Health Education Research Advance Access originally published online on November 3, 2006
Health Education Research 2006 21(Supplement 1):i85-i97; doi:10.1093/her/cyl137
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© 2006 The Author(s).
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Innovative application of a multidimensional item response model in assessing the influence of social desirability on the pseudo-relationship between self-efficacy and behavior
1 Department of Pediatrics, Children's Nutrition Research Center, Baylor College of Medicine, 1100 Bates Street, Houston, TX 77030, USA
2 Department of Exercise and Health, Centre for Sport and Exercise, University of Bristol, Tyndall Avenue, Bristol BS8 1TP, UK
3 Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, 332 N. Lauderdale Street, Memphis, TN 38105, USA
* Correspondence to: K. Watson. E-mail: kwatson{at}bcm.tmc.edu
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Abstract |
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This study examined multidimensional item response theory (MIRT) modeling to assess social desirability (SocD) influences on self-reported physical activity self-efficacy (PASE) and fruit and vegetable self-efficacy (FVSE). The observed sample included 473 Houston-area adolescent males (10–14 years). SocD (nine items), PASE (19 items) and FVSE (21 items) were measured with previously validated self-report instruments containing Likert-type responses. Physical activity was objectively measured using the Computer Science Application Incorporated/Manufacturing Technology Incorporated (CSA/MTI) accelerometer. Total fruit, juice and vegetable consumption was measured with a food frequency questionnaire. Correlations between self-efficacy and behaviors were minimal, regardless of controlling for SocD. However, in a simulated sample derived to demonstrate the utility of MIRT when relationships exist, the pseudo-relationships between self-efficacy and behaviors were substantially weaker after controlling for SocD. MIRT provided disattenuated correlations between SocD and self-efficacy, thereby providing more precise estimates of the real influence of SocD on the relationship between self-efficacy and behavior. However, as shown in the observed sample, more research is needed to understand the influence of SocD on the relationship between self-efficacy and behaviors for different populations and for different degrees of SocD response bias.
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Introduction |
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Social desirability (SocD) response bias is the self-reported overestimation of acceptable traits or behaviors and the underestimation of unacceptable ones [1, 2]. Responding in a socially desirable manner is considered a normal part of child development and often decreases as children age. Although SocD response bias has been assessed in conjunction with some age-related psychological constructs and socially unacceptable behaviors [1, 2], little research has examined the relationship between SocD and physical activity and diet-related self-efficacy. Several studies examined the association between SocD and nutrition/energy intake [3, 4] and physical activity [5] in adults and college students. There was no effect of SocD on total energy intake and fruit and vegetable (FV) intake derived from 24-hour daily recalls and three self-report measures in the overall sample [4]. However, when comparing highly educated women to women with less than a college education, the educated women tended to underreport on the food frequency self-report by SocD [6]. SocD also was associated with energy underreporting in women only [7], with underreporting of energy/FV intake [8, 9], and was among the best predictors of the 24-hour recall [but not the food frequency questionnaire (FFQ)] in men and women [10].
Although SocD was not correlated with several measures of self-report physical activity in a sample of women in a study by Adams et al. [11], additional results indicated inconsistencies with associations between SocD and the overreporting of physical activity and the underestimation of the duration of physical activity. There was minimal evidence of a weak relationship between SocD and two self-report measures of physical activity in a large sample of college students [5], but SocD was the strongest predictor of physical activity in adults [12]. In a sample of young African–American girls, SocD was positively associated with physical activity [1]. These studies provide evidence of potential confounding of health outcomes due to SocD response bias in self-reported behaviors. New methods for assessing how and when SocD influences responses should enable other researchers to better control for it.
Traditionally, SocD is represented as a composite average or summative score (based on classical test theory methods) and its influence is statistically controlled through its treatment as a covariate in analysis (e.g. partial correlations, multiple regression analyses, analyses of covariance). Item response theory (IRT) modeling [13, 14] offers an alternative multidimensional method for analyzing the effects of SocD bias. The multidimensional item response theory (MIRT) analyses can be performed with the self-efficacy scales and SocD by assessing SocD as a second dimension of self-efficacy [15–18]. The MIRT model provides more precise measures (higher reliability, better model fit) of SocD and self-efficacy by incorporating additional information [15–18]. For example, in the estimation of self-efficacy, the method of estimation accounts for the relationship between self-efficacy and SocD. The relationship between IRT model-derived scores for self-efficacy and behaviors controlling for SocD can then be investigated. This study demonstrated the utility of the MIRT method in examining the role of SocD influences on physical activity and fruit and vegetable self-efficacy (FVSE) and behaviors.
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Methods |
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Sample
The sample included 473 Houston-area 10- to 14-year old boys from 42 Boy Scout troops recruited to participate in a randomized intervention involving either a 9-week Internet-based physical activity [19] or nutrition program (Thompson et al., submitted). The data reported in the current analyses used only baseline assessment from the study. The race and ethnic distribution of youth consisted of 73% non-Hispanic white, 4% non-Hispanic African American, 13% Hispanic and 10% classified as ‘other’. Approximately 36% of the participants were considered to be at risk of overweight [body mass index (BMI)
85%] using Centers for Disease Control and Prevention norms [20]. The Baylor College of Medicine's Institutional Review Board approved the study protocol. Written informed parental consent and youth assent were obtained from all study participants.
Instruments
Social desirability
SocD was ascertained with the ‘lie scale’, from Reynolds' and Paget's Revised Children's Manifest Anxiety Scale [21]. The scale has shown to be reliable and valid in children 8–10 years of age [1]. Concurrent validity of the lie scale as a measure of SocD was also established in a study by Hagborg [22]. The lie scale consisted of nine items related to SocD. Item responses were modified from the original two-point scale (‘yes/no’) to a five-point Likert scale from ‘1 = always true of me’ to ‘5 = never true of me’. The items were recoded so that a higher score indicated a greater tendency to respond in a more SocD manner. The internal consistency of the scale, based on Cronbach's alpha, was 0.86 in this sample.
Physical activity self-efficacy
The physical activity self-efficacy (PASE) scale included Saunders' et al. [23] scale plus two additional items. The scale was shown to be reliable and valid in a sample of 558 youth. In the original study, test–retest reliability was 0.76, 0.82 and 0.61 for the support seeking, barriers and positive alternatives subscales, respectively. In the developmental sample, construct validity was established with observed significant correlations among the subscales and intention to be physically active. Additionally, the barriers subscale was significantly correlated with a self-report measure of physical activity. The scale contained 19 Likert-type items with each response scored between ‘1 = disagree a lot’ and ‘5 = agree a lot’. High PASE scores indicated a strong sense of self-efficacy. The internal consistency of the scale, based on Cronbach's alpha, was 0.94 for this sample.
Fruit and vegetable self-efficacy
The 21 FVSE items included two subscales: ‘shopping/asking’ self-efficacy and ‘selection’ self-efficacy [24, 25]. The original 34-item scale [24] employed three response options to each item: ‘not at all sure’, ‘a little sure’ and ‘very sure’. The original scale demonstrated adequate 2-week test–retest reliability (r = 0.70) and high internal consistency (Cronbach's 
> 0.80). Content validity of the instrument in the original study was assessed through a series of steps: pilot testing, revisions and principal components with stable loadings across two split-half samples. Construct validity was assessed through correlations among the FVSE subscales, preferences, outcomes expectancies and FV consumption. Three of four subscales were significantly correlated with preferences (r = 0.18–0.49) and health/physical activity outcomes expectancies (r = 0.13–0.25). One subscale (‘breakfast and lunch FV, and paying for FV’) was significantly correlated (r = 0.18) with FV consumption. In the second study [25], the number of items was reduced and content validity of the reduced scale was assessed using principal components. The two-factor scale demonstrated adequate reliability (Cronbach's 0.78). The two subscales were significantly correlated with FV preferences, exposure, knowledge, outcome expectancies, social norms and asking behaviors (r = 0.15–0.43). The two subscales were significantly correlated (r = 0.12) with FV consumption. In this study, the scale employed five Likert-type responses scored between ‘1 = disagree a lot’ and ‘5 = agree a lot’. High FVSE scores indicated a strong sense of self-efficacy. The internal consistency of the scale, based on Cronbach's alpha, for this sample was 0.91.
Fruit, juice and vegetable consumption
Fruit, juice and vegetable consumption was measured through the use of a FFQ previously validated against the 24-hour recall in a sample of Boy Scouts [26]. In the validation study, there was evidence of strong validity (r = 0.92) between the first assessment of the FFQ and the 24-hour daily recall for fruit, juice and vegetable consumption, after correcting for the reliability of the recall [26]. The FFQ contained a list of four juices, 17 fruits and 17 vegetables. The response scale represented the number of servings consumed in the past week, thereby avoiding averaging. Total consumption was computed as the sum of all servings of fruit, 100% juice and vegetables in the past week. Cronbach's alpha for the FFQ in this sample was 0.88.
CSA/MTI accelerometer
Habitual physical activity was measured with the CSA/MTI accelerometer which has previously been demonstrated to be valid and reliable [19, 27–29]. The accelerometer was validated and calibrated against several measures of energy expenditure for a period of six continuous hours. Correlations with the accelerometer located on the hip were 0.66, 0.61 and 0.57 for respiration calorimetry, activity by microwave detector and heart rate by telemetry, respectively [28]. The CSA/MTI was placed above the right hip and the boys were instructed to wear the accelerometer at all times, except when in contact with water, for three consecutive days. After 3 days, the information obtained from the CSA/MTI was downloaded. Only minutes between 6.00 a.m. and midnight were included in the analyses. Unrecorded time (or time the monitor was not worn) was determined as any interval of 20 min of continuous zero CSA counts. Days with recorded time <800 out of a possible 1080 min were considered invalid and excluded from analyses [19]. The number of minutes of moderate-to-vigorous physical activity (MVPA) was classified at or above the cut point of 3200 counts minute–1 as determined against energy expenditure [28]. Minutes with CSA counts equal to or above this threshold indicated that the participant was engaged in MVPA and were subsequently summed each day. To account for differences in the time the monitor was worn, the average number of minutes of MVPA was weighted by the inverse of the proportion of time the monitor was worn providing an estimate of number of MVPA minutes per 1080 minutes.
Statistical analyses
Overview of Rasch IRT models
IRT, a method for determining the psychometric properties of a scale, consists of a variety of mathematical models that link the probability of a response to the underlying (latent) ability or ‘trait’ being measured [13, 14, 30]. For this study, the traits being measured were self-efficacy (e.g. PASE and FVSE) and SocD. Each item and each person are ordered by their location along the latent continuum of the construct being measured. ‘Item difficulty’ refers to the underlying trait and how difficult it is for a person to respond affirmatively to an item. ‘Item discrimination’ refers to how well an item can discriminate between persons who have relatively higher or lower levels than the item on the continuum [14]. The Rasch family of models, a special case of IRT, assumes constant slope (fixed at 1.0) across all items in the instrument [13]. In other words, items are assumed to discriminate equally well between persons with traits above and below the difficulty of the item. Results from the Rasch model for data yield item location and person ability/trait estimates, model fit values [infit mean square (MSQ) values] and reliability measures [13, 30, 31]. A more detailed discussion of the Rasch family of models may be found in the introductory articles of this issue [30, 31].
Model development
Although a primary assumption for most IRT models is that the underlying trait is unidimensional, there are circumstances in which this assumption may not be valid. As such, the potential benefit of incorporating multidimensionality into IRT models has been recognized [15, 17, 32, 33]. Additional issues to consider are local independence of items and appropriate sample size. Local independence may be inferred when the unidimensionality assumption is met [14]. The sample size issue is complex and depends on a number of factors (e.g. number of items, goal of the study, number of responses per item, the IRT model, the distribution of the sample) [34, 35]. According to Lincare [34], a sample size of 150 is adequate for a simple Rasch model and an additional 10 participants per response category is needed for polytomous items. In a study by Choi et al. [36], a sample of 250 participants has been demonstrated to be adequate for the estimation of the partial credit model. Only two additional parameters, in addition to the total number of item parameters associated with the SocD and self-efficacy scales, were estimated due to multidimensionality. The partial credit model [37] is an extension of the Rasch model and accounted for the ordinal nature of the responses.
A between-item multidimensional model was used in this study, indicating that each item measured only one dimension. This model provides more efficiency in regard to reliability and model fit than independent unidimensional models [15, 18]. Any possible interrelationship between items in the PASE (or FVSE) and SocD would be ignored if each scale were treated independently; therefore, consideration of multidimensionality would show influence from another variable, in this case SocD. When a relationship between variables is present, more efficient estimation [smaller standard errors (SEs)] is possible. The additional information provided by participants' SocD responses may provide additional information regarding the participants' reports of self-efficacy and would best be utilized by choosing multidimensional modeling. As an example, better fit was demonstrated with the multidimensional model in a study regarding mathematical proficiency classification [32]. The multidimensional approach has the advantage of providing a measure for each trait being assessed while simultaneously considering any correlation between the latent constructs [38]. In other examples [17, 33], estimates from the unidimensional model as opposed to the multidimensional model were attenuated substantially. All IRT analyses were performed using ConQuest [38].
Although there are several methods to assess unidimensionality [39], the IRT assumption of unidimensionality for each of the scales (SocD, PASE and FVSE) was assessed through confirmatory factor analyses (CFAs). Item fit was examined through the use of infit MSQ summary statistics. MSQ fit values range between zero and infinity with values near one indicating close agreement between observed and expected values. Values >1.0 indicate more variation in the responses and values <1.0 indicate less variation in the responses than expected. Infit MSQ values outside the range of 0.75 and 1.33, with significant t values, were used for establishing item misfit [30]. Reliability, conditional on the trait, was also used to assess the model [13, 31].
Item and person difficulty (latent variable) estimates were obtained for each scale. Thurstonian thresholds were presented to describe the degree of difficulty of selecting the various responses for each of the items. Person estimates were obtained from the plausible values (PVs) computed during the estimation process [38, 40]. PVs, as opposed to maximum likelihood estimates and expected a posteriori estimates, were used because the PVs provide unbiased estimates of the latent traits [38, 40].
Simulated data
Preliminary analyses showed weak correlations among the SocD raw scores, self-efficacy raw scores and physical activity and dietary intake behaviors. Therefore, a data set was simulated for the purpose of demonstrating the influence of SocD when a relationship among SocD, self-efficacy and behaviors was present. In other words, a pseudo-relationship among SocD, self-efficacy and behaviors was created to demonstrate the utility of MIRT. The simulated data consisted of a subset of participants who met the following criteria: (i) the observed standardized (Std) PASE score was within the 68% confidence bands corresponding to the hypothesized regression line representing the desired moderate correlation between Std PASE and Std SocD and (ii) within the same confidence band for the regression line representing the desired moderate correlation between Std PASE and Std MVPA. Participants were selected in a similar fashion for the FVSE analysis. The simulated data consisted of 206 participants for the PASE analyses and 210 participants for the FVSE analyses. Please note that these data were selected to demonstrate the utility of the MIRT procedure, and should not be construed to reflect true relationships
Correlational analyses
Pearson bivariate correlations were used to examine the relationships among self-efficacy, SocD and behavior. Partial correlations were used to assess the associations between self-efficacy and behaviors, controlling for SocD. SocD was deemed to be a substantial confounder if the change-in-estimate due to confounder [(crude – adjusted estimate)/crude estimate x 100%] [1] was >25%. The correlations between self-efficacy and SocD were extracted from the ConQuest output and all other correlations were performed using SPSS for Windows Release 11.0.01 [41].
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Results |
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Participants
Although 473 Houston-area Boy Scouts participated in the study, only 452 scouts provided PASE, FVSE and SocD data. Race/ethnicity was the only significant (P < 0.05) difference observed in demographic and anthropometric characteristics between those with and without complete data. Although Hispanic and other youth did not have any missing data, the overall distribution of participants with complete data was nearly the same as the total sample with 73% white, 3% African American, 14% Hispanic and 10% other youth. A large portion (70.4%) of scouts came from homes with a parent who had at least a college education. Roughly one-fourth (23.8%) of the scouts lived in homes where the parent had at least some college or technical education and 5.6% of the scouts came in homes where the parents had a high school education or less. The majority (64.2%) of scouts was considered to have a normal (BMI < 85%) for their age and gender. The mean age of the scouts was 12.8 years (SE = 0.05). Mean SocD and self-efficacy IRT-derived scores were 0.45 (SE = 0.05), 1.01 (SE = 0.06) and 0.84 (SE = 0.04) for SocD, PASE and FVSE, respectively. Scouts spent an average of 5.31 (SE = 1.10) min day–1 engaged in MVPA and consumed 0.84 (SE = 0.04) servings of FV per day based on IRT.
Rasch models
CFA was performed using LISREL 8.54 [42] to establish unidimensionality of the scales. CFA indicated adequate model fit of the SocD scale [goodness of fit 
2 = 78.37, df = 21, P < 0.001; root mean square error of approximation (RMSEA) = 0.078; comparative fit index (CFI) = 0.98] [43]. Adequate model fit was also demonstrated for the PASE scale (2 = 537.72, df = 142, P < 0.001; RMSEA = 0.079, CFI = 0.98) and the FVSE scale (2 = 607.69, df = 181, P < 0.001; RMSEA = 0.072, CFI = 0.97). Upon examination of the SocD and PASE IRT analysis, all average item location infit indices and step parameter indices for the SocD and PASE model were within the range (0.75–1.33) of acceptable fit. These results indicated that the SocD and PASE model fit the data reasonably well. Thurstone item threshold estimates for SocD and PASE are shown in Fig. 1. Threshold 1 for the SocD scale represents the point at which the cumulative effect of the ‘sometimes true of me’, ‘not sure’, ‘not often true of me’ and ‘never true of me’ options are more likely than ‘always true of me’. Threshold 1 for the PASE scale represents the point at which the cumulative effect of the ‘disagree a little’, ‘not sure’, ‘agree a little’ and ‘agree a lot’ options are more likely than ‘disagree a lot’. For a more complete discussion of the Thurstone thresholds, refer to the paper of Wilson et al. [30] in this issue. The latent distribution for SocD and PASE, in conjunction with item thresholds, are also shown in the Wright map in Fig. 1. While the SocD item thresholds (Items 1–9) extended over most of the SocD person trait distribution, the PASE trait distribution appears to be top heavy, meaning that the PASE scale items (Items 10–28) did not encompass participants with high levels of PASE.
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The reliability for the PASE scale derived from the multidimensional model is shown in Fig. 2, as a function of PASE. Lower reliability was seen at the extreme ends of the PASE continuum. Except at the extreme ends, the reliability was acceptable. The summary measures of between-person reliability, analogous to Cronbach's alpha, were 0.80 and 0.94 for SocD and PASE, respectively.
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Examination of the SocD and FVSE analysis yielded average item location infit indices, with exception of the first SocD item (infit = 1.49, t = 4.2), within the range (0.75–1.33) of acceptable fit. All step parameter estimates were within the range of acceptable fit. Overall, these results indicated that the SocD and FVSE model fit the data reasonably well. Thurstone item threshold estimates for SocD and FVSE are shown in Fig. 3. Similar to PASE, Threshold 1 for the FVSE scale represents the point at which the cumulative effect of the ‘disagree a little’, ‘not sure’, ‘agree a little’ and ‘agree a lot’ options are more likely than ‘disagree a lot’. The latent distribution for SocD and FVSE traits, in conjunction with item thresholds, are also shown in Fig. 3. As expected, examination of the Wright map indicated that the SocD thresholds (Items 1–9) covered the range of the trait distribution. The FVSE trait was more tightly distributed with a more restrictive range. The item thresholds for the FVSE scale (Items 10–30) provided better coverage of higher levels of self-efficacy than the PASE. For participants at high levels of FVSE, the majority of item coverage was at the lower extreme of this trait. The reliability for the FVSE scale derived from the multidimensional model is shown in Fig. 4, as a function of FVSE. Lower reliability was seen at the extreme ends of the FVSE continuum. Except at the extreme ends, the measurement error was small and the reliability was adequate. The summary measures of between-person reliability were 0.86 and 0.80 for SocD and FVSE, respectively.
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Correlational analyses
Results from the correlational analyses for the observed sample and the simulated sample are shown in Table I. The observed sample yielded extremely weak to no correlations (0.01–0.18) among SocD, PASE and MVPA, regardless of controlling for SocD. Similar results were observed among SocD, FVSE and FV intake. There was minimal (4.3–5.8%) change-in-estimate due to confounding by SocD for PASE and FVSE, respectively.
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Results from the simulated sample yielded a moderate correlation (r = 0.60) between SocD and PASE. The correlation between PASE and MVPA minutes was moderately weak (r = 0.35). However, after controlling for the effect of SocD, the correlation between PASE and MVPA minutes was very weak (r = 0.23). The correlation between SocD and MVPA minutes was 0.30. The change-in-estimate decrease for PASE after including SocD was 34.3%. Similar results were observed for the relationship between FVSE and FV intake. The correlation between SocD and FVSE was 0.56. The correlation between FVSE and FV intake was moderate (r = 0.43). However, after controlling for the effect of SocD, the correlation was weak (r = 0.29). The change-in-estimate decrease for FVSE after including SocD was 32.6%. The correlation between SocD and FV intake was 0.37.
In IRT analyses where SocD and self-efficacy were estimated using separate unidimensional models (analyses not shown), the correlations (r = 0.42 and 0.38) between SocD and self-efficacy were moderately weak for PASE and FVSE, respectively. With the sample sizes >200, all the above correlations in the simulated sample were statistically significant (P < 0.05).
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Discussion |
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The analyses from the original data demonstrated a weak relationship, at best, among SocD, self-efficacy and physical activity and dietary behaviors among adolescent males. Since these analyses were conducted, in part, to demonstrate the utility of MIRT in assessing the influence of SocD on the relationship between self-efficacy and behaviors, the results from the simulated data did show the pseudo-relationship between self-efficacy and behaviors were noticeably weaker when controlling for SocD. These findings suggest that when a strong relationship exists between SocD and self-efficacy and a moderate relationship exists between SocD and behaviors, the influence of SocD on the relationship between self-efficacy and behaviors is substantial as shown by change-in-estimates >25%. With an objective measure of MVPA, it is more likely that the influence of SocD was more highly related to the self-efficacy responses and less for the accelerometer data. Furthermore, the moderate correlations between SocD and self-efficacy were not attenuated as observed with the unidimensional models of SocD with PASE (from r = 0.60 to 0.42) and FVSE (from r = 0.56 to 0.38). In other words, the attenuated correlations were lower than the true correlations due to measurement error and thus misrepresent the true relationship.
The strengths of this study include the use of instruments (SocD, PASE and FVSE) that were validated in previous studies; the use of two (not one) measures of self-efficacy (PASE and FVSE) to replicate findings; and the use of statistical techniques that overcame limitations of traditional psychometric techniques. In general, the use of IRT provided difficulty estimates that were not dependent on this sample and ability estimates of SocD and self-efficacy that were not specific to the items on the instrument. Additionally, by using IRT, specific areas (e.g. the extremes) along the SocD and self-efficacy constructs that were not well measured by the items were identified. More specifically, by using the multidimensional model, additional information related to correlated constructs, e.g. SocD, was used to provide more reliable estimates. The MIRT model also allowed the examination of the ability distributions and item difficulty estimates of both constructs along the same continuum. Although the self-efficacy scales exhibited adequate reliability, except for participants with scores at the extreme ends, the FVSE scale was better able to capture and discriminate among participants with high levels of self-efficacy than the PASE scale. The practical implications would be that it may be necessary to add a couple of items that would discriminate among participants with high levels of PASE or caution should be used when trying to interpret the relationship between self-efficacy and behaviors for a very extreme homogeneous sample. Additionally, the FVSE distribution of scores was much tighter than the PASE distribution, which could be indicative of a less heterogeneous sample. Perhaps, the relationship between FVSE and behaviors might be different in a more diverse sample.
This MIRT method may also be used for other correlated psychological constructs such as norms, attitudes and beliefs. The benefits of this study in relation to the association among SocD, self-efficacy and physical activity and diet behavior, support the need for further research in these areas. The limitations of the study include the lack of generalization of the relationship among SocD, self-efficacy and behaviors because of the deliberate selection of the subset for creating simulated data. Additionally, different magnitudes of the relationship among SocD, self-efficacy and behaviors were not examined.
In summary, MIRT methods to assess response biases such as socially desirable responding holds promise of more precisely estimating self-reported psychosocial measures. The advantages of IRT in scale construction and assessment have been well documented [14]. Through the enhancement of the precision of measurement and the dimensions underlying psychosocial measures, IRT techniques will likely provide more precise estimates of their relationship to behaviors and a more comprehensive understanding of the true nature of the relationship. Furthermore, by obtaining more information about a scale through improved measurement, such as identifying levels of the trait that are not as reliable, and levels of the trait which are not being reached, knowledge of the association between psychosocial measures and behaviors can be enhanced. Recommendations for future research would include additional studies to examine the influence of SocD on the relationship between subconstructs of self-efficacy and behaviors as well as using MIRT to examine the relationship among other psychosocial constructs and behavior.
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Conflicts of interest statement |
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None declared.
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Acknowledgements |
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TopAbstractIntroductionMethodsResultsDiscussionConflicts of interest statementAcknowledgementsReferences |
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The writing of this paper was supported in part by a grant from the American Cancer Society, ACS TURSG-01. This work is also a publication of the US Department of Agriculture (USDA)/Agricultural Research Service (ARS) Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, Houston, TX. This project has been funded in part by federal funds from the USDA/ARS under cooperative agreement 58-6250-6001. The contents of this publication do not necessarily reflect the views or polices of the USDA, nor does mention of trade names, commercial products or organizations imply endorsement by the US Government.
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References |
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Received on February 16, 2006; accepted on September 25, 2006
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