Psychological Assessment 2006, Vol. 18, No. 2, 215–219

Copyright 2006 by the American Psychological Association 1040-3590/06/$12.00 DOI: 10.1037/1040-3590.18.2.215

Perceived Likelihood as a Measure of Optimism and Pessimism: Support for the Future Events Scale Aaron L. Wichman

Darcy A. Reich

Ohio State University

Texas Tech University

Gifford Weary Ohio State University The Future Events Scale (FES; S. M. Andersen, 1990) is an expectancy-based measure of optimism and pessimism, grounded in cognitive theories of depression, with implications for clinical practice. Although ample research has documented the utility of the FES in predicting important cognitive and behavioral outcomes, psychometric data on the scale are lacking. The current article presents multisample analyses to show that the FES has clear factor structure, good reliability, and a theoretically meaningful nomological network. The FES is shown to be distinct from the best known measure of optimism and pessimism, the Life Orientation Test (M. F. Scheier & C. S. Carver, 1985). Applications are discussed. Keywords: optimism and pessimism, Life Orientation Test, Future Events Scale, FES, LOT

optimism and pessimism (e.g., Andersen, 1990; Andersen & Schwartz, 1992). We focus here on the use of the FES as a measure of optimism and pessimism. The FES provides an aggregate measure of expectancies, based on the perceived likelihood of specific positive and negative future events. As such, it does not rely on participants’ self-assessment of their own general beliefs. The FES is composed of 26 items describing events that differ in valence. Participants indicate on an 11-point scale (⫺5 ⫽ extremely unlikely, ⫹5 ⫽ extremely likely) the subjective likelihood of each event happening to them at some point in their lives. Participants’ summed likelihood rating for the 13 positive events typically is subtracted from their summed likelihood rating for the 13 negative events. Higher composite FES scores thus indicate more pessimistic outcome expectancies. Previous research (e.g., Andersen, 1990; Andersen & Schwartz, 1992) has demonstrated that expectancies assessed using the FES are associated with depression as assessed using the Beck Depression Inventory (BDI; Beck, 1967). These expectancies have been conceptualized as comprising an organized knowledge structure that can become automatized. Moreover, Andersen, Spielman, and Bargh (1992) used a dual-task, concurrent memory load paradigm to provide evidence that the pessimistic event-predictions of individuals with depression are activated and applied relatively automatically, for both the self and others. These results suggest that FES-assessed expectancies may systematically bias perception and interpretation processes, potentially contributing to the maintenance of inaccurate worldviews and negative perceptions of relevant others. Such expectancies may have their greatest impact when situational stressors, intrusive thoughts, or fatigue render automatic thoughts inaccessible to critical examination. Several studies (Reich & Weary, 1998; Weary & Reich, 2001; Weary, Reich, & Tobin, 2001; Weary, Tobin, & Reich, 2001) have shown that FES-assessed expectancies bias perceivers’ judgments of others in a relatively automatic, assimilative fashion when perceivers’ cognitive resources are de-

Expectancies are implicated in a range of psychological disturbances, and attempts to change them constitute a common therapeutic strategy. Generalized expectancies function as schemas that can either facilitate or inhibit healthy psychological functioning, and several instruments have been developed to assess them. In addition to the well-known Life Orientation Test (LOT; Scheier & Carver, 1985), other measures of general expectancies include Dember, Martin, Hummer, Howe, and Melton’s (1989) Optimism– Pessimism Instrument and Snyder et al.’s (1991) Hope Scale. This article focuses on Andersen’s (1990) Anticipated Life Events Survey, also known as the Future Events Scale (FES). The FES was developed within the framework of the hopelessness theory of depression (Abramson, Metalsky, & Alloy, 1989). This theory assumes that the experience of negative life events causes vulnerable individuals to become hopeless. This hopelessness then serves as a proximal cause of depression. Andersen (1990) developed the FES to measure the two distinct constructs of hopelessness and pessimism and to examine their association with depression. The use of the FES to measure hopelessness is conceptually and empirically distinct from its use as a measure of

Aaron L. Wichman and Gifford Weary, Department of Psychology, Ohio State University; Darcy A. Reich, Department of Psychology, Texas Tech University. An extended report of this study is available from Aaron L. Wichman. This research was supported by National Institute of Mental Health Training Grant T32-MH19728 to Aaron L. Wichman and National Science Foundation Grant SBR-9631858 to Gifford Weary. We thank Robert Arkin, Anthony Hermann, Geoffrey Leonardelli, and Christian Wheeler for assistance in obtaining the data sets we report in the Convergent and Discriminant Validity section of this article. We also thank Stephanie Tobin for her comments on previous versions of this article. Correspondence concerning this article should be addressed to Aaron L. Wichman, Department of Psychology, Ohio State University, 1885 Neil Avenue, Columbus, OH 43210-1222. E-mail: [email protected] 215

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pleted. Of interest, under certain conditions, evaluative judgments may be contrasted from FES-assessed expectancies. For example, when perceivers’ attention is drawn to their expectancies under cognitive load conditions, they make judgments that reflect the opposite of their chronic expectancies (Weary, Tobin, & Reich, 2001). These findings have promising implications for the effectiveness of cognitive therapies that draw clients’ attention to their automatic negative expectancies and thoughts. Finally, FES-assessed expectancies appear to have behavioral and interpersonal consequences. During a social interaction, perceivers asked FES-biased questions and elicited expectancyconsistent behavior from interaction partners (Reich, 2004).

comparative fit index ⫽ .895, its two positive factors lacked a clear conceptual difference. Further, in both our sample of N ⫽ 2,217 and the other validation samples, the two positive factors in the three-factor model were highly correlated (range of rs ⫽ .86 to .89). Such high levels of multicollinearity often lead to unstable regression weights (Cohen, Cohen, West, & Aiken, 2003). For these reasons and because, on theoretical grounds, a two-factor structure was expected, we retained the more parsimonious and interpretable two-factor model. This model had FES Optimism and Pessimism factors composed of the scale’s remaining positive and negative items, respectively.

FES Descriptive Statistics and Reliability Overview of the Current Research In this article, we examine the psychometric properties of the FES, including its factor structure, descriptive statistics, and reliability. We then explore the scale’s convergent validity and the differential predictive validity of the FES and the LOT. As has previous research using the FES, we used college student samples. These are designated by term of data collection: autumn (AU), winter (WI), and spring (SP). The two digits at the end of the sample designation indicate the year of data collection.

Method and Results Previous research (e.g., Andersen, 1990) has used separate positive and negative FES subscales, as well as total scale scores computed by collapsing across the subscales. These practices suggest potentially different underlying scale structures. Accordingly, our first goal was to conduct factor analyses to determine the scale’s latent structure and to identify items not substantially contributing to the scale’s latent construct(s). Theoretically, we expected a simple two-factor structure, with one factor reflecting optimism and the other reflecting pessimism. Exploratory analyses (N ⫽ 1,142) with Maximum Wisehart Likelihood estimation and Crawford–Ferguson oblique rotation suggested a two- or three-factor model, and revealed three items with low communalities across different models. Further analyses supported the removal of these items. All subsequent analyses were performed using this reduced set of FES items.1 We used confirmatory factor analyses with Maximum Wisehart Likelihood estimation in a separate sample (AU99; N ⫽ 2,217) to decide which model provided the best theoretical and empirical fit to the data. We examined one-, two-, and three-factor solutions, comparing their model fit and interpretability. A one-factor solution yielded ␹2(230, N ⫽ 2,217) ⫽ 3,986; root-mean-square error of the approximation (RMSEA) ⫽ .090; and comparative fit index ⫽ .78, suggesting suboptimal model fit. A two-factor solution fared better, with ␹2(229, N ⫽ 2,217) ⫽ 2,376; RMSEA ⫽ .065; and comparative fit index ⫽ .875. In four validation samples (Ns ⫽ 700 –2,217), the two-factor solution specified positive items as loading (.49 –.87) on an optimism factor, specified negative items as loading (.27–.67) on a pessimism factor, and estimated the correlation between factors to range from ⫺.57 to ⫺.65. The three-factor model specified two positive factors and one negative factor and estimated correlations between all factors. Although this model fit slightly better than the twofactor model, ␹2(227, N ⫽ 2,217) ⫽ 2,027; RMSEA ⫽ .060; and

In our AU99 sample of N ⫽ 2,217, descriptive statistics for groups based on gender and ethnicity (Caucasian, n ⫽ 1,736; African American, n ⫽ 213; and Asian American, n ⫽ 116) revealed similar means. However, individuals with depression compared with individuals without depression reported that positive events were significantly less likely (Cohen’s d ⫽ 1.1) and negative events were significantly more likely (d ⫽ 1.0) to occur. Reliability was tested in four samples, with test–retest intervals ranging from 6 to 8 weeks. Test–retest reliability for each FES subscale ranged from .61 to .75 (M ⫽ .68). We computed full sample descriptive statistics for the FES for the four large samples we factor analyzed (Ns ⫽ 700 –2,223). Cronbach’s alphas for FES Optimism (.89 –.90) and FES Pessimism (.79 –.81) were consistent across samples. Means ranged from 24.0 to 26.0 (SDs ⫽ 16.4 –18.7) for FES Optimism and from ⫺9.5 to ⫺16.1 (SDs ⫽ 17.3–19.9) for FES Pessimism. For FES Optimism, higher values indicate greater optimism; for FES Pessimism, higher values indicate greater pessimism. Across samples, correlations between FES Optimism and Pessimism ranged from ⫺.47 to ⫺.52. Given our factor analytic results and the fact that the subscales have only 22% to 27% of their variance in common, the subscales were examined separately in the analyses that follow.

Convergent and Discriminant Validity We expected that the use of separate FES Optimism and Pessimism would yield insights unobtainable from the original, aggregated FES. To investigate the nomological network of the scales, we examined their relationships with measures of self-worth and self-competencies. Self-worth is known to be associated with expectancies (e.g., learned helplessness theory; Seligman, 1975). Because self-competencies should help people to achieve desired and avoid undesired outcomes, expectancies also should be related to self-competence beliefs. We expected that FES Optimism would be associated with greater feelings of self-worth and higher perceived competence and that FES Pessimism would show the reverse relationships. The Rosenberg Self-Esteem Scale (RSE; Rosenberg, 1965) and the BDI (Beck, 1967) were used to assess self-worth feelings, and the Self-Doubt scale (Oleson, Poehlmann, Yost, Lynch, & Arkin, 1

The three removed items (with original item numbers from Andersen, 1990) were Item 10, “To be institutionalized (e.g. prison or asylum) in the next 20 years”; Item 20, “To win the lottery”; and Item 21, “To retire at the age of 40 and do all the things I would like to do.”

BRIEF REPORTS

2000) and Causal Uncertainty Scale (Weary & Edwards, 1994) were used to measure self-competencies. Higher values on all scales indicate a higher level of the construct indexed by the scale. Table 1 presents zero-order and semipartial correlations. As predicted, greater self-worth feelings and self-competency were associated with higher FES Optimism and lower FES Pessimism. Demonstrating the insights obtainable from the separate scales approach, FES Optimism was consistently more strongly associated with self-worth and self-competency measures than was FES Pessimism. Analyses using Fisher’s z transformation of r showed that for all measures presented in Table 1, except the BDI, the zero-order FES Optimism correlation with criterion measures was significantly larger than its FES Pessimism counterpart. This asymmetry may stem from the attributional implications of success and failure. Although success is generally thought to implicate high ability (e.g., Reeder & Brewer, 1979), failure can be beyond an actor’s control and thus does not unequivocally implicate low ability. Therefore, although failure experiences could lead to negative expectancies, these expectancies would not necessarily implicate self-worth. Attributing outcomes internally implies that the self is seen as an effective causal agent. Given the stronger link of FES Optimism than Pessimism with self-worth-related measures, we wondered whether optimism might be associated with increased internal attributions in general, where individuals take responsibility (ascribe internal causality) for even negative outcomes. We used the

217

Attributional Style Questionnaire (ASQ; Dykema, Bergbower, Doctora, & Peterson, 1996) to test this idea. Participants are asked to rate the imagined causes of six positive and six negative hypothetical events on 7-point scales for the attributional dimensions of internality, stability, and globality. Higher scores indicate greater perceived internality, stability, or globality of causes. As expected, FES Optimism was more strongly related to internality ratings (for both positive and negative events) than was FES Pessimism. Such evidence of differential predictive validity adds support to the two-factor model, and it speaks against acquiescence bias as a cause for these results. On measures not tapping attributions to the self (e.g., ASQ stability, ASQ globality), the predictive advantage of FES Optimism disappeared (see Table 1). Such specificity in prediction would have been impossible with the aggregated FES. Although the greater specificity of the FES Optimism and Pessimism scales is their greatest strength, we also compared the predictive power of these scales with the original, aggregated FES. For 9 of the 10 constructs in Table 1, the separate scales accounted for at least marginally more variance than the aggregate scale (see Table 1 for these F values).

On the Relationship Between the FES and the LOT For comparison with the FES, we present data from Scheier and Carver’s (1985) LOT. Higher LOT scores indicate more optimism. Across two samples (AU99, N ⫽ 153; and SP00, N ⫽ 170),

Table 1 Relationships Between the Future Events Scale (FES) Subscales and Other Measures rzero-order FES Optimism

Measure

rsemipartial

FES Pessimism

FES Optimism

FES Pessimism

F for fit improvement vs. aggregate scale

.45* ⫺.31*

⫺.19* .25*

17.51* 11.59*

⫺.35* ⫺.30*

.15* .20*

8.60* 2.96†

Self-worth feelings a

Rosenberg Self-Esteem Scale WI99 Beck Depression Inventory AU99b

.55* ⫺.44*

⫺.42* .41*

Self-competency measures ⫺.43* ⫺.40*

a

Self-Doubt Scale WI99 Causal Uncertainty Scale WI99c

.35* .35*

Attributional Style Questionnaire d

Dimension and Event Valence AU96 Internality Positive Negative Stability Positive Negative Globality Positive Negative

.36* .14

⫺.26* .00

.32* .23*

⫺.06 .14

2.40 3.30†

.46* ⫺.17

⫺.32* .45*

.41* .15

⫺.07 .53*

4.59† 10.74*

.15 ⫺.21*

⫺.01 .52*

.24* .15

.14 .61*

3.42† 14.67*

Note. Semipartial correlations each control for the other FES subscale. Term of sample collection (WI ⫽ winter; AU ⫽ autumn; the two digits at the end of the sample designation indicate the year of data collection) and sample size are given for each measure. All Attributional Style Questionnaire components were measured in the same sample. Significant probability values in the far-right column indicate that the separate FES Optimism and FES Pessimism scales explain significantly more variance in the outcome than the aggregate FES scale. a N ⫽ 692. b N ⫽ 2,217. c N ⫽ 1,204. d N ⫽ 112. † p ⬍ .10. * p ⬍ .05.

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correlations between the LOT and the FES subscales (FES Positive, rs ⫽ .39 and .46; FES Negative, rs ⫽ ⫺.39 and ⫺.49) revealed that only 15% to 24% of the variance in these scales is shared, suggesting that these scales measure different constructs. Regressions using both the FES and the LOT as predictors of other constructs provide further evidence that the scales are distinct and account for unique variance in other constructs. In another sample, measures of the FES and LOT were combined with participants’ responses to a set of questionnaires containing individual-differences measures (listed in Table 2) administered 3 to 7 weeks earlier. A measure of perceived control (Edwards & Weary, 1998) also was examined, for which higher scores indicate greater perceived control. Hierarchical regressions were conducted to compare the predictive validity of the FES and the LOT for these constructs. We first added the FES to an equation already containing the LOT, then conducted separate analyses entering these predictors in the reverse order. Regardless of order of entry, R2 increased significantly when the second measure was added. The fact that both scales predict variance above and beyond the other underscores each scale’s uniqueness.

Discussion The FES has a clear, theoretically interpretable factor structure and good test–retest reliability. Its nomological network follows logically from its conceptualization as a measure of general positive and negative expectancies. It also has good discriminant validity. The FES uniquely predicted several constructs, even after accounting for variance explained by the conceptually similar LOT. Furthermore, the fact that FES Optimism and FES Pessimism differentially predicted other self-relevant individual differences indicated that the use of the separate scales can provide superior conceptual articulation and empirical precision when investigating predictive relationships.

Our data showed that positive expectancies were more strongly associated with constructs related to self-worth and self-competency than were negative expectancies. This is especially interesting, given that pessimism, not optimism, is often the more important predictor for outcomes such as mortality (Schulz, Bookwala, Knapp, Scheier, & Williamson, 1996), anxiety, perceived stress, and self-rated health (Robinson-Whelen, Kim, MacCallum, & Kiecolt-Glaser, 1997). This discrepancy suggests that researchers should continue to investigate differences between optimism and pessimism, perhaps by relating them to the Big Five personality factors. The FES’s discrete likelihood judgments for specific events provides an aggregate measure of general expectancies that does not seem to strongly rely on introspection or on conscious access to a generalized cognitive representation of past experience. The FES may, therefore, show a resistance to systematic measurement problems associated with biased recall or biased interpretation of previous behaviors. Furthermore, aggregate scores on the FES may provide an index of a relatively efficient knowledge structure about the future that may automatically guide interpretations, judgments, and behaviors (Reich & Weary, 1998; Weary, Tobin, & Reich, 2001). Cognitive approaches to psychopathology have repeatedly suggested that these sorts of automatic cognitions are central to both etiology and treatment. When clients seek treatment, they often are experiencing stressful life events and a great deal of anxiety and confusion. It may be difficult for clients to achieve an “objective” perspective that could help them to identify and begin to neutralize their maladaptive cognitions and expectancies. Yet, this often is the goal of therapy. The FES may give therapists a way to assess the nature and extremity of these relatively automatic schemas and to help clients become aware of these cognitions. We believe that the FES is well suited for continued and expanded use as a measure of relatively automatic expectancies.

Table 2 Summary of Hierarchical Regression Analyses Showing the Relative Predictive Power of the Life Orientation Test (LOT) and the Future Events Scale (FES) Step 1

Step 2

Sample

N

Construct

LOT Positive

LOT Negative

LOT Positive

LOT Negative

FES Optimism

FES Pessimism

⌬R2

SP00 SP00 SP00 SP00 WI01

110 110 113 114 165

BDI CUS Self-Doubt RSE PC

⫺.37* ⫺.34* ⫺.25* .33* .09

.22* .16 .35* ⫺.31* ⫺.29*

⫺.21* ⫺.20 ⫺.13 .20* .01

.16 .11 .23* ⫺.19 ⫺.26*

⫺.21* ⫺.24* ⫺.18 .23* .20*

.22* .12 .20* ⫺.20* ⫺.03

.097* .070* .073* .090* .033*

FES Optimism

FES Pessimism

FES Optimism

FES Pessimism

LOT Positive

LOT Negative

⫺.35* ⫺.36* ⫺.28* .35* .24*

.29* .18 .33* ⫺.33* ⫺.12

⫺.21* ⫺.24* ⫺.18 .23* .20*

.22 .12 .20* ⫺.20 ⫺.03*

⫺.21* ⫺.20 ⫺.13 .20* .01

.16 .11 .23* ⫺.19* ⫺.26

SP00 SP00 SP00 SP00 WI01

110 110 113 114 165

BDI CUS Self-Doubt RSE PC

.078* .056* .071* .078* .053*

Note. Table values are betas. BDI ⫽ Beck Depression Inventory; CUS ⫽ Causal Uncertainty Scale; RSE ⫽ Rosenberg Self-Esteem Scale; PC ⫽ Perceived Control Scale. Sample designations are given as follows: SP ⫽ spring term; WI ⫽ winter term; the two digits at the end of the sample designation indicate the year of data collection. * p ⬍ .05.

BRIEF REPORTS

References Abramson, L. Y., Metalsky, G. I., & Alloy, L. B. (1989). Hopelessness depression: A theory-based subtype of depression. Psychological Review, 96, 358 –372. Andersen, S. M. (1990). The inevitability of future suffering: The role of depressive predictive certainty in depression. Social Cognition, 8, 203– 228. Andersen, S. M., & Schwartz, A. H. (1992). Intolerance of ambiguity and depression: A cognitive vulnerability factor linked to hopelessness. Social Cognition, 10, 271–298. Andersen, S. M., Spielman, L. A., & Bargh, J. A. (1992). Future-event schemas and certainty about the future: Automaticity in depressives’ future-event predictions. Journal of Personality and Social Psychology, 63, 711–723. Beck, A. T. (1967). Depression: Clinical, experimental, and theoretical aspects. New York: Hoeber. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences. Mahwah, NJ: Erlbaum. Dember, W. N., Martin, S., Hummer, M. K., Howe, S., & Melton, R. (1989). The measurement of optimism and pessimism. Current Psychology: Research and Reviews, 8, 102–119. Dykema, J., Bergbower, K., Doctora, J. D., & Peterson, C. (1996). An Attributional Style Questionnaire for general use. Journal of Psychoeducational Assessment, 14(2), 100 –108. Edwards, J. A., & Weary, G. (1998). Antecedents of causal uncertainty and perceived control: A prospective study. European Journal of Personality, 12, 135–148. Oleson, K. C., Poehlmann, K. M., Yost, J. H., Lynch, M. E., & Arkin, R. M. (2000). Subjective overachievement: Individual differences in self-doubt and concern with performance. Journal of Personality, 68, 491–524. Reeder, G. D., & Brewer, M. B. (1979). A schematic model of dispositional attribution in interpersonal perception. Psychological Review, 86, 61–79. Reich, D. A. (2004). What you expect isn’t always what you get: The roles of extremity, optimism, and pessimism in the behavioral confirmation process. Journal of Experimental Social Psychology, 40, 199 –215.

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Reich, D. A., & Weary, G. (1998). Depressives’ future-event schemas and the social inference process. Journal of Personality and Social Psychology, 74, 1133–1145. Robinson-Whelen, S., Kim, C., MacCallum, R. C., & Kiecolt-Glaser, J. K. (1997). Distinguishing optimism from pessimism in older adults: Is it more important to be optimistic or not to be pessimistic? Journal of Personality and Social Psychology, 73, 1345–1353. Rosenberg, M. (1965). Society and the adolescent self-image. Princeton, NJ: Princeton University Press. Scheier, M. F., & Carver, C. S. (1985). Optimism, coping, and health: Assessment and implication of generalized outcome expectancies. Health Psychology, 4, 219 –247. Schulz, R., Bookwala, J., Knapp, J. E., Scheier, M., & Williamson, G. M. (1996). Pessimism, age, and cancer mortality. Psychology and Aging, 11, 304 –309. Seligman, M. E. P. (1975). Helplessness: On depression, development, and death. San Francisco: Freeman. Snyder, C. R., Harris, C., Anderson, J. R., Holleran, S. A., Irving, L. M., Sigmon, S. T., et al. (1991). The will and the ways: Development and validation of an individual-difference measure of hope. Journal of Personality and Social Psychology, 60, 570 –585. Weary, G., & Edwards, J. A. (1994). Individual differences in causal uncertainty. Journal of Personality and Social Psychology, 67, 308 –318. Weary, G., & Reich, D. A. (2001). Attributional effects of conflicting chronic and temporary outcome expectancies: A case of automatic comparison and contrast. Personality & Social Psychology Bulletin, 27, 562–574. Weary, G., Reich, D. A., & Tobin, S. (2001). The role of contextual constraints and chronic expectancies on behavior categorizations and dispositional inferences. Personality & Social Psychology Bulletin, 27, 62–75. Weary, G., Tobin, S., & Reich, D. A. (2001). Chronic and temporary distinct expectancies as comparison standards: Automatic contrast in dispositional judgments. Journal of Personality and Social Psychology, 80, 365–380.

Received November 30, 2004 Revision received September 12, 2005 Accepted November 8, 2005 䡲

Perceived Likelihood as a Measure of Optimism and ...

Support for the Future Events Scale. Aaron L. Wichman ... Texas Tech University ..... PC .09 .29* .01 .26* .20* .03 .033*. FES. Optimism. FES. Pessimism. FES.

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