Abstract
Affective characteristics, such as attitudes, self-efficacy, and values are impossible to directly observe directly in other humans; they are latent constructs. Latent constructs are variables that we cannot observe directly; instead, we infer their existence through observed variables. This chapter introduces the theoretical concept of the latent variable and the important role it plays in understanding affective characteristics. In addition, we describe the operationalization of a latent construct, the process where instrument developers make both substantive and methodological decisions about the treatment of the observed variables that they are using to model the affective characteristic. This chapter aims to aid the instrument developer in the question or item construction process by highlighting the numerous theoretical and empirical implications of construct definition, measurement, and scaling choices available. This chapter provides both historical perspectives and a review of recent research within the areas of scaling, item construction, and response scale construction.
The parameter is what we aim to estimate; the corresponding statistic represents our current best estimate of it. Just so, the trait is what we aim to understand, and the corresponding construct represents our current best understanding of it.
Jane Loevinger 1957, p. 642
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Notes
- 1.
- 2.
Edwards and Bagozzi (2000, p. 156) stress that a measure refers not to the instrument used to gather data or to the act of collecting data, but to the score generated by these procedures.
- 3.
Thurstone also developed a technique that used paired comparisons. After the set of items had been scaled by the judges, items were paired with other items with similar scale values; and sets of paired comparisons were developed. In some cases, each item was paired with all other items from other scales on the instrument, and respondents were asked to select the item from the pair that best described the target object. Thus, readers should be aware that some references to Thurstone scaling are actually references to Thurstone’s method of paired comparisons.
- 4.
Up to this point, scale has been used to represent a cluster of items on a particular instrument. For the Semantic Differential technique, Osgood uses the term scale to represent a single item.
- 5.
Some researchers do include a few scales from the potency or activity dimensions to see where these scales load in a factor analysis of the total set of scales. In this situation, the potency and activity scales function as marker scales to facilitate interpretation of the main factor structure.
- 6.
Barnette (1999) found that a 5% pattern of nonattention responses can have strong effects on coefficient alpha (usually in the positive direction). This can lead instrument designers to conclude that the data are more internally consistency than they actually are.
- 7.
This advice is not necessarily consistent when considering items across instruments. Work by Chang (1997), for example, suggests that as long as the number of scale points used in the instrument is consistent, changing the labeling of the anchors in the scale from say 1 = disagree, 2 = somewhat disagree, 3 = somewhat agree, 4 = agree to 1 = strongly disagree, 2 = disagree, 3 = agree, 4 = strongly agree, does not add to the observed variance. One potential implication is that instrument developers need not be “overly concerned” with the practice of using different labels to anchor the Likert response scale for items in different instruments. Still, pilot studies of different formats are always good insurance during the process of instrument development.
- 8.
It should also be noted that extremely long or complex item stems, which we discussed earlier in the chapter, can overburden the cognitive optimizing processes of respondents, causing them to engage in satisfying behavior.
- 9.
Initially, the validity coefficients seem low, but each needs to be considered in light of the alpha reliabilities for the respective scales (in parentheses on the main diagonal). The low number of items used for both the normative and ipsative scales appears to result from low reliability levels, except for the normative GRD scale. The maximum validity coefficient is the square root of the product of the reliabilities of the two scales. For example, while the correlation between IFAV and NFAV was 0.41, the maximum correlation possible was \( \sqrt {(0.45)(0.45)} \) or 0.45. The maximum correlation possible between the IGRD and NGRD scales is approximately \( \sqrt {(0.75)(0.55)} \) or 0.64 and the correlation reported is only 0.36. For these scales, the normative and ipsative measures using the same items are not highly related. The diagonal validity values are higher than the row and column counterparts in the dashed-line triangles (MTMM). It is difficult to interpret the values in the dashed triangles, since they partially represent the ipsative scales which reflect both the occupational-values content and the ipsative-scale properties.
- 10.
It should be noted that some recent research suggests there is little or no effect of item ordering on internal consistency of the data. A study by Sparfeldt et al. (2006) found similar factorial structures for groups of high school students presented with items in a blocking order and in a traditional randomized order.
- 11.
The response-window evaluative priming instrument participants respond to words that have a negative or positive evaluative meaning by pressing one key for good words and another key for bad words. Immediately preceding each word, a white or black face appeared for 200 ms and participants were required to respond within 200–600 ms (Draine and Greenwald 1998).
- 12.
The response-window IAT is identical to the IAT except that it requires participants to respond within 225–675 ms of the stimulus presentation.
- 13.
Cunningham et al. (2001) report that estimates of Cronbach’s alpha indicated that more than 30% of the observed variance in the measures was due to random error.
- 14.
Cunningham et al. (2001) also note that “although multiple measures of implicit and explicit attitudes are robustly correlated, the two kinds of attitude measures tap unique sources of variances (Cunningham et al. 2001, p. 170); a single-factor [confirmatory factor analysis] solution does not fit the data”.
- 15.
A recent meta-analysis of IAT studies examining six criterion categories (interpersonal behavior, person perception, policy preferences, microbehaviors, reaction times, and brain activity) for two versions of the IAT (stereotype and attitude IATs), three strategies for measuring explicit bias (feeling thermometers, multi-item explicit measures such as the Modern Racism Scale, and ad hoc measures of intergroup attitudes and stereotypes), and four criterion-scoring methods (computed majority-minority difference scores, relative majority-minority ratings, minority-only ratings and majority-only ratings) suggested that IATs were poor predictors of every criterion category. The only exception to these finding were in brain activity. Ultimately, the researchers found that the IATs performed no better than simple explicit measures for these same criteria (Oswald, F. L., Mitchell, G., Blanton, H., Jaccard, J., & Tetlock, P. E. (in press). Predicting ethnic and racial discrimination: A meta-analysis of IAT criterion studies. Journal of Personality and Social Psychology).
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McCoach, D.B., Gable, R.K., Madura, J.P. (2013). Defining, Measuring, and Scaling Affective Constructs. In: Instrument Development in the Affective Domain. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7135-6_2
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