O ver the years, researchers have been concerned about the possibility that selfreport

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1 A Psychometric Investigation of the Marlowe Crowne Social Desirability Scale Using Rasch Measurement Hyunsoo Seol The author used Rasch measurement to examine the reliability and validity of 382 Korean university students scores on the Marlowe Crowne Social Desirability Scale (MCSDS; D. P. Crowne & D. Marlowe, 1960). Results revealed that item-fit statistics and principal component analysis with standardized residuals provide evidence of MCSDS s unidimensionality. Six (of 33) items displayed differential item functioning for gender. v O ver the years, researchers have been concerned about the possibility that selfreport instruments could be contaminated by respondents answering questions in ways that influence whether they are perceived favorably or unfavorably. Socially desirable responding in self-reports on sensitive behaviors such as delinquency, violence, and drug abuse interferes with accurate inferences of self-report scores (Fraboni & Cooper, 1989; King & Bruner, 2000). Social desirability bias, which is defined as the tendency for individuals to portray themselves in a generally favorable fashion (Holden, 1994, p. 429), has been studied since the 1950s (e.g., Edwards, 1957). One concern, however, is that Edwards s scale was derived from the Minnesota Multiphasic Personality Inventory (MMPI) and was associated with psychopathology. When Edwards s scale with items drawn from the MMPI is administered to college students, Crowne and Marlowe (1960) noted that the meaning of high social desirability scores is not at all clear (p. 349). Accordingly, the Marlowe Crowne Social Desirability Scale (MCSDS; Crowne & Marlowe, 1960) was developed to minimize pathological implications (p. 349) and tested on college students. The MCSDS consists of 33 true false statements in which 18 items describe socially approved behaviors but infrequent behaviors (e.g., I never hesitate to go out of my way to help someone in trouble ), and the other 15 items refer to socially disapproved but frequent behaviors (e.g., I like to gossip at times ). Since its development, the MCSDS has been described and adopted in more than 1,000 diverse studies (Beretvas, Meyers, & Leite, 2002). Although this scale has been widely used to measure one form of response bias or faking good, because of its length, several short forms of the MCSDS (Ballard, 1992; Reynolds, 1982; Strahan & Gerbasi, 1972) have been developed and used. According to Barger (2002), for example, Reynolds s forms were cited in 128 studies and Strahan and Gerbasi s forms were cited in 145 studies in the 1990s. In addition to the variety of uses to which the MCSDS has been put, there has also been some concern that the scale represents a unidimensional structure (called social desirability or need for approval ). Several researchers have investigated the dimensionality of the full and short forms of the MCSDS using factor analytic techniques, and v Hyunsoo Seol, Department of Education, Chung-Ang University, Seoul, Republic of Korea. This work was supported by a Korea Research Foundation Grant (MOEHRD, Basic Research Promotion Fund, KRF B00232) funded by the government of the Republic of Korea. Correspondence concerning this article should be addressed to Hyunsoo Seol, Department of Education, Chung-Ang University, Dongjak- Gu, Seoul, , Republic of Korea ( snow@cau.ac.kr) American Counseling Association. All rights reserved. Measurement and Evaluation in Counseling and Development October 2007 Volume

2 their findings have been contradictory (Barger, 2002; Collazo, 2005; Fischer & Fick, 1993; Leite & Beretvas, 2005; Loo & Thorpe, 2000; Paulhus, 1984; Ramanaiah & Martin, 1980; Ramanaiah, Schill, & Leung, 1977). In most of the studies investigating the factor structure of responses to the MCSDS, it was found that the MCSDS did not fit a one-factor model, suggesting a multidimensional structure. Recently, Barger found that the MCSDS did not fit the one-factor model using the Satorra-Bentler robust maximum likelihood estimation (Bentler, 1995). Leite and Beretvas also conducted confirmatory factor analysis using the mean and variance-adjusted weighted least squares estimator (L. Muthén & Muthén, 1998) and have found two subscales, thus raising questions about the unidimensional structure of the MCSDS. A number of problems regarding the use of linear factor analytic techniques on dichotomous data have been noted (Meara, Robin, & Sireci, 2000; B. Muthén, 1978; Olsson, 1979). The dimensionality of a matrix of phi coefficients, for example, may differ from the dimensionality of the underlying continuous variables (Bernstein & Teng, 1989; Hambleton & Rovinelli, 1986). Because responses to the MCSDS are in a true false format, the linear factor analysis may distort the underlying structure of dichotomous data (Bock, Gibbons, & Muraki, 1988; McDonald & Ahlawat, 1974). Many of the problems posed by factor analysis procedures with dichotomous items can be avoided with the use of item response theory models or, as illustrated in this study, with the use of the Rasch model (Rasch, 1960). Unlike the traditional factor analytic techniques, Rasch modeling places both the difficulty of items and the ability of persons on a common scale. The graphical representation (item person map) of the conjoint distribution of the person and item estimates on the same scale provides evidence about whether the instrument is appropriate for a given sample. The lack of spread of item difficulty estimates on the item person map suggests that some of the items may be redundant. Furthermore, although the models of factor analysis work under the assumption of normal distribution of the data, Rasch models make no such assumption (Slinde & Linn, 1979). It should be noted, however, that the importance of item-level analysis (i.e., focusing on the quality of the individual items, rather than on the whole test) has not been properly recognized in the published research of the MCSDS, mostly because previous research on the MCSDS is based on traditional methods of factor analysis. Because the Rasch model is based on the assumption of unidimensionality, deviations from Rasch model data fit are considered evidence of multidimensionality (Linacre, 1998; E. V. Smith, 2002; R. Smith & Miao, 1994; Wright, 1996). Given the frequent use of the MCSDS in many different fields and the conflicting findings about whether the MCSDS represents a unidimensional or a multidimensional construct, it is imperative to examine the dimensionality of the scale with the use of appropriate statistical techniques (Collazo, 2005; Leite & Beretvas, 2005); therefore, the purpose of this study was to examine the psychometric properties of the MCSDS by using the Rasch model with a college student sample. Method Participants The participants were 382 (248 undergraduate and 134 graduate) students from several education classes at a large private university in the Republic of Korea. Participants received extra course credit for participating. Participants were given a consent form and a brief explanation of the goals of the study. The questionnaire consisted of a page of demographic statements, followed by 33 true false items. Of the participants, 28.3% (n = 108) were men and 71.7% (n = 274) were women. The average age was years (median = 23.0). All participants completed the MCSDS in its entirety. 156 Measurement and Evaluation in Counseling and Development October 2007 Volume 40

3 Instrument The MCSDS consists of 33 items, of which 18 are keyed true items (i.e., highly desirable behaviors but low probability of occurrence) and 15 are keyed false items (i.e., socially disapproved behaviors but high probability of occurrence). Each item was rated on a 2-point (true false) scale. Gender and age were requested at the end of the questionnaire. Responses to the items were dummy coded as true = 1 and false = 0 for the positively keyed items and the reverse of this for the negatively keyed items. Thus, high scale scores indicate a strong tendency to respond in a socially desirable fashion. Translation Procedure I translated the original version of the MCSDS into Korean. The translation was checked by a counseling psychologist. Some modifications were made in the wording of certain items. The Korean version was then translated back into English by two bilingually fluent researchers. High convergence between the two versions was obtained. The Dichotomous Rasch model The dichotomous Rasch model specifies through log-odds that the probability of a person s response to item i is governed by the ability of the person (B n ) and the difficulty of the item (D i ): ln [P ni1 /P ni0 ] = B n D i, where P ni1 is the probability of an endorsed response, P ni0 is the probability of a nonendorsed response, B n is the trait (or ability) measure of person n, and D i is the difficulty of endorsing item i. One of the most important features of the Rasch model is its specific objectivity (Wright & Stone, 1979), which means the estimates of the ability of a person are freed from the sampling distribution of the items attempted and that the estimates of the difficulty of the items are freed from the sampling distribution of specific people used in the calibration if the data fit the model requirements. The Rasch model, according to Dinero and Haertel (1977), has also been found to be robust in detecting departure from the assumption of equal item discrimination. In the Rasch model, item-fit statistics, item standardized residual correlations, and principal component analysis (PCA) of the standardized residuals can be used to investigate the dimensionality of the MCSDS (Andrich, Sheridan, & Luo, 2005; Linacre, 1998, 2006). The Rasch analysis was conducted using the Winsteps computer program (Linacre, 2006). Measurement Properties Measurement properties of the MCSDS, including person separation reliability, person separation, and person strata indices, were evaluated. The Rasch analogue to Cronbach s alpha is called the person separation reliability, which refers to the ability to differentiate persons on the measured variable and the replicability of person placement across other items measuring the same construct (Wright & Masters, 1982). Fox and Jones (1998) considered person separation reliability equal to or greater than.80 acceptable. On the other hand, E. V. Smith (2001) has suggested that the index of person separation reliability is nonlinear and suffers from ceiling effect. The person separation index is an estimate of the spread of persons on the measured variable and is computed by the adjusted person standard deviation divided by the root mean square measurement error (Wright & Masters, 1982). The higher the value of the person separation index, which Measurement and Evaluation in Counseling and Development October 2007 Volume

4 has a range from zero to infinity, the more spread out the persons are on the variable being measured. In this study, I also examined the person strata index, which indicates the number of distinct ability levels separated by three errors of measurement. The person strata index is helpful when creating an assessment instrument designed to determine group differences. For example, a reliability of.80 gives a separation of 2.0, so that three strata can be usefully distinguished. Content Evidence of Construct Validity Messick (1995) defined the content evidence of construct validity as the degree to which the items on the instrument are representative of and relevant to the construct domain, as well as the technical quality of individual items. To examine content evidence of construct validity of the MCSDS, several methods are available within the framework of Rasch measurement (e.g., E. V. Smith, 2001; Wolfe & Smith, 2007a, 2007b). First, content evidence of construct validity can be examined by an interpretation of the intended item hierarchy, the spread of the item calibration, and separation and reliability statistics. In the Rasch analysis, the distributions of the ability of persons and item parameter estimates can be visually inspected to assess whether the difficulty level of the MCSDS is appropriate for the sample of persons. If an instrument is appropriately targeting a sample, there should be sufficient overlap between the range of person trait (ability) levels and the range of item difficulty levels. E. V. Smith (2001) also noted that the examination of person-fit statistics provides an indication of substantive validity. Therefore, to ensure that a person s response patterns to the MCSDS items correspond to that predicted by the Rasch model, I also examined person-fit statistics. Infit and outfit person-fit mean square (MNSQ) within a range of 0.5 to 1.5 is considered evidence of adequate fit, but these cutoff values are not absolute rules (Wright & Linacre, 1994). To provide evidence of internal structure of the MCSDS for the study sample, two (infit and outfit) item-fit MNSQ statistics (Wright & Stone, 1979) and point-measure correlations of each item were computed to determine how well each item contributes to defining one common construct. MNSQ has an expectation of 1.0 and a range from zero to infinity. Although a variety of ranges have been suggested to indicate adequate fit, I considered items to fit if their MNSQ fell within the range of 0.5 to 1.5, as suggested by Linacre (2006). Item MNSQ values greater than 1.5 indicate a lack of construct homogeneity, and values less than 0.5 indicate redundancy with other items. The point-measure correlations are computed in the same way as the point biserial correlations, except that the total raw scores are replaced by Rasch measures. Negative point-measure correlations may be due to scoring errors, rating scale with reversed direction, or multidimensionality. As noted earlier, the Rasch model is based on the assumption of unidimensionality. If there is a good model fit for persons and items, the residual components, representing the difference between what the Rasch model predicts and what is observed, should be random and contain no additional systematic information (Andrich et al., 2005; Linacre, 1998, 2006). This implies that the interitem residual correlations would approach zero if the data fit the Rasch unidimensional measurement model. The residual analysis for assessing unidimensionality in the Rasch model is similar to the method based on the principle of essential unidimensionality (W. F. Stout, 1990; W. R. Stout 1987). To help in decisions about the dimensionality of the MCSDS, I conducted PCA with standardized residuals. Linacre (1998) recommended that standardized residuals (residuals divided by their model standard deviation) are very useful, along with other types of residuals (e.g., raw score residual, logit residual). In this study, I considered an eigenvalue less than 3.0 (Linacre, 2006) and variance explained by the first component of residuals less than 10% to be an indication of unidimensionality, but there are also some other criteria for representing the existence of a secondary dimension (Andrich et al., 2005; Linacre, 2006; Raiche, 2005; Reckase, 1979; E. V. Smith, 2002; R. Smith & Miao, 1994). 158 Measurement and Evaluation in Counseling and Development October 2007 Volume 40

5 Generalizability of Construct Validity In the context of Rasch measurement, E. V. Smith (2001) stated that the generalizability aspect of construct validity is concerned with scope to which inferences regarding person measures or item calibration are invariant, within measurement error, across different tasks, items, groups, or contexts (p. 298). In order to provide evidence of generalizability of construct validity in this study, differential item functioning (DIF) to the MCSDS data was carried out to examine whether the items work in the same way for gender groups, regardless of their placement on the trait or ability. Differences in item location parameters from one subgroup to another were examined by dividing the difference by the joint standard errors of the items (Linacre, 2006). The resulting ratio is considered significant when it exceeds 2.0 in absolute value. This test is equivalent to the Mantel Haenszel significance test (Linacre & Wright, 1989). I also examined the Mantel Haenszel (Mantel & Haenszel, 1959) DIF estimates and the empirical item characteristic curve for each group. Results Paticipants Summary Statistics Table 1 provides participants summary statistics for the total sample and by gender. The mean measures for the male respondents and the female respondents on the MCSDS were.03 (SE =.07) and.37 (SE =.05), respectively, on the logit scale. A statistically significant gender difference was found, t(380) = 4.39, p <.01. The person separation reliability of the MCSDS was slightly higher for the female respondents than for the male respondents (.74 vs..71, respectively). Similar patterns were found for male respondents and the female respondents with respect to the person separation and person strata indices. The person separation reliability, person separation, and person strata indices for the total sample were 0.74, 1.69, and 2.59, respectively. This result implies that if the sample is normally distributed, there are about two measurably different levels of performance in this sample. Item Hierarchy and Targeting Figure 1 shows the distribution of the 382 participants and 33 MCSDS items on a common metric (logit scale), where each # represents 3 persons, and the MCSDS items are indicated by the item number. Table 1 Participants Summary Statistics for the Marlowe Crowne Social Desirability Scale Gender Variable Rasch Measure M SE a b R p Men (n = 108) Women (n = 274) Total Sample (N = 382) G p c Strata d a Standard error of person mean. b Person separation reliability, which has a range from 0 to 1. c Person separation index, which has a range from 0 to infinity. d Person strata index = (4G p + 1)/3. Measurement and Evaluation in Counseling and Development October 2007 Volume

6 Logit Scale Key: <more> = most able persons <less> = least able persons <rare> = most difficult to endorse items <frequ> = least difficult to endorse items # = 3 persons M = the location of the mean person and item measures S = one standard deviation away from the mean T = two standard deviations away from the mean FIGURE 1 Comparison Between Person Trait and Item Difficulty Measure As shown in Figure 1, the MCSDS items are arranged by degree of item difficulty from most difficult to endorse at the top (Item 31) to least difficult to endorse at the bottom (Item 24). The coverage of the 33 MCSDS items, which spans a wide range of a person s ability, indicates that this set of items is appropriately targeted for the sample being measured. The 160 Measurement and Evaluation in Counseling and Development October 2007 Volume 40

7 item separation index, which has a range from zero to infinity, shows whether the items are adequately dispersed along the latent traits of the MCSDS, implying the replicability of the order of the items across other samples (Bond & Fox, 2001). Larger separation index value indicates higher reliability (E. V. Smith, 2001). The item strata index can be used to determine the number of statistically distinct levels of item difficulty that respondents have distinguished (E. V. Smith, 2001; Wright & Masters, 1982). In this study, the item separation and strata indices were 7.54 and 10.39, respectively, thus indicating adequately dispersed items on the logit scale. Item Fit Two (infit and outfit) item-fit MNSQ statistics (Wright & Stone, 1979) and point-measure correlations of each item were computed to determine how well each item contributes to defining one common construct for the internal structure of the MCSDS (see Table 2). The results showed that no items produced infit or outfit values greater than 1.5 and less than 0.5. The fit value for one item (Item 7: I am always careful about my manner of Table 2 Item Location Estimates and Fit Statistics of the Marlowe Crowne Social Desirability Scale Item Measure Error Infit Outfit r pm Note. Infit = information-weighted mean square statistic; Outfit = outlier-sensitive mean square statistic; r pm = point-measure correlation. Measurement and Evaluation in Counseling and Development October 2007 Volume

8 dress ) was substantially higher than other fit values (infit = 1.27, outfit = 1.39), and there was a negative point-measure correlation for this item (r pm =.01), which may be viewed as a problem with the unidimensionality of the MCSDS. Person Fit Only 16 participants (out of 382; approximately 4%) in the study sample showed outfit values greater than 1.5, and no participants produced infit values greater than 1.5. This implies that the Rasch model requirements are not being severely violated to accommodate the data from this sample. Unidimensionality of the MCSDS To investigate whether the items constituting the Korean version of the MCSDS were measuring one general construct of social desirability, I examined the standardized item residual correlation matrix. High positive (or negative) correlation of residuals between pairs of items may indicate item redundancy, that is the dependency in the responses on these items is higher than can be accounted for by the unidimensional model. The finding showed that even the biggest correlation coefficient is relatively small (.23), which indicates that the standardized residuals may contain no additional systematic information. Next, a PCA of standardized residuals was performed. Table 3 shows the eigenvalues and percentage of variance attributed to each residual component. Sixteen residual components had eigenvalues greater than 1.0 and accounted for 65.10% of the total residual variance. The first component had an eigenvalue of 2.27, representing 6.87% of the residual variance. This can be interpreted as indicating unidimensionality of the MCSDS on the basis of the guidelines indicated earlier. As further evidence, the more similar the distribution of the percentages of variance accounted for by the component across the set of items, the more likely all items will fit the Rasch model (Andrich et al., 2005). Table 3 shows that the percentages of variance accounted for by the components were quite similar in magnitude, thus suggesting the lack of secondary structures (subdimensions) underlying the data. Figure 2 displays the expected and empirical item characteristic curves (ICC). The boundary lines indicate the upper and lower 95% two-sided confidence intervals. When an empiri- Table 3 Principal Component Analysis of Standardized Residuals Component Eigenvalue VAF Note. VAF = percentage of residual variance accounted for by the component. Cumulative VAF Measurement and Evaluation in Counseling and Development October 2007 Volume 40

9 Key: X = empirical point = item characteristic curve cal point, which is represented by x, lies outside the boundaries, then some unmodeled source of variance may be present in the observations (Linacre, 2006). Clearly, most of the empirical points are located within the 95% confidence interval along the expected ICC, implying that the data fit the Rasch unidimensional model. Differential Item Functioning for Gender The results of the DIF analyses for the MCSDS data are presented in Table 4. The DIF t tests identified 6 (out of 33) items as displaying DIF. As expected, the Mantel Haenszel significance test also revealed the same items as showing significant DIF, with the exception of Item 32 that showed nonsignificant gender-related DIF. Item 7 ( I am always careful about my manner of dress ) and Item 27 ( I never make a long trip without checking the safety of my car ) were significantly easier for women to endorse as true than they were for men. On the other hand, Item 19 ( I sometimes try to get even rather than forgive and forget ) and Item 32 ( I sometimes think when people have a misfortune they only got what they deserved ) were easier for women to endorse as false than they were for men. However, Item 3 ( It is sometimes hard to go on with my work if I am not encouraged ) and Item 28 ( There have been times when I was quite jealous of the good fortune of others ) were easier for men to endorse as false than they were for women. The expected and empirical ICCs of the 6 items showing significant DIF by gender are depicted in Figure 3. For Item 3 and Item 28, the bold line for men is higher than the line for women along the expected ICC, indicating that the DIF is in favor of men on these 2 items. Discussion FIGURE 2 Item Characteristic Curves (With 95% Confidence Intervals) The MCSDS was designed to assess the impact of social desirability on self-report measures. Despite a sufficient theoretical grounding in developing this instrument, the linear factor analyses with dichotomous data have yielded conflicting results. Several conclusions can be Measurement and Evaluation in Counseling and Development October 2007 Volume

10 Table 4 Differential Item Functioning for Gender Groups Item Men Measure SE Women Measure SE t MH ** ** * ** 3.26** ** ** ** ** ** 0.75** Note. MH = Mantel Haenszel significance test estimates. *p <.05. **p <.01. drawn from the present study, in which I used a Rasch measurement model to investigate the psychometric properties of the MCSDS. First, as seen in Figure 1, the difficulty levels of the 33 items reflect a wide range of respondents trait levels, indicating that the distribution of the person trait level is well targeted by the difficulty level of the 33 items. The person and item separation indices also indicate adequately dispersed persons and items on the logit scale. The order of item difficulty seems reasonable, although there is no previous research to check for agreement on this matter. Item 31 ( I have never felt that I was punished without cause ) was the most difficult to endorse item, followed by Item 2 ( I never hesitate to go out of my way to help someone in trouble ). These hard-to-endorse items were responded to as true by few participants and were responded to as false by most of the participants high severity indicators. On the other hand, the easiest to endorse item was Item 24 ( I would never think of letting someone else be punished for my wrong-doings ), and the next easiest was Item 17 ( I always try to practice what I preach ); that is, these easy-to-endorse items were responded to as true by most of the participants low severity indicators. These findings on item and person hierarchy can also be useful for developing short forms of the MCSDS. In 164 Measurement and Evaluation in Counseling and Development October 2007 Volume 40

11 Key: X = empirical point = women = men = item characteristic curve FIGURE 3 The Expected and Empirical Item Characteristic Curves of Six MCSDS Items Showing Significant Differential Item Functioning by Gender Measurement and Evaluation in Counseling and Development October 2007 Volume

12 this study, the most difficult (or easiest) to endorse items provide little information about social desirability for the study sample. Second, the results from the PCA with standardized residuals seem to support a unidimensional construct of the MCSDS, thus suggesting that the application of the scale as a measure of social desirability is warranted. In addition, item-fit MNSQ statistics also support a unidimensional construct of the MCSDS, with the exception of Item 7 ( I am always careful about my manner of dress ), which exhibited a negative pointmeasure correlation and statistically significant DIF showing that, for persons with the same trait location on the scale, women scored higher (responding true) than did men on this item. In other words, Item 7 is functioning differentially in favor of women. This is expected because, in general, Korean women are more concerned about appearance than are men during college life irrespective of women s tendency to respond in a socially desirable way. Moreover, because of cultural differences between Western and non-western samples, it might be that Item 7 demonstrates inadequate fit. Because little research exists to draw any definitive conclusions, cross-cultural investigations using the Rasch measurement models are needed to understand different displays of item fit across Western and non-western samples. Third, in this study, the male respondents had higher mean scores than did the female respondents on the MCSDS. Fraboni and Cooper (1989) also reported similar results. However, there are studies (e.g., Dalton, 1994; Robinette, 1991; Zook & Sipps, 1985) indicating that women score higher than men, as well as studies that reveal no gender differences on the MCSDS (e.g., Andrews & Meyer, 2003; Barger, 2002; Loo & Thorpe, 2000). To examine if DIF exists on the scale, the item difficulty estimates for the gender groups of male participants and female participants were compared. The results showed that 6 (out of 33) items displayed DIF. These 6 items are likely to have different meanings for men and women, and, therefore, extreme caution is warranted in gender comparisons on MCSDS scores. Additional research, involving content analysis, is needed to examine the role of culture and other factors that may cause differential functioning for some MCSDS items. Some limitations of this study must also be mentioned. First, the generalizability of the findings is limited to the Korean college student population. Because the MCSDS was developed and validated using data from Western samples, the adequacy of the MCSDS with non-western samples should be examined before using the MCSDS with such samples. Second, even though proper procedures were used in this study to ensure adequate translation of the original MCSDS, it is possible that cultural differences in the verbal expression of emotional and physical experiences may have affected the validity of MCSDS items; therefore, it should be noted that the Rasch analysis may have affected the validity of the MCSDS items and may lead to inconsistent results for the dimensionality of the MCSDS across Western and non-western samples. Third, to a certain degree, indications on the lack (or presence) of DIF may also result from a low test power because of the relatively small sample size used in this study. Future research is needed to crossvalidate the findings with larger samples (say, between 500 and 1,000 people). In summary, this article provides evidence of the MCSDS s unidimensionality. At the same time, however, some items display gender-related DIF. If there is a common cause for such items to function differentially, this may suggest the presence of a secondary dimension in the MCSDS. If, however, the DIF is caused by an unknown reason for each item, this should not be perceived as indicating new dimension(s). The content examination of the DIF items in this study did not indicate a common cause for their differential behavior, and, therefore, the presence of a second dimension in the MCSDS was ruled out. In conclusion, using the Rasch model to analyze the validity of the content and the generalizability of the MCSDS can be useful to researchers in counseling, education, and related fields. 166 Measurement and Evaluation in Counseling and Development October 2007 Volume 40

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