Academic literature on the topic 'Multitrait-multimethod matrix'

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Journal articles on the topic "Multitrait-multimethod matrix"

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Eid, Michael, and Fridtjof W. Nussbeck. "The Multitrait-Multimethod Matrix at 50!" Methodology 5, no. 3 (January 2009): 71. http://dx.doi.org/10.1027/1614-2241.5.3.71.

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Meyer, Joseph F., Randy O. Frost, Timothy A. Brown, Gail Steketee, and David F. Tolin. "A multitrait-multimethod matrix investigation of hoarding." Journal of Obsessive-Compulsive and Related Disorders 2, no. 3 (July 2013): 273–80. http://dx.doi.org/10.1016/j.jocrd.2013.03.002.

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Maas, Cora J. M., Gerty J. L. M. Lensvelt-Mulders, and Joop J. Hox. "A Multilevel Multitrait-Multimethod Analysis." Methodology 5, no. 3 (January 2009): 72–77. http://dx.doi.org/10.1027/1614-2241.5.3.72.

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The classical multitrait-multimethod (MTMM) matrix can be viewed as a two-dimensional cross-classification of traits and methods. Beside commonly used analysis methods such as structural equation modeling and generalizability theory, multilevel analysis offers attractive possibilities. If the focus is only on analyzing classical MTMM data, the multilevel approach has no surplus value, because the resulting model is equivalent to a confirmatory factor model with additional restrictions imposed by the multilevel parameterization. However, if the data contain further complexities, such as additional information on the traits or persons, multilevel analysis of MTMM data offers a flexible analysis tool with more possibilities than the other approaches.
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Ginsberg, Pauline E. "Cross-Cultural Research, Ethnography and the Multitrait-Multimethod Matrix." American Journal of Evaluation 19, no. 3 (September 1998): 411–15. http://dx.doi.org/10.1177/109821409801900317.

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Ginsberg, P. "Cross-cultural research, ethnography and the multitrait-multimethod matrix." American Journal of Evaluation 19, no. 3 (1998): 411–15. http://dx.doi.org/10.1016/s1098-2140(99)80226-4.

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Sinha, Birendra K., and David C. Watson. "Personality Disorder in University Students: A Multitrait-Multimethod Matrix Study." Journal of Personality Disorders 15, no. 3 (June 2001): 235–44. http://dx.doi.org/10.1521/pedi.15.3.235.19205.

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Kenny, David A., and Deborah A. Kashy. "Analysis of the multitrait-multimethod matrix by confirmatory factor analysis." Psychological Bulletin 112, no. 1 (July 1992): 165–72. http://dx.doi.org/10.1037/0033-2909.112.1.165.

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Mearns, Jack, Eryn Patchett, and Salvatore J. Catanzaro. "Multitrait–multimethod matrix validation of the Negative Mood Regulation Scale." Journal of Research in Personality 43, no. 5 (October 2009): 910–13. http://dx.doi.org/10.1016/j.jrp.2009.05.003.

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Reichardt, Charles S., and S. C. Coleman. "The Criteria for Convergent and Discriminant Validity in a Multitrait-Multimethod Matrix." Multivariate Behavioral Research 30, no. 4 (October 1995): 513–38. http://dx.doi.org/10.1207/s15327906mbr3004_3.

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Brannick, Michael T., and Paul E. Spector. "Estimation Problems in the Block-Diagonal Model of the Multitrait-Multimethod Matrix." Applied Psychological Measurement 14, no. 4 (December 1990): 325–39. http://dx.doi.org/10.1177/014662169001400401.

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Dissertations / Theses on the topic "Multitrait-multimethod matrix"

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Spangler, Tamara L. "Anxious solitude, unsociability, and peer exclusion in middle childhood a multitrait-multimethod matrix /." Greensboro, N.C. : University of North Carolina at Greensboro, 2007. http://libres.uncg.edu/edocs/etd/1456Spangler/umi-uncg-1456.pdf.

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Thesis (M.A.)--University of North Carolina at Greensboro, 2007.
Title from PDF t.p. (viewed Feb. 28, 2008). Directed by Heidi Gazelle; submitted to the Dept. of Psychology. Includes bibliographical references (p. 41-47).
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Phillips, Joshua Parker. "CONSTRUCT VALIDITY OF A LABORATORY AGGRESSION PARADIGM: A MULTITRAIT-MULTIMETHOD APPROACH." UKnowledge, 2011. http://uknowledge.uky.edu/gradschool_diss/131.

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There continues to be doubt regarding the validity of laboratory aggression paradigms. This paper provides an investigation of the construct validity of one prominent aggression task, the Taylor Aggression Paradigm (TAP), within a Multitrait Multimethod Matrix (MTMM) methodology. Participants consisted of 151 male undergraduate psychology students with a median age of 19 years old (M=19.45, SD = 2.03). Participants completed self-report and behavioral measures of aggression, impulsivity, and pro-social behavior which were analyzed using a Correlated Trait – Correlated Method Confirmatory Factor Analysis model. Results supported the construct validity of the MTMM model and the TAP. This study provides one of the only a priori tests of construct validity for the TAP and provides a basis for additional validation studies using this methodology.
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Jones, Catherine Toni, and n/a. "Biggs's 3P Model of Learning: The Role of Personal Characteristics and Environmental Influences on Approaches to Learning." Griffith University. School of Applied Psychology, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030304.092316.

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The aim of this research programme was to examine the 3P model of learning (Biggs, 1987a, 1999). The first stage necessarily involved an examination of the Study Process Questionnaire (SPQ) (Biggs, 1987a), an instrument developed to measure the process component of the model. The structure of the SPQ was examined utilising exploratory and confirmatory factor analysis of undergraduate responses (n= 260). The results indicated the higher-order factor structure of deep-achieving and surface-achieving-motive provided the most reliability and a better model fit than either the subscales or scales of the SPQ. The construct validity of the two constructs deep and surface was assessed next using a multitrait-multimethod matrix (MTMM) constructed from the three measures of the self-report questionnaire, interview ratings and written assessments from first-year students (n = 50). The results indicated good convergent validity between the deep scale of the SPQ and the interview ratings on the deep scale, between the deep scale on the SPQ and the written assessment ratings, and between the interview ratings and written assessment ratings. The results indicated good convergent validity between the surface scale on the SPQ and the interview ratings on the surface scale, but not between the surface scale on the SPQ and the written assessment ratings, and between the interview ratings and written assessment ratings. The discriminant validity between deep and surface was good for the SPQ, but not for either the interview or the written assessment. The findings indicate the deep and surface scales of the SPQ adequately measure the underlying deep and surface constructs. The retest reliability of the SPQ was then examined utilising Spearman’s Rho to assess the rank-order correlations with a sample of third-year students (n=87). Over a period of three months there were significant correlations for the surface motive, surface strategy, deep strategy, achieving motive and achieving strategy subscales of the SPQ, suggesting good reliability for these subscales. The results at the scale level of the SPQ result in similar conclusions. There was a moderate significant correlation for the surface, deep and achieving scales of the SPQ, suggesting the scales have good reliability over a period of three months. There was also a moderate significant correlation for the surface-achieving-motive and deep-achieving scales over a period of three months. The stability of SPQ scores was also assessed utilising a series of one-way repeated measures MANOVA’s with a sample of third-year undergraduates (n = 64). The results suggest some change occurs in self-reported use of approaches to learning between the first and third-years of an undergraduate degree programme. The role of the teaching-learning environment was next examined. Utilising a within-subjects design, undergraduate students (n=48) concurrently enrolled in traditional (viz. lecture and tutorial) and non-traditional (viz. workshops and group projects) subjects completed the SPQ to describe their approaches to learning in each subject. A series of 2x2 repeated measures MANOVA’s were undertaken. The results indicated students were likely to change their approach to learning based on their perceptions of the learning environment (traditional or non-traditional subject). However, those students identified as predominantly surface learners significantly increased their deep scale scores in the non-traditional subject when compared to deep learners. The next study examined a range of personality (locus of control, sensing function, thinking function, intelligence) and demographic variables (age, gender, year of study) to assess which were good predictors of deep and surface approaches to learning. A series of regression analyses identified age, sensing function and locus of control as significant predictors of the surface, surface-achieving-motive, and deep approaches to learning. Locus of control was found to be a significant predictor of the deep-achieving approach to learning. The final study examined the 3P model of learning. Based on the results of earlier studies in the research programme the situational component of the presage factors was not included. The model was examined using structural equation modelling (n= 394). Two initial models were tested using both the three (deep, surface, achieving) and two (surface-achieving-motive and deep-achieving) process factor models. The three process factor model provided the better model fit. The results suggest deep and surface approaches to learning do not mediate between personal characteristics and learning outcomes (i.e. GPA). The results of this series of studies suggest the need for further research into the SPQ and the 3P model of learning. The implications of the research programme are also discussed.
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Jones, Catherine Toni. "Biggs's 3P Model of Learning: The Role of Personal Characteristics and Environmental Influences on Approaches to Learning." Thesis, Griffith University, 2003. http://hdl.handle.net/10072/366357.

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Abstract:
The aim of this research programme was to examine the 3P model of learning (Biggs, 1987a, 1999). The first stage necessarily involved an examination of the Study Process Questionnaire (SPQ) (Biggs, 1987a), an instrument developed to measure the process component of the model. The structure of the SPQ was examined utilising exploratory and confirmatory factor analysis of undergraduate responses (n= 260). The results indicated the higher-order factor structure of deep-achieving and surface-achieving-motive provided the most reliability and a better model fit than either the subscales or scales of the SPQ. The construct validity of the two constructs deep and surface was assessed next using a multitrait-multimethod matrix (MTMM) constructed from the three measures of the self-report questionnaire, interview ratings and written assessments from first-year students (n = 50). The results indicated good convergent validity between the deep scale of the SPQ and the interview ratings on the deep scale, between the deep scale on the SPQ and the written assessment ratings, and between the interview ratings and written assessment ratings. The results indicated good convergent validity between the surface scale on the SPQ and the interview ratings on the surface scale, but not between the surface scale on the SPQ and the written assessment ratings, and between the interview ratings and written assessment ratings. The discriminant validity between deep and surface was good for the SPQ, but not for either the interview or the written assessment. The findings indicate the deep and surface scales of the SPQ adequately measure the underlying deep and surface constructs. The retest reliability of the SPQ was then examined utilising Spearman’s Rho to assess the rank-order correlations with a sample of third-year students (n=87). Over a period of three months there were significant correlations for the surface motive, surface strategy, deep strategy, achieving motive and achieving strategy subscales of the SPQ, suggesting good reliability for these subscales. The results at the scale level of the SPQ result in similar conclusions. There was a moderate significant correlation for the surface, deep and achieving scales of the SPQ, suggesting the scales have good reliability over a period of three months. There was also a moderate significant correlation for the surface-achieving-motive and deep-achieving scales over a period of three months. The stability of SPQ scores was also assessed utilising a series of one-way repeated measures MANOVA’s with a sample of third-year undergraduates (n = 64). The results suggest some change occurs in self-reported use of approaches to learning between the first and third-years of an undergraduate degree programme. The role of the teaching-learning environment was next examined. Utilising a within-subjects design, undergraduate students (n=48) concurrently enrolled in traditional (viz. lecture and tutorial) and non-traditional (viz. workshops and group projects) subjects completed the SPQ to describe their approaches to learning in each subject. A series of 2x2 repeated measures MANOVA’s were undertaken. The results indicated students were likely to change their approach to learning based on their perceptions of the learning environment (traditional or non-traditional subject). However, those students identified as predominantly surface learners significantly increased their deep scale scores in the non-traditional subject when compared to deep learners. The next study examined a range of personality (locus of control, sensing function, thinking function, intelligence) and demographic variables (age, gender, year of study) to assess which were good predictors of deep and surface approaches to learning. A series of regression analyses identified age, sensing function and locus of control as significant predictors of the surface, surface-achieving-motive, and deep approaches to learning. Locus of control was found to be a significant predictor of the deep-achieving approach to learning. The final study examined the 3P model of learning. Based on the results of earlier studies in the research programme the situational component of the presage factors was not included. The model was examined using structural equation modelling (n= 394). Two initial models were tested using both the three (deep, surface, achieving) and two (surface-achieving-motive and deep-achieving) process factor models. The three process factor model provided the better model fit. The results suggest deep and surface approaches to learning do not mediate between personal characteristics and learning outcomes (i.e. GPA). The results of this series of studies suggest the need for further research into the SPQ and the 3P model of learning. The implications of the research programme are also discussed.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Applied Psychology
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Books on the topic "Multitrait-multimethod matrix"

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Behnke, Rita Southwood. SELIGMAN'S LEARNED HELPLESSNESS: ASSESSMENT OF CONSTRUCT VALIDITY BY A MULTITRAIT-MULTIMETHOD MATRIX. 1986.

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Book chapters on the topic "Multitrait-multimethod matrix"

1

Kenny, David A. "Multitrait-multimethod matrix." In Advanced Multitrait-Multimethod Analyses for the Behavioral and Social Sciences, 16–27. London: Routledge, 2021. http://dx.doi.org/10.4324/9780429320989-2.

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Wothke, Werner. "Covariance Components Analysis of the Multitrait-Multimethod Matrix." In Personality Research, Methods, and Theory, 125–44. Psychology Press, 2014. http://dx.doi.org/10.4324/9781315806815-9.

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Kenny, David A. "The Multitrait-Multimethod Matrix: Design, Analysis, and Conceptual Issues." In Personality Research, Methods, and Theory, 111–24. Psychology Press, 2014. http://dx.doi.org/10.4324/9781315806815-8.

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