 3 .87 3 .83 7 .76 7 .89 7 .68 4 6 .64 1 - 3 .72 8 .42 4 .69 5 .42 5 6 .82 3 .70 3 .77 6 .62 6 .80 10 .62 6 4 .68 1 - 3 .72 6 .51 4 .69 4 .37 7 6 .76 3 .79 3 .85 6 .64 3 .66 4 .54 8 4 .84 3 .78 3 .87 8 .73 4 .78 8 .62 9 6 .81 4 .81 4 .78 7 .41 6 .85 4 .63

10 3 .80 1 - 3 .69 7 .62 6 .80 3 .82 11 3 .68 1 ~ 3 .65 10 .73 5 .79 3 .76 12 7 .78 3 .81 3 .63 8 .63 4 .64 3 .35 13 6 .84 1 - 3 .61 13 .68 5 .76 6 .56 14 6 .77 1 - 3 .77 11 .82 4 .76 2 .25 15 6 .83 1 - 3 .37 11 .87 5 .84 8 .55 16 6 .88 3 .75 2 .44 11 .73 7 .77 4 .66

Note. ni = number of items. (x = Cronbach’s alpha values. F = females. M = males. Aact = attitude; SN = subjective norm; PBC = perceived behavioral control; b = belief; e = outcome evaluation; nb = normative belief: mc = motivation to comply; c = control belief; p = perceived power.

Validi<\* of Aact, SN, and PBC

Table 3 shows the Spearman correlation coefficients between Aact, SN, PBC, and behavioral intention. For all of the studies. the three variables were statistically related to behavioral intention. Moreover, except for PBC in Studies 15 and 16 (Table 2), a satisfactory Cronbach’s alpha was obtained for each direct construct. Hence, Aact, SN, and PBC were valid criteria fo rb x e, nb x mc, and c x p, respectively.


Table 3

Spearman Correlation Coefficients Behveen Aact. SN, PBC, and Behavioral Intention

Study no. n Aact SN PBC

1 359 .54 .59 .62 2 345 .52 .6 1 .70 3 350 .72 .56 .8 1 4 83 .26 .32 .56 5 150 .66 .47 .80 6 404 .34 .19 .49 7 134 .65 .s3 .79 8 378 .5 1 .5 1 .7 1 9 171 .43 .42 .60

10 255 .34 .3 1 .32 1 1 284 .35 .22 .15 12 20 1 .25 .29 .36 13 419 .53 .3 1 .6 1 14 69 .52 .29 .70 15 368 .45 .27 .58 16 640 .77 .54 .63

Note. All correlations are significant a t p = .05. Aact = attitude; S N = subjective norm; PBC = perceived behavioral control.

Construct 0peru:ionalization

In order to avoid useless comparisons, the scaling methods tested in this study were those currently recommended by Ajzen and Fishbein (1980) and Ajzen (1991) and by others reported in the literature. For Aact, two models were tested: bb x eb and b, x eb. Regarding SN, the models contrasted were nbb x mc, and nbb x mcb. Finally, concerning PBC, all four models were tested; that is, Cb x Pb, Cb x p,, c, x Pb, and cu x p,. Scoring a scale as unipolar or bipolar consisted of assigning positive (e.g., +1 to +7) or positive and negative numbers (e.g., -3 to +3) to subjects’ answers, respectively. The range of the scale values varied with the number of response options offered. The response format was not manipulated in these studies. Also, it should be noted that it was not possible to make a comparison between different scoring methods for all 16 studies. Indeed, some scales were intrinsically unipolar; thus, the assignment of a negative score would have been inappropriate.


Table 4

Spearman Correlation Coeficients Behveen 23, 22, Z(b x e), and Aact Using Different Scoring Methods

Study no.a n Cb Ce C(bb X eb) x ( b ~ eb)

4 94 .32 -.23 .27 .02 5 150 .56 .24 .50 .45 6 404 .58 .04 .47 .36 7 134 .58 .15 .62 .48 8 378 .55 .06 .54 .42 9 170 .28 .2 1 .24 .33

10 253 .30 .08 .28 .22 11 2 84 .32 .12 .38 .30 13 42 1 S O .03 .43 .29 14 70 .55 .39 .60 .53 15 3 80 .40 .35 .44 .43 16 636 .64 .05 .65 .44

Nore. b = behavioral belief; e = outcome evaluation; Aact = attitude. aStudies I , 2,3, and 12 are not included in this analysis.

Statistical Tests

Spearman correlation coefficients were computed between the appropriate direct and belief-based measures and between the direct measures and one arm of the expectancy-value model (i.e., Cb, Xnb, Cp). Moreover, the Friedman test or the Wilcoxon signed-rank test were computed in order to test the statistical sig- nificance of the overall difference between the correlation coefficients. With the Wilcoxon test, the difference between correlation coefficients was computed in a way that takes into account the sample size (n,,,p~e/ntota~) so that correlations based on larger samples receive greater weight than do those from smaller samples.

Results and Discussion

Scaling Method

Table 4 shows the Spearman correlation coefficients between belief-based x eb and and corresponding direct measures of attitude when b x e was scored


b, x eb. The comparison of bb x eb and b, x eb scaling was possible in 12 of the studies. For 11 of these 12 studies, the bb x eb scaling approach yielded similar or higher correlations with Aact than did the b, x eb method; the exception being observed in Study 9 concerning application of universal precaution measures. The Wilcoxon signed-rank test revealed that, overall, the correlation between b x e and Aact was higher when a bipolar scaling was used for both b and e (S = 34.0, p = .OO, N = 12). These findings are in agreement with published data that indicate that the correlation between Aact and Cbe is almost always better when the bb x eb scaling method is used. Sparks et al. (1991) suspected that a unipolar scoring forb may yield a higher correlation between Aact and Cbe when subjects wrongly consider the scale as unipolar, as may be the case when the endpoints of the bipolar belief scale are not qualified with extreme terms. This interpretation, however, was not supported in this research because the opposite poles of the belief scale in Study 9, qualified as high!)? unlikelv and highly likelv, were the same as in Studies 4 to 8 and 13. More research is needed to understand why a unipolar rather than a bipolar scoring method for belief may sometimes yield a higher correlation between Aact and Xbe.

The results concerning SN are presented in Table 5. The operational definition of mc on a unipolar or a bipolar scale was possible in 5 of the 16 data sets. In all cases (5 out of S ) , the SN - C(nb x mc) correlation was better with the nbb x

mc, scaling method, compared to the nbb x mcb approach. However, the Wilcoxon signed-rank test indicated that, overall, the correlation between SN and C(nb x rnc) was not different when a unipolar instead of a bipolar scale was used for mc (S= 7 . 5 , ~ = .06, N = 5). The relatively low number of studies may explain that the difference was not statistically significant. It should be emphasized, however, that the findings may have been influenced by the fact that in all five studies reviewed, the nbb x mc, scoring led to a greater variability of scores in compari-

Ajzen and Fishbein (1980) argued that the motivation to comply must be viewed as a unipolar concept because individuals are generally motivated to comply with salient referents. It should be noted, however, that in four of the five studies, about half of the items had a negative mean value. In other words, a sub- stantial proportion of the subjects was not motivated to comply with about half of the salient referents mentioned in the questionnaire. Although not presented, a similar pattern was observed for the other 11 studies that used intrinsically unipolar scales. Subjects had a tendency to indicate that they were sometimes, rather than often or always, motivated to comply with most of the referents listed, suggesting that they were not very strongly motivated to comply with most of the salient referents that were specified. This could be explained by a reactive measurement problem: Subjects want to appear autonomous in their decisions to adopt a given behavior (Saltzer, 1981). It may also indicate that subjects are motivated to comply with a limited number of salient referents. Indeed,

son to the nbb X mCb approach.


Table 5

Spearman Correlation Coeficcients Between Znb, zinc. C(nb x mc), and SN Using Different Scaling Methods

1 2 3 4 6 9

10 11 12 13 14 15 16

373 360 362

94 432 169 254 282 190 382

69 345 568

.54

.42

.61

.34

.45 S O .32 .38 .63 .68 .49 .54 .66

.30

.09

.20 -. 14 .11 .12 .07 .o 1 .13 .25

-.o 1 -.o 1 .29

.55

.44

.60

.05

.3 1

.35

.28

.29

.52

.67

.5 1

.36

.67

-

-. 18 -.01

-

-.08 .47 .oo

Note. nb = normative belief; mc = motivation to comply; SN = subjective norm aStudies 5 , 7, and 8 are not included in this analysis.

individuals may obtain the advice of several individuals in their social environ- ment, but may consider the opinion of only a limited number of them. Similarly, it is possible that a given referent is salient only for specific behaviors. Finally, the use of modally rather than personally salient referents may explain why the subjects are not strongly motivated to comply with many of the referents listed in the questionnaire.

In summary, the results show the importance of establishing a distinction between scoring method and scale format for the mc variable when modally salient beliefs are used. Indeed, although a unipolar scoring for mc (e.g., +1 to +7) generally yields a closer SN - C(nb x mc) correlation, the format of mc scale should offer bipolar response options; that is, response options translating both agreement and disagreement, since many subjects were not motivated to comply with at least half of the referents listed in the questionnaire. Additional studies will be needed to verify the impact of offering a real unipolar scale format for mc with only one disagreement response option and several agreement response options versus a real bipolar scale format with several response options translating both agreement and disagreement.


Table 6

Spearman Correlation Coe8cients Between 2, Zp, Z(c xp), and PBC Using Dgerent Scoring for cp

Study no. n Zc Zp c(cbx Pb) C(cbx Pu) C(cux Pb) C(cux Pu)

1

2

3 4 5 6 7 8 9

10 11 12 13 14 15 16

M: 184 F: 184

M: 178 F: 177

361 94

150 435 134 378 171 253 282 20 1 412

71 382 643

.60 .56 S 3 -67 .35 .58 .so .49 .45 .69 .07 S O .46 .74 S 3 .46 .44 .62 .51 .54 .41 .62 .22 .20 .25 . I 8 .19 .33 .35 .56 .39 .55 .35 .58 .56 .53

.60

.57

.47

.so

.67

.20

.49

.so

.5 1

.47

.so

.26

.25

.28

.45

.30

.19

.46

.63

.55

.42

.53

.60

.14

.53

.53

.52

.54

.46

.26

.26

.24

.44

.4 1

.29

.so

.6 1

.68

.59

.55

.72

.35

.68

.53

.60

.56

.6 1

.25

.24

.33

.57

.53

.57

.52

.64

.63

.55

.58

.72

.26

.70

.54

.59

.60

.55

.27

.25

.30

.54

.49

.49

.56

Note. M = males; F = females; c = central belief; p = perceived power; PBC = perceived behavioral control.

Table 6 presents the correlation between PBC and Z(c x p) for scoring c x p either on unipolar or bipolar scales. Two scaling methods yielded higher correlations with PBC: c, x p, (8 of the 18 analyses) and c, x Pb (10 of the analyses). There is no evidence that ch x Pb or Cb x p, could be the optimal scoring method, as suggested by Ajzen (1991). If the two methods that emerge from this analysis are compared, it can be seen that the difference between them is not very large. Generally, the differences in correlation coefficient values varied between .O 1 and .04. The Friedman test revealed that the correlation between PBC and Ccp may vary according to the scaling method, F(3, 17) = 1 9 . 1 4 , ~ = .OO. Multiple comparisons indicated that, at an alpha level of .05, correlations implying both c, x p, and c, x Pb scaling methods differed significantly from correlations

2188 GAGNE AND GODlN

implying Cb x Pb and C b x p, approaches. However, there was no difference between c, x p, and c, x Pb or between Cb x Pb and Cb x p,, The higher correlation between PBC and C(c x p), when c, x p, and c, x Pb scaling methods are used, may be partially explained by the fact that these latter methods led to a higher variability of scores than did cb x Pb and Cb x p, approaches.

The data in Table 6 were, however, those obtained when the item analysis was made on c x p, operationalized as Cb x Pb, as with Ajzen (1991). Reliability analysis with this scoring method resulted in the rejection of few items (in 1 1 out of 18 studies) in order to obtain satisfactory Cronbach’s alphas. It should be noted that more items would have been rejected with an items analysis performed on C(c x p) scored Cb x p,. To verify more carefblly the adequacy of the c, x p, and c, x Pb scaling approaches, the PBC - C(c x p) correlation coefficients were scrutinized again, but with an item analysis performed on the c x p construct operationalized the same way as the scaling method tested. This time, no item had to be rejected to obtain an appropriate alpha value. This was true for both the c, x p, and c, x Pb methods. The results also indicate that in 14 of the 18 analyses, scoring cp as c, x pb, compared to c, x p,, yielded slightly better Cron- bach’s alphas. In spite of this apparent better performance of the c, x Pb method, however, it must be noted that a satisfactory Cronbach’s alpha of .65 or more was obtained in 15 of the 18 analyses performed with the c, x p, method, in comparison to 13 of the 18 analyses with the c, x Pb model. With respect to the correlation between PBC and C(c x p), the highest correlation was observed for c, x p, in 6 of the 18 analyses, whereas the c, x Pb was the best model in 9 of the analyses. The correlation coefficients were equivalent in 3 of 18 analyses. The Wilcoxon signed-rank test indicated that the correlation between PBC and C(c x p) was not statistically different according to the scaling method used (i.e., c, x

Thus, taken together, the results suggest that the c, x p, and c, x Pb scaling methods are particularly valuable. Finally, it should be emphasized that the sug- gestion to use the c, x p, or the c, x Pb method does not imply developing real unipolar scale formats that do not offer the possibility of taking into account opposing views. Indeed, the presence of c and p items with a negative mean value suggests that it is preferable to use a true bipolar scale format in order to secure the validity of the measure. What is suggested is to assign positive scores (e.g., +1 to +7) when a unipolar scoring method is adopted.

p, Or C, X pb; s = 23.5, p = .32, N = 18).

Adequacy of the Expectanc-v- Value Model

The results concerning the second research question, dealing with the effect of omitting one arm in the sum of products of b x e, nb x mc, and c x p are presented in Tables 4, 5, and 6. The results concerning the b x e variable are dis- played in Table 4. Consistent with the observations of Hom and H u h (198 l), the


results of the present research indicate that the correlation coefficients between Aact and Zb were often similar or better (in 8 out of 12 studies) than were the correlation coefficients between Aact and C(bb x eb). This conclusion was also true when the belief-based attitude variable was scored b, x eb (10 out of 12 studies). The Wilcoxon signed-rank test indicates that the difference between Aact - Cb and Aact - C(b x e) correlations was statistically significant when a unipolar scoring was used forb (S = 1 1 . 0 , ~ = .42, N = 12), but not when a bipolar scoring was adopted forb (S = 3 2 . 0 , ~ = .01, N = 12).

A few authors (Lechner, 1998; Smith & Clark, 1973) have suggested that the low variance in the e variable may be responsible for its low influence on the correlation with Aact when it is combined with b. However, such an interpretation is not supported in this research since the mean variance of e was greater in studies where the correlation between Aact and Zb was stronger than in studies where the correlation between Aact and Z(b x e) was the best model. Thus, based on the present findings, the use of Cb provides as good a correlation coefficient with Aact as does the composite score of C(b x e).

In Table 5, it can be observed that for all of the studies, and whatever the scaling approach adopted, the inclusion of the mc variable suppressed or yielded similar correlation coefficients between SN and C(nb x mc). The difference between SN - Cnb and SN - C(nb x mc) correlations was statistically significant when mc was scored in a unipolar fashion (S = 3 0 . 5 , ~ = .03, N = 13), but not when bipolar scoring was adopted for mc (S= 7 . 5 , ~ = .06, N = 5) . This supports the idea that motivation to comply does not add to the predictive power of Cnb. This is in agreement with the observations of Budd et al. (1984) and Hom and Hulin (1981).

Three factors have generally been suggested to explain why mc suppresses the SN - C(nb x mc) correlation: the low variance of the mc variable (Ajzen & Fishbein, 1970; Miniard & Cohen, 1981); social desirability bias, which could lead the subject to indicate that he or she is not influenced by others concerning his or her behavior performance (Hom & Hulin, 1981; Saltzer, 1981); and the possibility that the SN formulation better translates the nb concept, compared to the mc dimension (Fishbein, 1976). The results of the present research, however, do not suggest that low variance is an explanatory factor. Indeed, the comparison of nb and mc items having the same number of response options-which is a pre- requisite because the variance of an item is influenced by the number of response options offered-indicated that the variance was sometimes greater for the Cmc, compared to the Cnb (in 3 out of 7 studies). Further research will be necessary to clarify the role of social desirability bias or the effect of SN formulation on the SN - C(nb x mc) correlation.

In Table 6, it can be seen that the p variable alone yielded similar or higher correlation with PBC in 14, 13, 12, or 10 of the 18 studies, depending on the scaling method for C(c x p). The Wilcoxon signed-rank test indicated that the

2190 GAGNE AND GODlN

differences between PBC - Cp and PBC - c(c x p) correlations were statistically significant when Z(c x p) was scored cb x pb ( S = 5 9 . 5 , ~ = .01, N = 18) and cb x p, ( S = 52.S,p= .02, N = 18), but not when the scaling methods used were c, x Pb ( S = - 3 1 . 5 , ~ = .18, N = 18) or c, x pu ( S = - 1 6 . 5 , ~ = .49, N = 18). Similar results were obtained when the items analysis was performed on c, x Pb ( S = 11.5, p = .63, N = 18) or c, x p, ( S = 9.5, p = .70, N = 18). At first glance, this result could not be explained by the low variance of the Cc variable. Indeed, for all of the scaling methods in Table 6, the mean variance of the Cc variable was higher in studies where the PBC - Cp correlation was better than in studies where the correlation between PBC and Ccp was the best model. One explanation could be the formulation of the p items. Indeed, the c items generally assess if an individual anticipates bamers or obstacles to the adoption of a given behavior or if he or she foresees the presence of facilitating factors. In translating what the subject expects to do if the barriers or facilitating factors were present, the p items appear to capture more information on the individual’s perception of control. Finally, Cp corresponds to the concept of self-efficacy formulated by Bandura (1977). Thus, it is possible that this operational definition fully accounts for one’s perception of control or perceived self-efficacy in adopting a given behavior.

The results of this research suggest that, for most situations in the domain of health-related behavior, bb x eb, nbb x mc,, c, x Pb, or c, x p, yield higher correlation coefficients with the corresponding direct measures. However, the face validity of these scaling methods must be demonstrated. Moreover, the results of this research suggest that using only Xb, Cnb, and Cp, instead of C(b x e), C(nb x mc), and C(c x p) yields similar or higher coefficient correlations with their specific direct measures. This latter conclusion holds, regardless of the scaling method adopted for defining the indirect constructs. Thus, this research tends to confirm, as suggested by Ajzen (1991). that the expectancy-value model may not be the best to describe the relationship between direct and indirect measures of constructs of the TPB.

It should be emphasized that using only one arm of the belief-based construct of attitude, SN, and PBC may result in a loss of information for differentiating subjects who have and those who do not have the intention to adopt a behavior. However, using only one arm, rather than the sum of the product variables, presents a few advantages. It ensures that the scaling problem resulting from multi- plying two variables not measured on a ratio scale is avoided, and it also leads to a reduction in the number of items presented to subjects. Such a reduction in the number of items is apt to lower the administration time of the questionnaire and, consequently, to reduce subjects’ fatigue and boredom when answering redun- dant questions. Hence, more valid information may be obtained. However, assessing a given belief-based construct and deciding afterward to use only one arm may not be equivalent to assessing and analyzing only one component. Indeed, many studies have indicated that subjects who filled out questionnaires


may be influenced by the order of questions, as well as by the information contained in the preceding questions (e.g., Schwarz, Strack, & Mai, 1991; Tourangeau, Rasinski, Bradburn, & D’Andrade, 1989a, 1989b). Consequently, additional studies comparing responses to a questionnaire with all of the variables proposed by the theory with another omitting the e, mc, and c items will be necessary before a more definite conclusion can be reached.

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