Assessing the Achievement Motive Using the Grid Technique

Journal of Research in Personality 33, 109–130 (1999) Article ID jrpe.1999.2245, available online at http://www.idealibrary.com on

Assessing the Achievement Motive Using the Grid Technique Heinz-Dieter Schmalt University of Wuppertal, Germany This article has three goals: first, to present a new technique for measuring the achievement motive that jointly incorporates both situational (pictures) and personality variables (statements) within the framework of an interactional conception of motivation; second, to test this instrument for its psychometric properties; and third, to document its construct validity and potential usefulness in instructional settings. The grid technique and its situational and personality constituents are described in detail. Factor analyses of the statements all recommend the adoption of a threefactor solution in which one hope-of-success and two fear-of-failure concepts are differentiated. Reliability and validity studies demonstrate that the grid technique is a psychometrically sound measure of the achievement motive and that it possesses substantial construct validity. Behavioral differences found in regard to performance and persistence can easily be integrated into an achievement motivation nomological network. Furthermore, the effectiveness of motive-modification programs in schools can be documented using the grid technique.  1999 Academic Press Key Words: achievement motivation; grid technique; motive measurement; persistence.

Traditionally there have been two main approaches to finding out about one’s motives. The first is simply to ask a person what his or her motives are or to ask him or her about motive-related thoughts and actions. The second is to analyze daydreams and other fantasies in order to detect hidden, repressed, or secret motives. Indirect motive measurement usually involves confronting individuals with a set of ambiguous pictures and asking them to make up fanciful stories about the pictures, a method originally developed by H. Murray (1938). Since the pioneering work of McClelland and his associates on the achievement motive (McClelland, Atkinson, Clark, & Lowell, 1953), measurement of the motive has been by means of the Thematic Apperception Test (TAT). The stories written in response to the pictures are scored for a variety of achievement subcategories, such as stated need for achievement, Address correspondence and reprint requests to Heinz-Dieter Schmalt, Fach Psychologie, University of Wuppertal, Fachbereich 3, Gaussstraße 20, 42097 Wuppertal, Germany. E-mail: [email protected]. 109 0092-6566/99 $30.00 Copyright  1999 by Academic Press All rights of reproduction in any form reserved.

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anticipatory positive and negative goal states, instrumental activity, and positive and negative affective states associated with achievement strivings. The sum of theses categories is assumed to reflect the strength of the underlying motive. It is contended that the degree to which these categories are elicited by the pictures corresponds to the degree of elicitation in similar real-life situations. To put it more formally: What is measured by this method is the way in which a person construes the world spontaneously or how often his or her thoughts turn spontaneously to certain themes such as achievement (McClelland, 1971, p. 13). Thoughts concerning the achievement motive should be readily accessible in memory and should easily be triggered by achievement situations. Thus diagnostic endeavors are strictly guided by an interactionist position assuming that achievement behavior is influenced by dispositional as well as by situational factors (Snow & Jackson, 1994). Although TAT procedures such as those proposed by McClelland have been criticized within the framework of classical test theory for not having test–retest and internal consistency reliability (cf. Entwisle, 1972), there is overwhelming evidence for its construct validity (Dweck & Elliott, 1983; Heckhausen, Schmalt, & Schneider, 1985; McClelland, 1985). This reliability–validity paradox has been resolved in the meantime, however (Atkinson, 1982; Heckhausen et al., 1985, p. 22; Spangler, 1992). Critics have also pointed out that questionnaire and TAT measures of the achievement motive almost always fail to correlate, thus demonstrating lack of convergent validity. This led McClelland and his coworkers (McClelland, Koestner, & Weinberger, 1989) to argue that the motives measured by TAT and those measured by questionnaires are conceptually different. They made a distinction between implicit motives, as measured by the TAT, and selfattributed motives, as measured by questionnaires, the latter being conscious self-descriptions. Implicit motives interact with natural or activity incentives, whereas self-attributed motives respond to external or to social incentives, such as goals and demands expressed by others. Furthermore, the two motives have been assigned construct validity in different areas. Implicit motives are hypothesized to be good predictors of what people really do, how people spend their time, and of long-term operant behavior like career developments. Self-attributed motives are claimed to be valid predictors of attitudes, values, goals, or duties, especially when effort is required (McClelland et al., 1989; McClelland, 1995). The research presented here introduces a new technique for measuring human motives. The guiding idea is to merge the TAT and questionnaires, thus combining the merits of operant and respondent measures. First, I describe how the measure developed, including the use of pictures and statements. Then I report on several studies in which the psychometric features— especially factor structure and diverse aspects of reliability—were tested.

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Finally I refer to correlational and experimental data that corroborate the validity of the new technique. THE ACHIEVEMENT MOTIVE GRID When I started to develop a new technique for the measurement of the achievement motive (in this case, a children’s version) some years ago, I decided to try a ‘‘third way’’ in order to combine the benefits of TAT and questionnaire measures (Schmalt, 1976). This meant developing a test which has demonstrable psychometric qualities in the classic sense but at the same time has the validity of the TAT. The so-called Achievement Motive Grid (AMG) is a self-report measure which, like the TAT, is based on an interactional model of personality which posits that the interaction is manifested in the evaluative representation of a situation by the individual. So it was decided not to directly assess global attitudes but to arouse the achievement motive situationally. In contrast to the TAT, however, a prearranged set of verbal statements was used which describe typical thoughts, expectancies, aspirations, and behavioral manifestations of the achievement motive. Persons were asked to consider these statements and to check those statements that they thought fit the situation. Thus the grid technique resembles the TAT in that motive arousal is by pictorial stimuli but resembles questionnaires in its test responses. These resemblances to and differences from the TAT procedure led us to characterize the grid technique as a ‘‘semi-projective’’ device. Situations The AMG consists of 18 pictured situations in which achievement could play a role. We collected descriptions of situations where persons could feel challenged and could compete with standards of excellence. The descriptions were transformed into pictures (see Fig. 1). Only those situations were retained that have a more than ephemeral achievement-related characteristic, documented by the fact that they have been used with TAT procedures ever since (McClelland et al., 1953; Smith, 1992). The pictures were clearly structured as far as the concrete activity is concerned; they were ambiguous, however, in regard to the sex of the portrayed persons and in regard to possible success or failure outcomes. This is quite in line with the TAT procedure: using pictures that are moderately arousing but ambiguous in regard to success or failure. Statements Underneath each picture the achievement-related statements were listed. These statements represent some of the most important and empirically documented cognitive, emotional, and behavioral correlates of the approach (Hope of Success, HS) and avoidance (Fear of Failure, FF) motives. These

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FIG. 1. Picture situations of the AMG from the following domains: Manual Activities (Picture 1), Asserting Independence (Picture 6), Sports (Picture 12), and Scholastic Activities (Picture 16). From Das LM-Gitter by H.-D. Schmalt. Go¨ttingen: Hogrefe. Copyright 1976 by Hogrefe-Verlag; reprinted with permission from the publisher.

two motives represent an individual’s anticipatory goal reactions to potential success and failure outcomes. This fundamental dichotomy has retained its relevance in contemporary conceptions (Covington & Omelich, 1991; Higgins, 1997) and even in modern goal theories of achievement motivation (Elliot & Harackiewicz, 1996; Elliot & Church, 1997). The only convincing TAT-based procedure for a simultaneous and independent assessment of the HS and FF motives was developed by Heckhausen, whose scoring categories were empirically refined and validated (see Heckhausen et al., 1985, p. 21). This enabled statement development to be guided by these scoring categories (see Table 1). More precisely the AMG statements pertain to the anticipation of success and failure, to levels of expectancy of success and failure, to preference patterns for tasks of intermediate or extreme difficulty, and to instrumental actions in ensuring success or preventing failure. Different selfconcepts of ability were also taken into account.

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TABLE 1 Statements Used in the Grid Test of the Achievement Motive TAT content category — If E G Ef Gf — If N E Ef — Nf N N — I Ef

Statements 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.

He feels good doing this. He thinks: ‘‘If that is difficult, I’d rather finish some other time.’’ He believes he will be able to do that. He thinks: ‘‘I’m proud of myself because I can do that.’’ He thinks: ‘‘I wonder if anything is wrong?’’ He is not satisfied with what he can do. He is getting tired doing this. He thinks: ‘‘I’d rather ask someone to help me.’’ He thinks: ‘‘I want to be able to do that some day.’’ He believes he did everything right. He’s afraid he could do something wrong. He doesn’t like that. He doesn’t want to do anything wrong. He wants to be able to accomplish more than all the others He thinks: ‘‘Most of all I want to do something that is a little bit difficult.’’ 16. He prefers to do nothing at all. 17. He thinks: ‘‘If that’s very hard I’ll surely try longer than others.’’ 18. He thinks he can’t do that.

Scale — FF1 — HS FF2 FF1 — FF1 HS — FF2 FF1 FF2 HS HS FF1 HS FF1

Note. Content categories were taken from the Heckhausen TAT: I, Instrumental activity; E, Expectancy; G, Affective State; N, Need. An additional f indicates failure orientation of the corresponding categories.

INITIAL SCALE DEVELOPMENT Method Overview. The purpose of the first studies was to identify a common set of statements that represent the HS and FF motives. As mentioned above, each motive was assessed independently and it was assumed that they are unidimensional constructs. The AMG yields data from i subjects in j (⫽18) situations for k (⫽18) statements that can be put in a three-dimensional data block (i*j*k). The first two studies reported below were conducted on the basis of i*k data matrices by adding up the raw scores across all situations, thus eliminating situational variance. This procedure allows the application of more conventional techniques of factor analysis. The third study analyzed the original i*j*k data block by means of three-mode factor analyses (Tucker, 1966). This study was done by Ro¨sler, Jesse, Manzey, and Grau (1982). Two-mode principal-axis analyses were conducted with communalities estimated by R 2. Threemode factor analyses were conducted by a principal-components analysis because communalities are not defined for the case of a three-mode factor model. For each sample two-, three-, four- and five-factor solutions were rotated to a Varimax solution. In the fourth study maximum likelihood confirmatory factor analyses via AMOS 3.6 (Arbuckle, 1997) were performed in order to focus more closely on the three-factor structure which emerged from the exploratory analyses. This model was evaluated in several steps and

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by different methods (see Table 3). First the fit was assessed by chi-square statistics, the goodness-of-fit-index (GFI), and the adjusted-goodness-of-fit index (AGFI) (Jo¨reskog & So¨rbom, 1984). In contrast to the chi-square statistics, the GFI and AGFI are not decisively influenced by sample size. Large data sets can produce significant chi-squares, not because of suboptimal fit but because with larger sample sizes, smaller differences appear to make a significant contribution. Next the significance of the path coefficients was estimated, and, last, comparisons of alternative factor solutions were calculated. These models were compared by global indices and by information-theoretic measures like Akaike’s information criterion (AIC, Akaike, 1987), the Browne-Cudeck criterion (BCC, Browne–Cudeck, 1989), and the Consistent AIC (CAIC, Bozdogan, 1987). These analyses are intended for the comparision of different (not nested) models and not for the evaluation of an isolated model. Participants. Study 1: Research participants consisted of 279 third- and fourth-graders (121 boys, 158 girls), 9 to 12 years of age. Study 2: Research participants consisted of 436 thirdto eighth-graders (228 boys, 208 girls), 9 to 16 years of age. Study 3: Research participants were 198 third- and fourth-graders (104 boys, 94 girls). Study 4: Research participants were 280 fourth-graders. Reliability study: Research participants were 140 third- and fourth-graders (61 boys, 79 girls), 9 to 12 years of age. Participants voluntarily completed the AMG while attending school. The research was conducted in different regions of northern and western Germany, ranging from more rural to metropolitan-like districts. Experimenters’ instructions stressed the informal nature of the task and asked participants to complete the AMG for research reasons.

Results Exploratory factor analyses. The number of eigenvalues greater than unity was 4 in the first two studies and 5 in the third study. As a criterion for determining the optimal number of interpretable factors, a scree plot of the relative magnitude of eigenvalues was used. In order to determine the point at which factors no longer explain substantial amounts of additional variance, the differences between adjacent eigenvalues were taken into account. The ordinal number of the last fairly big difference between two adjacent eigenvalues specifies the optimal number of interpretable factors. These differences were .56/.79/.77/.40/.30 in the first study and .71/1.38/.64/.25/.09 in the second study. In the third study, in which data were analyzed separately for boys and girls, these differences were .27/.64/.34/.01/.11 (for boys) and .34/.27/.47/.11/.06 (for girls). Thus all three studies strongly favored the adoption of a three-factor solution for the AMG statements. Three factors accounted for 42.8% (study 1), 40.3% (study 2), 31.7% (boys), and 30.6% (girls) (study 3) of the total variance. Gorsuch (1983) has noted that extracted variances of 40 to 50% of the variance reflect a factor structure of substantial impact. The first two studies, which used the conventional two-mode technique, conform to this criterion. Table 2 summarizes these studies. Table 2 presents the three-factor solutions for studies 1 and 2, for boys and girls combined, and for study 3, for boys and girls separately. The first factor in all solutions was clearly an FF factor as indicated by the corresponding TAT scoring categories and our theoretical considerations. The statements describe cognitive responses consisting of ruminative cognitions fo-

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cusing on lack of efficiency and personal inadequacies (e.g., ‘‘He is not satisfied with what he can do’’). Factor two in all studies was clearly an HS factor consisting of a positive evaluation of ones’ own efficiency (stated need to achieve) and expectations of success (e.g., ‘‘He thinks: I want to be able to do that some day’’). Factor three, a second FF factor, describes more emotional anticipation of impending failure (e.g. ‘‘He’s afraid he could do something wrong’’). Scale construction was based on this three-dimensional solution. Only statements with an a 2 /h 2 ratio above 50% and with factor loadings above .40 were retained (Stevens, 1992, pp. 382–383). This guarantees that only those statements were retained that made a significant contribution to each of the factors extracted. The following three scales resulted from factor analysis: Hope of Success (HS): Positive evaluation of efficiency (stated need to achieve) and initiation of actions designed to master difficult tasks (Statements 4, 9, 14, 15, 17). Fear of Failure (FF1): Negative evaluation of efficiency and initiation of actions to prevent failure (Statements 2, 6, 8, 12, 16, 18). Fear of Failure (FF2): Anticipation of impending failure (Statements 5, 11, 13). Scale intercorrelations were as follows: r HS/FF1 ⫽ .22, r HS/FF2 ⫽ .10, and r FF1/FF2 ⫽ .15 (study 1, N ⫽ 279). Two separate FF factors appear here along with one single HS factor. Clearly the FF1/FF2 discrimination is reminiscent of the cognitive-worry/ autonomic-emotional discrimination in anxiety research. As in the corresponding analyses of the Heckhausen TAT- scoring key (Kuhl, 1978) and of questionnaires of test anxiety (Endler, Edwards, Vitalli, & Parker, 1989; Endler, Parker, Bagby, & Cox, 1991; Liebert & Morris, 1967; Sarason, 1975; Wine, 1971), two separate FF factors emerge which separate subscales for cognitive-worry and autonomic-emotional anxiety. The HS and FF1 factors are based on positive and negative efficiency evaluations respectively. Most achievement theories identify efficiency evaluations, competence expectancies, and self-concepts of ability as important constituents of the achievement motive nomological network (Atkinson, 1957; Dweck & Elliott, 1983; Elliot & Harackiewicz, 1996; Harter, 1992; Elliot & Church, 1997). Confirmatory factor analysis. Following the findings of the exploratory factor analyses, a model with three factors, namely Hope of Success, Fear of Failure 1, and Fear of Failure 2 was postulated. It was predicted that items 4, 9, 14, 15, and 17 should load only on the HS factor; items 2, 6, 8, 12, 16, and 18 on the FF1 factor, and items 5, 11, and 13 on the FF2 factor. It was assumed that the three factors were uncorrelated and that the measurement errors of the observed variables were uncorrelated. In order to establish a common metric for the factors and to identify the model, one item of each subscale, called a reference indicator, was fixed to unity. Table 3 presents the results of the CFAs for the four models tested in the present investigation. The global model fit of the uncorrelated three-factor

2. He thinks: ‘‘If that is difficult, I’d rather finish some other time.’’ 6. He is not satisfied with what he can do.

4. He thinks: ‘‘I’m proud of myself because I can do that.’’ 9. He thinks: ‘‘I want to be able to do that some day.’’ 14. He wants to be able to accomplish more than all the others. 15. He thinks: ‘‘Most of all I want to do something that is a little bit difficult.’’ 17. He thinks: ‘‘If that’s very hard I’ll surely try longer than others.’’

Statement

.58 .59

.58

.54

.45

.48

.72 .59

.63

HS

.50

FF1 .66

FF2

.68

HS

.59

FF1

F2 FF2

F3

F1

F3

F1

F2

Study 2 (N ⫽ 436)

Study 1 (N ⫽ 279)

⫺.46

FF1

F1

.42

.62

.47

.57

HS

F2

F3 FF2

Study 3 (boys) (N ⫽ 104)

.52

.46

FF1

F1

⫺.47

⫺.45

⫺.47

⫺.63

HS

F2

F3 FF2

Study 3 (girls) (N ⫽ 94)

TABLE 2 Varimax-Rotated Factor Loadings, Eigenvalues, and Percentages of Total Variances for the AMG Statements for Three Independent Studies (Total n ⫽ 913)

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3.08 17.25

2.52 15.69

3.21 17.94

1.83 10.22

.66

.61

3.92 21.91

.66

.76

1.73 12.00

.79

.53 .62 .66

.65 .74 .63 .85

.57

.50

2.29 11.08

2.02 11.00

1.38 8.90

.61

.59

2.10 11.00

.56 .52 .52

⫺.64 ⫺.58 ⫺.62 .63

.50

⫺.52

1.76 9.80

1.59 9.40

⫺.68

⫺.74

⫺.74

b

Study 1 from Die Messung des Leistungsmotivs (pp. 110–111) by H.-D. Schmalt. Go¨ttingen, Hogrefe. Copyright 1976 by Hogrefe-Verlag. Study 2 from ‘‘Methodenkritische Untersuchungen zum LM-Gitter fu¨r Kinder (Schmalt)’’ by H.-D. Schmalt and W. Schab, 1984, Diagnostica, 30, p. 286. Copyright 1984 by Hogrefe Verlag. c Study 3 from ‘‘Ist das LM-Gitter nur ein LM-Test? Eine dreimodale Faktorenanalyse des LM-Gitters fu¨r Kinder (Schmalt)’’ by F. Ro¨sler, J. Jesse, D. Manzey, & U. Grau, 1982, Diagnostica, 28, p. 136. Copyright 1982 by Hogrefe Verlag. Adapted with permission of the author.

a

Eigenvalue Percentage of variance

5. He thinks: ‘‘I wonder if anything is wrong?’’ 11. He’s afraid he could do something wrong. 13. He doesn’t want to do anything wrong.

8. He thinks: ‘‘I’d rather ask someone to help me.’’ 12. He doesn’t like that. 16. He prefers to do nothing at all. 18. He thinks he can’t do that.

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TABLE 3 Goodness-of-Fit Statistics for Different Factor Models of the Achievement Motive Grid

Three-factor model uncorrelated Three-factor model correlated Two-factor model correlated One-factor model

χ2

χ 2 /d f

GFI

AGFI

AIC

BBC

CAIC

287 251.7 481.6 630.7

3.7 3.4 6.3 8.2

.87 .88 .79 .72

.82 .83 .71 .61

343 313.7 539.6 686.7

346.2 317.3 542.9 689.9

473.8 457.4 674 816.5

Note. AIC, BCC, and CAIC smaller values indicate better model fit (Bollen, 1989).

model was acceptable: χ 2(74) ⫽ 287, p ⬍ .001, GFI ⫽ .87, AGFI ⫽ .82. Because of the findings mentioned above, demonstrating moderately high correlations among the subscales of the AMG (see also Halisch, 1982), the same model was tested with the three factors correlated (r HS/FF1 ⫽ .36; r HS/FF2 ⫽ .30; r FF1/FF2 ⫽ .12). The results indicate that this model fits the data somewhat better: χ 2(77) ⫽ 251.7, p ⬍ .001, GFI ⫽ .88, AGFI ⫽ .83. The difference between the two χ 2 is significant: χ 2 difference (3) ⫽ 35.3, p ⬍ .001, so the correlated model is to be preferred. Those GFI values above .85 and AGFI values above .80 are generally interpreted as representing a good fit (Hayduk, 1987; Bryant, Yarnold, & Grimm, 1996). Finally, two alternative models were tested to compare them with the correlated three-factor model. First, a two-factor model with only one FF factor comprising FF1 and FF2 and the HS factor was used and, second, a model with only one common achievement motive factor was computed. The confirmatory factor analyses revealed a less-than-adequate fit for both alternative models. The indices AIC, BCC, and CAIC also imply that the correlated three-factor model fits the data best. Reliabilities Test–retest coefficients for the three subscales of the AMG ranged from a low of .67 to a high of .85 after a 2-week interval and ranged from .70 to .79 after an 8-week interval. Split-half reliabilities ranged from .84 to .93 (odd- versus even-numbered pictures). Nunnally (1978) has suggested that self-report measures with internal reliabilities in the .70-to-.80 range are acceptable for research purposes. The AMG subscales clearly exceed these benchmarks. These findings support the overall conclusion that the AMG is a reliable measure. In general the results of these studies suggest that the AMG scales are psychometrically sound measures of the different dimensions underlying the achievement motive. The AMG outperforms nearly all other measures of the achievement motive concerning internal consistency and stability across time (Fineman, 1977, p. 9; Klinger, 1966; McClelland et al., 1989; Spangler, 1992).

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TABLE 4 Correlations between AMG Subscales and Different Measures of the Achievement Motive, Anxiety, Intelligence, Performance, and the Tendency to Give Socially Desirable Answers Sample 1 (N ⫽ 35)

Sample 3 (N ⫽ 272)

Sample 2 (N ⫽ 86)

TAT

AFS

AMG

HS

FF

I

GPA

Lie Scale

CAT

TA

MA

HS FF1 FF2

.34* .10 .21

.06 .05 .04

.11 ⫺.25 .36*

.17 ⫺.32* .38*

⫺.07 ⫺.23* ⫺.20

.19 .23* .26*

.18** .18** .15**

.15** .13** .10

Note. HS, Hope of Success; FF, Fear of Failure; I, Intelligence; GPA, Grade Point Average [scores for GPA were reversed (multiplied by ⫺1)]; CAT, Children’s Anxiety Test; TA, Test Anxiety; MA, Manifest Anxiety. * p ⱕ .05; ** p ⱕ .01 for two-tailed tests.

CONVERGENT AND DISCRIMINANT VALIDITY To clarify the empirical relationships between the AMG and other achievement variables, some selected studies are now considered in order to document convergent and discriminant validity. First, it can be posited that the AMG taps implicit motives and hence shows positive correlations with the TAT. Furthermore, a positive correlation between AMG FF and questionnaire measures of anxiety is predicted. This is based on the tradition of measuring the fear-of-failure motive by means of anxiety scales. According to McClelland (1971, p. 10), the self-report measure of anxiety is adequate for assessing the avoidance motive because ‘‘. . . it calls for identifying failed demands that occur externally rather than fleeting fantasies of doing better that occur internally.’’ Thus the anxiety experienced in an achievement situation is proportional to the magnitude of the fear-of-failure motive (Atkinson, 1964, pp. 289–290). Only very low correlations were expected between the AMG and a measure of general intelligence as well as a measure of the tendency to give socially desirable answers. Method Participants. Samples consisted of fourth-graders only; sample sizes were N ⫽ 35 in study 1, N ⫽ 86 in study 2, and N ⫽ 272 in study 3 (see Table 4). Materials. Besides the AMG the following measures were used: (1) TAT: The TAT as developed by Meyer, Heckhausen, and Kemmler (1965). It is a children’s version of Heckhausen’s TAT (see above). As with the original TAT, the children’s version yields separate scores for Hope of Success (HS) and Fear of Failure (FF). (2) CAT: The Children’s Anxiety Test (KAT, Thurner & Tewes, 1969), which is a measure of general anxiety. (3) AFS: The AFS is another test of anxiety with different subscales (among others) for Test Anxiety (TA) and Manifest Anxiety (MA) (Wieczerkowski, Nickel, Janowski, Fittkau, & Rauer, 1974). (4)

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A lie-scale (Aschersleben, 1970) for detecting tendencies to give socially desirable answers in questionnaires. It is a derivate of the Crowne–Marlowe Social Desirability Scale (Crowne & Marlowe, 1960). (5) A measure of general intelligence (Hamburg-West Yorkshire). (6) A simple performance measure (grades). Since all measures were taken in schools, it was decided to use composite course marks in German, mathematics, and elementary science as a performance measure. Marks ranged from 1 (very good) to 6 (very bad). The fourth-grade GPA was used as the single criterion measure. Procedure. The TAT and the AMG were completed in the classroom, separated by a 4week interval and using different experimenters. The CAT, lie-scale, AFS, and the intelligence test were all applied 2 weeks after the AMG. The TAT pictures were projected via slides and participants recorded their stories on a prearranged sheet of paper. They were led to believe that the TAT was a test of creative imagination. They were asked to make up a fanciful story and to include responses to the following questions: (1) What is happening? Who are the persons? (2) What has led up to this situation? (3) What is being thought? What do the persons want? (4) How will the story go on? The stories were scored according to the Heckhausen key (see Heckhausen et al., 1985, pp. 20). Interscorer reliabilities were sufficiently high: HS (.88) and FF (.97).

Results Usually respondent measures of the achievement motive and TAT measures do not correlate significantly with one another (Halisch, 1986; Fineman, 1977; McClelland et al., 1989; Spangler, 1992). As can be seen in Table 4, there was strong support for convergent validity for the hope-ofsuccess motive, however, when the AMG/TAT correlations were examined. The correlation reported here (.34) testifies to a substantial overlap between TAT and AMG-HS measures, which might be due to the fact that the AMG statements were derived from the TAT scoring key. Furthermore, this correlation is remarkable because the two test-taking sessions were separated by a 4-week interval. There was no convergent validity, however, for the AMGFF scales and the TAT but a substantial overlap between AMG FF and different components of anxiety as measured by questionnaires. The two FF subscales of the AMG do not differ significantly as far as the magnitude of correlations with the anxiety scales are concerned. They do differ, however, in regard to the correlations with the GPA. The pattern of correlations reported here is consistent with the contention that the FF1 scale essentially samples aspects of a negative self-evaluation, which corresponds to the worry component of anxiety and is thought of as a debilitating motivational component, whereas the FF2 scale focuses on emotional-autonomic aspects of anxiety, which seem to have facilitating properties (Endler et al., 1991; Wine, 1971). Quite unexpectedly AMG-HS was also positively correlated with Test Anxiety and Manifest Anxiety. This correlation is not fully understood and needs further clarification including other anxiety measures. Discriminant validity was examined by inspecting the magnitude of correlations between AMG scales and measures of intelligence and the lie tendency. As can be seen from Table 4, there were only two moderately signifi-

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cant correlations, which suggest that the influences of intelligence and the lie tendency on the AMG scales are negligible. There was only one minor effect of the tendency to give socially desirable answers on FF1 scores. All the other scores of the AMG are virtually uninfluenced by this tendency. As far as the AMG-FF2/intelligence correlation is concerned, it might be argued that it is not substantially smaller than the other correlations that contribute to convergent validity. It should be remembered, however, that measures of intelligence are sometimes determined to some minor degree by motivational factors by emphasizing, as Atkinson (1974, p. 405) put it, ‘‘. . . motivational determinants of intelligence. . . .’’ Critics may argue that these data are not very convincing because the convergent validity coefficients are not that high and the discriminant validity coefficients are not that low. I attribute the latter to the discriminant validity indices. Future research should incorporate measures and variables that are clearly different from achievement-motivation variables. CONSTRUCT VALIDITY: PERSISTENCE BEHAVIOR When Atkinson (1964) developed his famous risk-taking model of achievement motivation, he was largely inspired by the ideas of Lewin and Tolman. Those ideas led him to construe motivation as a process in which dispositional and situational factors interact. Furthermore, two separate motivational tendencies were taken into account. The first is the tendency to achieve success (T s), the other is the tendency to avoid failure (T f ), the latter being an inhibitory force. So the resulting motivational tendency can be defined as RT ⫽ T s ⫺ T f. Each tendency has three constituents: the motives (hope of success and fear of failure), incentives for success (I s) and failure (I f ), and the subjective probabilities of success (P s) and failure (P f ). Since subjective probabilities of success and failure, which are mutually exclusive events, must add up to 1.0, and the incentives of success and failure are also dependent on subjective probability of success, this latter variable is the only variable besides HS and FF that must be controlled when the model is tested (Heckhausen et al., 1985, p. 61). Both motivational tendencies, T s and T f, reach their peak at moderately difficult tasks (when P s ⬇ .50) and decrease evenly toward both ends of the continuum of difficulty. Depending on whether HS or FF is dominant, the approach tendency (T s) or the avoidance tendency (T f ) will dominate. So, imagine working at a task you think is very easy (say P s ⫽ .90) but which in fact is insoluble. Failure is inevitable and should reduce P s successively. After a few trials you might feel that P s is approximately .50. At this point both motivational tendencies are maximally aroused (see above). If your HS motive is dominant (HS ⬎ FF) you should be maximally motivated and hence persist very long. If, on the other hand, your FF motive is dominant

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(HS ⬍ FF) you should be maximally—avoidance—motivated and give up as soon as possible. The reverse predictions can be made for a task whose initial probability of success is very low (say P s ⬇ .05). The experimental study described below was designed to test these hypotheses derived from Atkinson’s (1964) risk-taking model. The hypotheses were originally tested by Feather (1961, 1963). He induced failure on repeated tasks of the same kind in order to find out when subjects would turn to an alternative task. If the initial levels of P s were known, the persistence of success (HS ⬎ FF) and failure-motivated individuals (HS ⬍ FF) could be predicted making the following assumptions: Repeated induction of failure gradually reduces P s. In this way an initially high P s will approach an intermediate level (P s ⬇ .50), while an initially low P s will grow less and less. Because the differences in resultant motivation between HS and FF individuals were maximized at intermediate task difficulty (see above), moving toward this region or away from it should have divergent effects on the two motive groups. The following hypotheses can be deduced: (1) HS individuals will persist longer when the initial P s for the task at hand is high than when it is low; (2) FF individuals will persist longer when the initial P s for the task at hand is low than when it is high; and (3) HS individuals as compared to FF individuals will show more persistence when the initial P s for the task at hand is high. The study to be reported here was tailored according to the original Feather studies (1961, 1963), which were generally supportive for Atkinson’s theory and used TAT-motive scores. A more recent study, testing hypotheses from Atkinson’s risk-taking model, was conducted by Blankenship (1992). She did not find the predicted Achievement motive ⫻ Task difficulty interaction; instead the results show that HS individuals persisted longer than FF individuals across all conditions. The failure to find any significant effect of task difficulty was attributed to the fact that the different levels of task difficulty were not salient enough or not fully appreciated by participants because they were only asked to take seriously the label for the difficulty level and the corresponding P s information given by the experimenter. Method Design overview. This study used a 2 ⫻ 2 factorial design with the between-subject independent variables of task difficulty (P s) and motive disposition.1 The dependent variable was time spent working on the experimental task. A serious manipulation check was undertaken to ascertain the effectiveness of the task-difficulty induction (see below).

1

According to Atkinson’s model a resultant measure (HS minus FF) must be taken into account (see also McClelland, 1992).

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Participants. A total of 132 fifth- and sixth-graders (boys only) participated in the study. Materials. Participants were shown a sheet of paper which depicted an irregular arrangement of different circles, triangles, and rectangles. They were told that a circle counted as 1 point, a triangle 5 points, and a rectangle 10 points, and that they should add up the number of points in a 30-s interval. This time limit was determined in pretests which showed that the task was really insoluble within this limit. Each task was printed on a separate sheet and contained 24 symbols. A complete set of 16 different versions of this task was used. The experimenter always induced failure by saying that the answer given by the participants was wrong. Half of the participants were told that the task was difficult, the other half was told that the task was easy (see below). There was a second arrangement of symbols in front of the participants, purportedly of intermediate difficulty, and participants were told that they could switch to this second task whenever they wanted to. Our principal dependent measure was time spent (minutes) on the original task. Subjective probability of success (task difficulty). The stringency of a test of Atkinson’s risk-taking model by means of the persistence paradigm hinges on the problem of how to induce, control, and validly assess subjective probability of success (Blankenship, 1992, p. 59; Heckhausen, 1991, p. 226; Heckhausen et al., 1985, p. 62). Making use of the insights of the Blankenship study, great pains were taken to validly induce subjective probabilities of success. One can assume that subjective probabilities of success will result from objective characteristics of the task, such as the different number of symbols in the experimental task. In order to create diverging subjective levels of probability, based on face validity, participants were shown alternative tasks consisting of 11 or 41 symbols. So in the difficult condition participants were shown an alternative task consisting of 11 symbols because compared to an 11-symbol task the 24-symbol task is apparently difficult. In the easy condition participants were shown an alternative task consisting of 41 symbols, which makes the 24-symbol task an apparently easy one. Furthermore, participants were informed of social norms. They were told that 20% (difficult group) or 80% (easy group) of a comparison group had solved the task. But persons often assign little validity to social-norm information unless it is validated with respect to their own competence. So a third step was included in order to determine subjective probability of success: Participants worked on a preliminary task and the experimenter ‘‘graded’’ the test performance with the help of a reference norms table and gave oral feedback to each participant, telling that his personal probability of success was 20% (individual feedback ranged from 17 to 23%) or 80% (individual feedback ranged from 77 to 83%). Thus the procedure was a combination of various methods to induce subjective probability of success that have been used in the past more or less successfully (see Heckhausen et al., 1985, p. 62).

Results A manipulation check revealed that a total of 69 participants reported subjective probabilities of success of approximately .20 and .80%. They were retained in the present investigation. There were no significant motive score differences between these 69 participants and the rest of the group. As for the achievement motive, a measure of Resultant Motivation (RM ⫽ HS minus FF1) was used (see above) and split at the median to yield success- (HS ⬎ FF) and failure-motivated groups (HS ⬍ FF). Figure 2 shows means for time spent (minutes) in the different motive- and task-difficulty groups. A 2 ⫻ 2 ANOVA on the time-spent measure was conducted with the two between-subjects factors motive disposition and task difficulty. As expected, the analysis of variance of time spent yielded a significant interac-

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FIG. 2. Mean time spent (persistence in minutes) at easy and difficult tasks for HS and FF individuals, respectively.

tion effect, F(1/65) ⫽ 7.07, p ⫽ .01. Furthermore, level of task difficulty yielded a significant main effect, F(1/65) ⫽ 5.32, p ⬍ .05. To clarify the nature of the interaction, a series of planned contrasts was conducted, based on the within-cell error term from the overall ANOVA (Winer, 1971, p. 215). As predicted, HS individuals persisted longer at an easy task than at a difficult task, t(30) ⫽ 3.82, p ⬍ .001. In fact they spent more than twice the time working at an easy task than working at a difficult task. Comparing the two motive groups in the easy task condition, HS individuals persisted longer than their FF counterparts, t(41) ⫽ 2.02, p ⬍ .05. All other comparisons were nonsignificant. These results support hypotheses 1 and 3. Hypothesis 2 was not supported, however, because FF individuals did not reveal any substantial behavioral difference working at easy or difficult tasks. GENERAL DISCUSSION In the studies reported here a new technique for measuring the achievement motive in children and juveniles was introduced. The guiding idea for its development was to combine the advantages of the TAT and questionnaire

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methods of measuring the achievement motive and to place it within an interactional conception of personality functioning. This means to measure the motive in concrete situational contexts. The result was the Achievement Motive Grid (AMG), which consists of 18 depicted achievement situations and 18 achievement-related statements which cover important emotional, cognitive, and behavioral correlates of the motive to achieve success (Hope of Success, HS) and the motive to avoid failure (Fear of Failure, FF), as indicated by the corresponding categories of the TAT scoring key developed by Heckhausen (1963, 1991). Results of the initial studies examining the goodness-of-scale development suggest that the AMG comprises three factors reflecting hope-of-success and fear-of-failure components. Hope of success proved to be a relatively unitary construct, whereas the fear-of-failure construct was subdivided into two factors. The first of them (FF1) was described in more cognitive terms as a negative self-evaluation of efficiency and the other one (FF2) as an emotional fear-of-failure component. Conceptually, these two FF scales mirror a similar differentiation found with the TAT and in various forms of anxiety scales, namely the cognitive-worry and autonomic-emotional distinction (Covington & Roberts, 1994; Kuhl, 1978; Wine, 1971; Endler et al., 1991). This interpretation of the two fear-of-failure factors is further corroborated by diverging correlations with graded performance and with causal attributions for success and failure (Schmalt, 1982). Although not discussed in detail here some further evidence from a Dutch version (Snel, Bos, Uylings, & Ras, 1978) and a Portuguese version (Winterstein, 1991) of the AMG may be added that exactly replicate the three-factor structure. Schmalt and Schab (1984) and Halisch (1982) examined factorial invariance across different samples, different school types and classes (third- to eighth-graders), different forms of instructions, and across first and second test applications. They found the factorial structure to be highly invariant across all samples. This conclusion is further corroborated by the results originating from confirmatory factor analyses in which it was shown that a correlated three-factor model, comprising the HS, FF1, and FF2 factors, is the best model to describe the data. So the factorial structure of the AMG is highly stable and replicable, even across different methods and cultures. Reliability data show that, as expected, the documented reliability of the AMG was far higher than that reported for the TAT and even for most questionnaire measures of the achievement motive (Fineman, 1977; Klinger, 1966; Heckhausen et al., 1985; Spangler, 1992). This holds for test–retest as well as for item–remainder coefficients. So the research presented here documents the high psychometric standard of the AMG. Turning to validity aspects of the AMG, some correlational and experimental data were presented in respect of two classic behavioral domains of the achievement motive: performance and persistence. The most interesting

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result from the convergent–discriminant–validity section seems to be that there is substantial overlap between AMG and TAT concerning the success motive and between the AMG and anxiety questionnaires concerning the failure motive. In light of the discussion initiated by McClelland pertaining to the implicit versus explicit nature of motives measured by TAT and by questionnaires the data seem to suggest that the AMG scores can reflect implicit as well as explicit aspects of the achievement motive. If it is convenient to measure the success motive by means of the TAT and to measure the failure motive by self-report measures, as reflected in the U.S. tradition (Atkinson, 1982, 1987; Atkinson & Feather, 1966; Elliot & Harackiewicz, 1996; McClelland, 1985), the AMG can best meet these standards. The construct-validity part of the present study yields results that support the hypotheses derived from Atkinson’s risk-taking model (Atkinson, 1964). In short, HS individuals perform better in school and persist longer in the face of repeated failure when working on tasks that are purported to be very easy. In the light of the distinction between implicit and explicit motives, these data strongly support the contention that the achievement motive as measured by the AMG does reflect implicit aspects of the motive. McClelland contended that implicit motives get aroused by task-inherent incentives, whereas explicit motives are activated by explicit external incentives such as demands, rewards, and so on. The nature of behavior predicted by implicit motives is more operant, whereas the nature of behavior predicted by explicit motives is more respondent. The chosen dependent behavior in the studies reported here, performance over a 1-year interval and time spent on an insoluble task, are clearly operant in nature, they are neither demanded nor prompted or expected by the experimenter. So the contention derived from the convergent–validity study that the motive scores obtained with the AMG reflect an implicit motive is further corroborated. On a last note, many psychologists are interested in identifying potentially important variables for individual differences that influence learning in instructional settings. The achievement motive certainly ranks among them. From the very beginnings of research on achievement motivation, there has been a marked interest modifying unfavorable motive patterns (e.g., high fear of failure) in order to promote students’ learning behavior through enhanced achievement motivation (Ames, 1992; Dweck & Elliott, 1983; Harter, 1992). The details, problems, and enigmas of different motive-modification programs have been discussed elsewhere (Rheinberg & Krug, 1993) and are not considered in detail here. Suffice it to say that the research summarized here is mainly based on Heckhausen’s (1977, 1991) conceptualization of the achievement motive as a self-evaluation system and the effects of these motive modification programs were ascertained with the AMG. The main idea of Heckhausen’s theory stems from attribution theory and assumes divergent attributional propensities for HS and FF individuals; FF individuals are especially prone to have unrealistic performance standards and a pattern of attri-

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butions for success and failure that leads to unfavorable self-evaluation and a self-defeating self-assessment of ability, both of which are immune to novel and unexpected experiences and therefore maintain themselves. Krug and Hanel (1976) conducted the first study involving failure motivated, low-achieving fourth-graders in which pupils were trained to set realistic goals for themselves and to develop more favorable attributional patterns for success and failure. The results reveal that compared to a control group and an expectation control group, the training group showed—besides a modified standard-setting and attributional pattern—a clear modification of motives, namely a reduction in fear of failure and an increase in hope of success. A follow-up study by Krug, Peters, and Quinkert (1977) replicated these findings and further demonstrated that the effects could be maintained over a 1-year interval. Other studies transferred the training procedure to sports in the schools. They trained pupils to set realistic goals for a high jump. A study by Hecker, Kleine, Wessling-Lu¨nnemann, & Beier (1979) showed a significant increase in success motivation as compared to a control group. These effects were accompanied by a simultaneous change in the attributional patterns, thus contributing to the validity of the results. Winterstein (1991) reported similar effects from a study conducted in Brazil. In this study the expected improvement showed itself in a decline in the fearof-failure motive (FF1). As mentioned above, it is not the purpose of this study to become involved in the intricacies of motive-modification programs, but since all the reported effects were confirmed by the AMG, this further corroborates its validity. These studies underline the usefulness of the AMG to measure the achievement motive in instructional contexts. Taken together, the data accumulated here demonstrate that the AMG is a psychometrically sound measure of the achievement motive, combining economic applicability with a high standard of reliability and validity. Since item contents in the AMG and the targeted behaviors were selected strictly on theoretical grounds, the results demonstrate that test construction as well as test validation can benefit from theory-based procedures (Jackson & Paunonen, 1985). REFERENCES Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52, 317–332. Ames, C. (1992). Achievement goals, motivational climate, and motivational processes. In G. C. Robert (Ed.), Motivation in sport and exercise (pp. 161–176). Champaign, IL: Human Kinetics Books. Arbuckle, J. L. (1997). Amos Users’ Guide. Version 3.6. Chicago: SmallWaters Corporation. Aschersleben, K. (1970). Entwicklung eines Lu¨gen-Scores zur Messung von Simulationstendenzen. Zeitschrift fu¨r Entwicklungspsychologie und Pa¨dagogische Psychologie, 2, 39– 47. Atkinson, J. W. (1957). Motivational determinants of risk-taking behavior. Psychological Review, 64, 359–372.

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