Development and Validation of Implicit Measures of Organizational Citizenship Motives. Tonielle Fiscus and Donald Fischer Missouri State University

Transcription

1 Development and Validation of Implicit Measures of Organizational Citizenship Motives Tonielle Fiscus and Donald Fischer Missouri State University Fiscus, T. & Fischer, D. (2017, April). Development and Validation of Implicit Measures of Organizational Citizenship Motives. Paper presented at the 32 nd annual meeting of the Society for Industrial and Organizational Psychology, Orlando, FL 1

2 ABSTRACT Implicit Association Test (IAT) procedures were used to develop non-bipolar, valence-balanced IATs designed to measure three organizational citizenship motives. Confirmatory factor analysis of nested models provided evidence of convergent and discriminant validity and regression analyses provided limited criterion-related validity evidence. The results suggest the measures warrant further development and study. PRESS PARAGRAPH Only recently have researchers begun to investigate the reasons why employees engage in OCB. The self-report measures of OCB motives that currently exist are susceptible to contamination from impression management and self-knowledge artifacts, which may attenuate predictive validity. Because the Implicit Association Test (IAT) uses reaction times on classification tasks, the procedure is resistant to contamination due to these artifacts. We developed four IATs to assess the OCB motives described by Rioux and Penner (2001). Confirmatory factor analysis provided evidence of convergent and discriminant validity, which along with limited criterionrelated validity evidence, suggests the measures warrant further development and study. WORD COUNT

3 Organizational Citizenship Behavior (OCB) is defined as individual behavior that is discretionary, not directly or explicitly recognized by the formal reward system, and that in the aggregate promotes the effective functioning of the organization (Organ, 1988, pg.4). The construct of OCB is not a recent one. Daniel Katz is often cited as the first to introduce this concept to industrial and organizational psychology when he identified three basic tenets of behavior that were essential for organizations to function effectively: individuals must be induced to enter the organization and remain, carry out tasks related to their role in a reliable fashion, and there must be actions that go beyond the prescribed role activities (Katz, 1964). The third domain is where the concept of OCB originated. Four lines of research have emanated from the introduction of the concept of OCB: exploring relationships between OCB and organizational effectiveness, determining the emphasis placed on OCB versus task performance during performance appraisals, investigating whether personality variables predict OCB better than task performance, and identifying motives for performing OCB in organizations (Borman & Penner, 2001). The focus of the current study is upon the motives for performing OCB in organizations. One way to operationalize measures of OCB motivation is through self-reports. For example, the Citizen Motives Scales (CMS), developed by Rioux and Penner (2001), contain three 10-item subscales which respondents rate on a six-point Likert scale. In addition to being internally consistent and stable, the CMS subscales have been shown to predict OCBs measured by self, peer and supervisor ratings (Rioux & Penner, 2001; Takeuchi, Bolino, and Lin, 2014). While the subscales correlate with each other, they are not so strongly related that singularity is thought to exist, which suggests that multiple motives are likely at work. However, because the scales are self-report measures, a potential problem is that individuals may intentionally 3

4 manipulate their results in order to be seen in a more favorable light. This is especially likely for the Impression Management subscale, where those who are most likely to score highly if the scale is answered honestly, are those most likely to avoid presenting themselves in a negative light by lying. To control for impression management effects, a format other than a self-report procedure might be used. For example, the Implicit Association Test (IAT) uses reaction times in categorical sorting tasks to determine the strength of associations among the target categories (Greenwald, McGehee & Schwartz, 1998). Target categories can be designed to include personality attributes, like motivational dispositions (Schnabel, Asendorpf & Greenwald, 2008). The IAT has been shown to be resistant to impression management and self-knowledge artifacts (Greenwald, Banaji, Rudman, Farnham, Nosek, & Mellot, 2002). In addition, assessment results based on IATs often differ from those based on self-report measures, especially when there is an incentive and opportunity for respondents to deliberate before responding on the explicit measure (Fazio & Olsen, 2003; Vecchione, Dentale, Alessandri, & Barbaranelli, 2014). Long before the current interest in IAT measures developed, psychologists like Henry Murray and David McClelland recognized and studied the dissociation of implicit and explicit measures of motivational constructs (McClelland, 1958; Murray, 1938) and found value in using implicit assessments to predict behavior (McClelland, Atkinson, Clark & Lowell, 1953). The present study sought to develop IATs that target OCB motives and investigate their psychometric properties. Campbell and Fiske s (1959) multitrait-multimethod (MTMM) design was used to investigate the construct validity of the implicit measures. More specifically, Widaman s (1985) confirmatory factor analytic (CFA) procedure, which involves evaluating a set of hierarchically nested latent trait models, was used to examine the convergent and 4

5 discriminant validity evidence framed by Campbell and Fiske s MTMM strategy. It was further hypothesized that the implicit measures will predict OCB in ways that compliment that which can be obtained with explicit measures alone. These hypotheses and procedures will be described in more detail below. Participants METHOD Participants (N = 236) were recruited through Amazon s Mechanical Turk. Explicit Measures Five types of OCB described by Organ (1988) were measured through the use of a scale developed by Podsakoff, MacKenzie, Moorman, and Fetter (1990). This 24-item measure provides scores on five subscales (Altruism, Conscientiousness, Courtesy, Sportsmanship, and Civic Virtue). Example items include, Attends meetings that are not mandatory, but are considered important and Helps others who have been absent. Responses are recorded using a five-point Likert scale to indicate how much or little OCB the respondent thinks his or her supervisor would likely say he or she does. Alpha coefficients exceed.80 for all of the subscales except for Civic Virtue (Podsakoff, et al., 1990). The Citizenship Motives Scales (CMS; Rioux & Penner, 2001) were used to assess OCB motives. This measure contains three subscales which are labeled Organizational Concern (OC), Prosocial Values (PV), and Impression Management (IM). Alpha coefficients for the three subscales are reported to be very good (OC =.94, PV =.91, IM =.91). Items from the subscales ask the respondent to report, for example, whether they do OCB Because I care what happens to the company (OC), Because I believe in being courteous to others (PV), and To look better than my co-workers (IM). Respondents rate the importance of each item using a six-point Likert scale. 5

6 Three facet scales from the NEO-PI-R (Costa & McCrae, 1992) were selected based upon their theoretical relationship to the three OCB motives identified by Rioux and Penner (2001). The three NEO scales and the corresponding OCB motives were A3-Altruism (for PV), N4-Self- Conscious (for IM), and C3-Dutifulness (for OC). Sample items include, I try to perform all the tasks assigned to me conscientiously (C3), I seldom feel self-conscious when I m around people (N4), and I try to be courteous to everyone I meet (A3). Respondents rate their agreement with each item using a five-point Likert scale. The alpha coefficients reported in the NEO-PI-R Manual for the scales range from.87 to.92. Implicit Measures Four IATs were developed following the guidelines provided by Lane, Banaji, Nosek, & Greenwald (2007) in conjunction with the standard seven-block procedure and D-scoring method described by Greenwald, Nosek and Banaji (2003). Several iterations were necessary to develop usable IATs. First, category labels representing the three motives measured by the CMS subscales were selected: Commitment (for OC), Altruism (for PV), and Status (for IM). Second, lists of word stimuli were developed for each category that were semantically related to the category labels. The valence of the stimuli, as well as the ease with which the stimuli could be quickly and accurately sorted into the labeled categories were rated by research team members. Stimuli were then selected according to these criteria and the resulting IATs pilot tested and further refined based upon their psychometric properties (internal consistency and misclassification error rates for stimuli). The matching of stimuli across categories in accord with their valence and classification ease follows the advice of Schnabel, Asendorf, and Greenwald (2008), who recommend using valence-balanced stimuli in order to avoid confounding IAT effects with implicit self-esteem. This was especially important for the Status IAT as negative 6

7 connotations are often associated with words related to impression management (i.e. selfish, manipulative, etc.). In the end, four stimuli were selected for each IAT category, as advised by Nosek, Greenwald, and Banaji (2005). Table 1 displays the category labels and stimuli chosen for the three IATs that target the motives measured by the OCM subscales. These IATs are nonbipolar in that they pair the OCB motive categories with each other (i.e. Status Altruism, Commitment Status, and Altruism Commitment), so as to assess the relative strength of each. This non-bipolar matching procedure has previously been used with IATs measuring Big Five personality traits (Back, Schmukle, & Egloff, 2009; Schnabel, Asendorf, and Greenwald, 2008). In addition to the three IATs that target individual OCB motives, a single composite IAT which targets overall OCB motivation was also developed. Two stimuli from each of the individual OCB motive categories and a single-target format (Karpinski & Steinman, 2006) were used to construct this IAT. Unlike the standard IAT format, the single target format does not have a comparison category for the target attribute; the IAT effect is based upon reaction times for the alternate pairing of target stimuli (work motive words) with each of two descriptor categories (good vs. bad). The category labels and stimuli used in the single-target IAT are displayed in Table 2. Procedure Participants selected the study from a listing on Amazon s Mechanical Turk web site by clicking on a link displayed on their computers. All participants received the IATs first, followed by the explicit measures. The explicit section began with basic demographic questions, followed by the OCB scales, CMS scales, and the NEO scales. Participants were compensated for their participation. RESULTS 7

8 Response data from the 236 participants was screened for inaccuracy (missing values and excessive error rates on the IAT classification tasks) and outlier status. Fifteen participants were eliminated because of multivariate outlier criteria (Mahalanobis distance metric, p <.001) and 38 were eliminated because of excessive missing values or classification error rates (40% or greater on one or more of the IATs). Of the 183 remaining subjects, the age range of participants was 20-69, with an average age of 40. One hundred fifty-six participants identified as United States citizens. The racial/ethnic composition of the sample was: 3% American Indian or Alaska Native, 17% Asian, 7% Black or African American, 4% Hispanic or Latino, 66% non-hispanic White, and 3% two or more race/ethnicities; 52% of the individuals identified as female. Participants averaged 18 years of work experience, and 83% indicated they were presently employed. Descriptive statistics for the 183 participants on the study variables are displayed in Tables 3 and 4. From a psychometric perspective, the three internal consistency reliability coefficients for the IATs which hovered near.70 are of particular interest in that they are lower than expected. To test the construct validity hypotheses, CFA methods were used to compare different latent trait models (Widaman, 1985). This technique calls for comparing the hypothesized model (Model 1) to a series of three subsequent models that are each more restrictive (i.e., subsequent models have fewer latent factors or more constraints on the relationships among latent factors). Figure 1 displays the hypothesized model for this study (Model 1), which consists of three correlated latent trait factors for the three OCB motives and two correlated method factors (implicit and explicit). Figure 2 shows the second model (labeled Model 2), which is more restrictive in that it consists of only the two method factors and contains no motive factors. Model 3 contains perfectly correlated motive factors and two freely correlated method factors 8

9 (see Figure 3). Model 4 allows the motive factors to correlate freely, but requires the two method factors be uncorrelated (see Figure 4). If the Model 1 Model 2 comparison shows that Model 2 has poorer goodness-of-fit statistics than Model 1, then there is evidence of convergent validity due to the lack of any motive specification factors in Model 2. The Model 1 Model 3 comparison involves a model with freely correlated motive factors (Model 1) to a model in which the traits are required to be perfectly correlated (Model 3). The larger the difference in goodness-of-fit statistics, the greater the support for discriminant validity. However, because the three OCB motive factors are expected to be correlated, a large difference between these two models is not expected. The Model 1 Model 4 comparison utilizes the same logic as that used in the Model 1 Model 3 comparison, but in reverse. Thus, discriminant validity is shown through Model 1 and Model 4 not being significantly different. We do not expect the methods (implicit and explicit) to be correlated and a non-significant result would demonstrate little/no bias in the measurement of traits across methods. The results of the goodness-of-fit statistics for all four models are displayed in Table 5. The goodness-of-fit statistics for Model 1 indicate that the model represents an adequate fit for the variance-covariance structure of the data. The CFI value is above the.90 threshold for a poor fit that Bentler (1990) and others recommend and the RMSEA value is less than.10, which is the threshold for a poor fit that Byrne (2010), MacCallum, Browne and Sugawara (1996) and others recommend. The goodness-of-fit statistics for Model 2 were much worse than those for Model 1. Both the CFI and RMSEA values fell far beyond the generally accepted thresholds for adequate fit. While the goodness-of-fit statistics for Model 3 were not as poor as those for Model 2, they also fell beyond the generally accepted thresholds for adequate fit. Model 4 fit the variance- 9

10 covariance structure of the data essentially as well as Model 1, with only slightly poorer CFI and RMSEA values. In comparing the goodness-of-fit statistics for the models (see Table 6), the first comparison involved Model 1 (three motive factors) and Model 2 (no motive factors). The significant change in Chi Square and CFI values provides evidence of convergent validity for the hypothesized model. Although Model 3 (perfectly correlated motive factors) was less restrictive than Model 2, the comparison with Model 1 indicates a poorer fit and provides evidence of discriminant validity. Model 4 (uncorrelated method factors) was the least restrictive and, as predicted, produced fit statistics that were virtually identical with those of Model 1. This evidence supports the conclusion that there is little/no method bias across the implicit and explicit measures. The factor loadings for Model 1 are displayed in Table 7. These results indicate that most of the indicator variables had significant loadings on the hypothesized factors (17 out of the 22 loadings were significant), which provides further evidence supporting the construct validity of the hypothesized measurement model. To test the predictive validity hypotheses, the four IATs were regressed on each of the five OCB measures. This produced mixed results in that the R 2 values ranged from.026 (p<.30) to.046 (p<.05). Hierarchical regression analyses indicated that the implicit measures improved prediction for only one of the OCB measures (Altruism), over that obtained with the explicit measures. The R 2 values ranged from.016 (p<.50) to.033 (p<.05). DISCUSSION The purpose of this research was to develop psychometrically sound implicit measures of OCB motives and to investigate their predictive validity. Regarding the psychometric properties of the IATs, the internal consistency of three of the measures was marginal, while one was 10

11 adequate in terms of Nunnally s (1978) generally accepted standards. This suggests that contamination due to measurement error is a problem with these measures that future research should address. The comparison of CFA models provided tepid results regarding the construct validity of the measures. The three motive, two method measurement model did an adequate (but not good) job of capturing the variance-covariance structure of the MTMM data. The comparisons of this model with more restrictive models provided more evidence for the convergent and discriminant validity of the measures. However, two of the IATs did not have significant loadings on their corresponding motive factors. The results of the regression analyses investigating the predictive validity of the measures suggest the implicit measures have little to offer, over that which can be obtained with the explicit (self-report) measures. The word stimuli that the IATs used may help to explain some of these findings. Of the three IATs that target a single motive, only the Status IAT with its focus upon Impression Management loaded significantly on its latent trait factor. Because the composite (single target) IAT significantly loaded on the Organizational Commitment factor and not the Impression Management factor, the two word stimuli (Reputation and Money) chosen for the composite IAT may not have been as representative of Impression Management alone as they were in conjunction with the other word stimuli for the category (Prestige and Impressive). This result also suggests that the word stimuli for Organizational Commitment (Devoted and Loyal) are more representative of this construct alone than in conjunction with the other word stimuli (Motivated and Concerned) that were selected for the IAT that targets this motive. 11

12 According to Lane, et al (2007), stimuli that are categorized more easily and accurately will contribute less to error variance in IAT effects. Ambiguity about an item s appropriate categorization will slow reaction times and increase the number of classification errors, both of which will distort the IAT effect and increase measurement error. Future research should work to further develop and refine the OCB motive IATs. This work might begin by developing better alternatives for the word stimuli identified above (Reputation, Money, Motivated, and Concerned) and re-examining both the word stimuli and category label for Altruism, as this IAT did not have a significant loading on its trait factor. Regarding the weak evidence in support of the incremental predictive validity hypothesis, the nature of the criterion measures may help explain some of these results. The self-reported OCB conduct measures are susceptible to contamination from the same impression management artifacts as the self-reported OCB motive measures. Or, to put it a little differently, we expect that there is considerable method bias among the explicit measures of the OCB motives and OCB conduct. Indeed, in a CFA that included the five OCB conduct measures as indicator variables, in addition to the 10 IAT and explicit motive measures, Model 1 (i.e., three motive factors and two method factors) resulted in goodness-of-fit statistics that are very similar to those reported above (see Table 5). Furthermore, all five of the criterion measures had large, significant loadings on the explicit method factor in this analysis, as did the explicit OCB motive measures. This underscores the likelihood of method bias in the regression analysis and suggests that the incremental validity hypothesis warrants further attention. Once the IATs are refined, future research should develop additional criterion measures that reflect other methods (e.g., peer/coworker reports, supervisor reports, objective recordings, etc.). Such data would allow for a more complete MTMM analysis in accord with Campbell and Fiske s (1959) classic strategy. 12

13 References Back, M. D., Schmukle, S. C., & Egloff, B. (2009). Predicting actual behavior from the explicit and implicit self-concept of personality. Journal of Personality and Social Psychology, 97(3), Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), Borman, W. C., & Penner, L. A. (2001) Citizenship Performance: Its Nature, Antecedents, and Motives. In B. W. Roberts, & R. Hogan (Eds), Personality Psychology in the Workplace. (pp ). Washington, DC: American Psychological Association. Byrne, B. M. (2010). Structural Equation Modeling with AMOS, Second Edition. New York: Routledge. Campbell, D. T. & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, Costa, P. T. & McCrae, R. R. (1992). NEO PI-R professional manual. Odessa, FL: Psychological Assessment Resources, Inc. Fazio, R. & Olson, M. (2003). Implicit Measures in Social Cognition Research. Annual Review of Psychology, 54, Greenwald, A., Banaji, M., Rudman, L., Farnham, S., Nosek, B, & Mellot, D. (2002). A Unified Theory of Implicit Attitudes, Stereotypes, Self-Esteem, and Self-Concept. Psychological Review, 109(1), Greenwald, A. G., McGhee, D. E., & Schwartz, J. K. L. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, Greenwald, A. G., Nosek, B. & Banaji, M. (2003). Understanding and using the Implicit Association Test: I. An improved scoring algorithm. Journal of Personality and Social Psychology, 85(2), Karpinski, A. & Steinman, R. B. (2006). The single category implicit association test as a measure of implicit social cognition. Journal of Personality and Social Psychology, 91(1), Katz, D. (1964). The motivational basis of organizational behavior. Behavioral Science, 9,

14 Lane, K. A., Banaji, M. R., Nosek, B. A., & Greenwald, A. G. (2007) Understanding and Using the Implicit Association Test: IV. In B. Wittenbrink & N. Schwarz (Eds.), Implicit measures of attitudes. (pp ). New York: The Guildford Press. MacCallum, R. C., Browne, M. W., & Sugawara, H., M. (1996), "Power Analysis and Determination of Sample Size for Covariance Structure Modeling," Psychological Methods, 1(2), McClelland, D. C. (1958). Methods of measuring human motivation. In J. W. Atkinson (Ed.), Motives in fantasy, action, and society. Princeton, NJ: Van Nostrand. McClelland, D. C., Atkinson, J. W., Clark, R. A., & Lowell, E. L. (1953) The achievement motive. New York: Irvington Publishers. Murray, H. A. (1938). Explorations in personality. New York: Oxford University press. Nunnally, J. C. (1978). Psychometric theory (2 nd ed.). New York: McGraw-Hill. Organ, D. W. (1988). Organizational citizenship behavior: The good soldier syndrome. Lexington, MA: Lexington Books. Podsakoff, P. M., MacKenzie, J. B., Moorman, R. H., & Fetter, R. (1990). Transformational leader behaviors and their followers trust in leader, satisfaction, and organizational citizenship behaviors. The Leadership Quarterly, 1(2), Rioux, S., & Penner, L. A. (2001). The causes of organizational citizenship behavior: A motivational analysis. Journal of Applied Psychology, 86(6), Schnabel, K., Asendorpf, J. B., & Greenwald, A. G. (2008). Understanding and using the implicit association test: V. Measuring semantic aspects of trait self-concepts. European Journal of Personality, 22, Takeuchi, R., Bolino, M. C., & Lin, C. (2014). Too many motives? The interactive effects of multiple motives on organizational citizenship behavior. Journal of Applied Psychology, 100(4), Vecchione, M., Dentale, F., Alessandri, G., & Barbaranelli, C. (2014). Fakability of implicit and explicit measures of the Big Five: Research findings from organizational settings. International Journal of Selection and Assessment, 22(2), Werner, J. M. (1994) Dimensions that make a difference: Examining the impact of in-role and extra-role behaviors on supervisory ratings. Journal of Applied Psychology, 79, Widaman, K. F, (1985). Hierarchically nested covariance structure models for multi-trait multimethod data. Applied Psychological Measurement,

15 Figure 1. CFA Model 1: Three freely correlated motive factors and two freely correlated method factors. 15

16 Figure 2. CFA Model 2: Two freely correlated method factors and no motive factors. 16

17 Figure 3. CFA Model 3: Two freely correlated method factors and perfectly correlated motive factors. 17

18 Model 4. CFA Model 4: Three freely correlated motive factors and two uncorrelated method factors. 18

19 Table 1. Category Labels and Word Stimuli for the Standard Single Motive IATs Status Altruism Commitment Good Bad Reputation Helpful Devoted Marvelous Tragic Money Empathic Loyal Superb Horrible Prestige Generous Motivated Glorious Terrible Impressive Friendly Concerned Wonderful Awful Table 2. Category Labels and Word Stimuli for the Composite Motive Single-Target IAT Work Motive Good Bad Reputation Marvelous Tragic Money Superb Horrible Helpful Glorious Terrible Empathic Wonderful Awful Devoted Lovely Humiliate Loyal Splendid Disaster 19

20 Table 3. Descriptive Statistics for Study Variables (N = 183) Variables Min Max Mean SD Alpha Implicit Measures Status IAT* Commitment IAT** Altruism IAT*** Comp Motive IAT**** Explicit Measures Org Concern Prosocial Values Impression Manage NEO-N4 Self-Consc NEO-A3 Altruism NEO-C3 Dutifulness Criterion Measures OCB-Conscientious OCB-Sportsmanship OCB-CivicVirtue OCB-Courtesy OCB-Altruism *Positive IAT values indicate that the individual is more strongly associated with Status, negative values reflect a stronger association with Altruism; **Positive IAT values indicate that the individual is more strongly associated with Commitment, negative values reflect a stronger association with Status; ***Positive IAT values indicate that the individual is more strongly associated with Altruism, negative values indication that the individual is more strongly associated with Commitment; ****Positive values indicate that individuals associate with all of the motivation components. 20

21 Table 4. Zero-Order Correlations for Study Variables Variables Implicit Measures 1. Status IAT - 2. Commitment IAT -.52 ** - 3. Altruism IAT -.16 * Comp Motive IAT -.18 * Explicit Measures 5. Org Concern.16 * Prosocial Values ** - 7. Impression Mgt.28 ** -.25 ** ** NEO-N4 Self-Con ** - 9. NEO-A3 Altruism **.57 ** ** NEO-C3 Dutiful * **.30 ** *.40 ** - Criterion Measures 11. OCB-Conscient **.36 ** *.50 **.65 * OCB-Sportsman -.22 **.19 ** **.50 **.36 **.40 **.50 ** OCB-CivicVirtu ** **.37 ** **.33 ** ** OCB-Courtesy **.48 ** **.33 **.47 **.61 **.33 **.47 ** OCB-Altruism * **.64 ** **.25 **.57 **.66 **.25 **.57 **.66 ** *p <.05; ** p <.01 21

22 Table 5. Summary of Goodness-of-Fit Statistics for Nested Model Comparisons Model x 2 df CFI RMSEA 90%C.I. 1. Freely correlated traits; ,.124 freely correlated methods 2. No traits; freely correlated methods 3. Perfectly correlated traits; freely correlated methods 4. Freely correlated traits; uncorrelated methods , , ,.124 Table 6. Differential Goodness-of-Fit Statistics for Nested Model Comparisons Model Comparisons χ2 df CFI Test of Convergent Validity Model 1 vs. Model * Tests of Discriminant Validity Model 1 vs. Model * Model 1 vs. Model * p <.01 22

23 Table 7. Trait and Method Loadings for CFA Model 1 Variables Org Concern Prosocial Values Impression Managemnt Implicit Explicit Implicit Measures Status IAT.186 **.719 ** Commitment IAT ** Altruism IAT Comp Motive IAT.236 * -.193* * Explicit Measures Org Concern **.278 ** NEO-C3 Dutiful ** Prosocial Values **.248 ** NEO-A3 Altruism **.598 ** Impression Manage.948 ** * NEO-N4 Self-Con.243 ** ** * p <.05, ** p <.01 23