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Journal articles on the topic 'Multilevel Confirmatory Factor Analysi'

Author: Grafiati

Published: 11 March 2023

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1

Ryu, Ehri. "Factorial invariance in multilevel confirmatory factor analysis." British Journal of Mathematical and Statistical Psychology 67, no. 1 (May 18, 2013): 172–94. http://dx.doi.org/10.1111/bmsp.12014.

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2

Dyer, Naomi, William P. Sipe, and Paul J. Hanges. "MULTILEVEL CONFIRMATORY FACTOR ANALYSIS: DEMONSTRATION OF MUTHEN'S TECHNIQUE." Academy of Management Proceedings 1997, no. 1 (August 1997): 391–94. http://dx.doi.org/10.5465/ambpp.1997.4989260.

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3

Can, Seda, Rens van de Schoot, and Joop Hox. "Collinear Latent Variables in Multilevel Confirmatory Factor Analysis." Educational and Psychological Measurement 75, no. 3 (August 29, 2014): 406–27. http://dx.doi.org/10.1177/0013164414547959.

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4

Geldhof, G. John, Kristopher J. Preacher, and Michael J. Zyphur. "Reliability estimation in a multilevel confirmatory factor analysis framework." Psychological Methods 19, no. 1 (March 2014): 72–91. http://dx.doi.org/10.1037/a0032138.

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5

Gajewski, Byron J., Diane K. Boyle, Peggy A. Miller, Frances Oberhelman, and Nancy Dunton. "A Multilevel Confirmatory Factor Analysis of the Practice Environment Scale." Nursing Research 59, no. 2 (March 2010): 147–53. http://dx.doi.org/10.1097/nnr.0b013e3181d1a71e.

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6

Little, Jonathon. "Multilevel confirmatory ordinal factor analysis of the Life Skills Profile–16." Psychological Assessment 25, no. 3 (September 2013): 810–25. http://dx.doi.org/10.1037/a0032574.

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7

Dyer, Naomi G., Paul J. Hanges, and Rosalie J. Hall. "Applying multilevel confirmatory factor analysis techniques to the study of leadership." Leadership Quarterly 16, no. 1 (February 2005): 149–67. http://dx.doi.org/10.1016/j.leaqua.2004.09.009.

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8

Siriparp, Thomrat, Duangkamol Traiwichitkhun, and Sirichai Kanjanawasee. "Using Multilevel Confirmatory Factor Analysis To Study Student Well-Being In Thailand." Journal of International Education Research (JIER) 8, no. 4 (September 20, 2012): 367–72. http://dx.doi.org/10.19030/jier.v8i4.7283.

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Multilevel confirmatory factor analysis was used to assess the factor structure of the six-factor student well-being scale at between- and within-class levels. A total of 2,707 Matthayom 4-6 students (equivalent to grades 10-12) from 71 classrooms (785 male and 1,922 female) completed this 54-items student well-being scale. Results showed that a single latent factor structure was acceptable fitted at between and within class levels. The reliability estimates of the six factors (Positive attitudes and emotions towards school, Enjoyment in school, Positive academic self-concept, (Absence of) Social problems in school, (Absence of) School worries, and (Absence of) Physical complaints) were 0.73, 0.38, 0.22, 0.13, 0.14, and 0.02, respectively, at the student level, whereas these estimates were 0.39, 0.44, 0.48, 0.61, 0.65, and 0.82, respectively, at the classroom level.

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Byman, Reijo, Jari Lavonen, Kalle Juuti, and Veijo Meisalo. "MOTIVATIONAL ORIENTATIONS IN PHYSICS LEARNING: A SELF-DETERMINATION THEORY APPROACH." Journal of Baltic Science Education 11, no. 4 (December 5, 2012): 379–92. http://dx.doi.org/10.33225/jbse/12.11.379.

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The first objective of the present study was to test the factorial validity of the inventory designed to measure students’ motivation to study physics in school. Based on self-determination theory (SDT), it was hypothesised that the four factors of External Regulation, Introjected Regulation, Identified Regulation and Intrinsic Motivation account for the covariances among the items of the inventory. It was also expected that gender moderates the factorial validity of the inventory. A sample of 3626 Finnish comprehensive school students was used. A multilevel confirmatory factor analysis (MLCFA) was used to investigate the fit of the hypothesised four-factor model. The second objective of the present study was to investigate different educational correlates of the four motivation factors. Girls had a statistically higher mean score than did boys for all four factors. Otherwise only minor gender differences were found. Implications for physics teaching are discussed. Key words: motivational orientations, multilevel confirmatory factor analysis, science learning, self-determination theory.

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10

Kyriazos, Theodoros A. "Applied Psychometrics: The Modeling Possibilities of Multilevel Confirmatory Factor Analysis (MLV CFA)." Psychology 10, no. 06 (2019): 777–98. http://dx.doi.org/10.4236/psych.2019.106051.

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11

Villares, Elizabeth, Melissa Mariani, Christopher A. Sink, and Kimberly Colvin. "Multilevel Confirmatory Factor Analysis of the Teacher My Class Inventory–Short Form." Measurement and Evaluation in Counseling and Development 49, no. 4 (October 2016): 263–73. http://dx.doi.org/10.1177/0748175616639107.

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12

Rice, Kenneth G., Emily N. Srisarajivakul, Joel Meyers, and Kris Varjas. "Multilevel confirmatory factor analysis of the Effective Behavior Support Self-Assessment Survey." School Psychology 34, no. 3 (May 2019): 318–27. http://dx.doi.org/10.1037/spq0000295.

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13

Greenbaum, Paul E., Wei Wang, Roger Boothroyd, Krista Kutash, and Robert M. Friedman. "Multilevel Confirmatory Factor Analysis of the Systems of Care Implementation Survey (SOCIS)." Journal of Behavioral Health Services & Research 38, no. 3 (May 4, 2011): 303–26. http://dx.doi.org/10.1007/s11414-011-9240-4.

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14

Ahn, Inok, Helen Patrick, Ming Ming Chiu, and Chantal Levesque-Bristol. "Measuring Teacher Practices That Support Student Motivation: Examining the Factor Structure of the Teacher as Social Context Questionnaire Using Multilevel Factor Analyses." Journal of Psychoeducational Assessment 37, no. 6 (July 30, 2018): 743–56. http://dx.doi.org/10.1177/0734282918791655.

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We used multilevel factor analyses to investigate the structure of the Teacher as Social Context Questionnaire (TASCQ)-short form—a prominent measure of teacher practices that promote student motivation to learn. Based on Self-Determination Theory (SDT), the TASCQ contains three scales: Autonomy Support, Structure, and Involvement. Few studies have tested the construct validities of these three scales together. Furthermore, the few studies using factor analyses with these constructs’ scales showed mixed results. Moreover, none of those studies properly modeled the clustered nature of data with a multilevel analysis. We examined the structure of TASCQ scores from 697 fifth and sixth graders in 35 classes by conducting multilevel exploratory and multilevel confirmatory factor analysis, each with half the sample. Results indicated that students did not distinguish among Autonomy Support, Structure, and Involvement items; the best fit was a single factor.

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15

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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Bakker, Arnold B., Ana I. Sanz-Vergel, Alfredo Rodríguez-Muñoz, and Wido G. M. Oerlemans. "The state version of the recovery experience questionnaire: A multilevel confirmatory factor analysis." European Journal of Work and Organizational Psychology 24, no. 3 (May 8, 2014): 350–59. http://dx.doi.org/10.1080/1359432x.2014.903242.

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17

Zhang, Ning Jackie, and Thomas T. H. Wan. "The Measurement of Nursing Home Quality: Multilevel Confirmatory Factor Analysis of Panel Data." Journal of Medical Systems 29, no. 4 (August 2005): 401–11. http://dx.doi.org/10.1007/s10916-005-5898-6.

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18

D’Haenens, Ellen, Jan Van Damme, and Patrick Onghena. "Constructing measures for school process variables: the potential of multilevel confirmatory factor analysis." Quality & Quantity 46, no. 1 (June 23, 2010): 155–88. http://dx.doi.org/10.1007/s11135-010-9346-4.

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D'Haenens, Ellen, Jan Van Damme, and Patrick Onghena. "Linking student outcome variables with school process variables: multilevel confirmatory factor analysis takes precedence." Effective Education 2, no. 2 (September 2010): 117–42. http://dx.doi.org/10.1080/19415532.2010.522796.

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20

Son, Sookyung, Hyojin Kim, and Sehee Hong. "Measurement Invariance Testing Using Random Effects model for Many groups: Multilevel Confirmatory Factor Analysis (ML CFA) and Multilevel Factor Mixture Modeling (ML FMM)." Korean Journal of Psychology: General 38, no. 2 (June 25, 2019): 185–218. http://dx.doi.org/10.22257/kjp.2019.6.38.2.185.

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21

Dedrick, Robert F., and Paul E. Greenbaum. "Multilevel Confirmatory Factor Analysis of a Scale Measuring Interagency Collaboration of Children’s Mental Health Agencies." Journal of Emotional and Behavioral Disorders 19, no. 1 (June 2010): 27–40. http://dx.doi.org/10.1177/1063426610365879.

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22

Brondino, Margherita, Margherita Pasini, and Silvia Costa Agostinho da Silva. "Development and validation of an Integrated Organizational Safety Climate Questionnaire with multilevel confirmatory factor analysis." Quality & Quantity 47, no. 4 (December 21, 2011): 2191–223. http://dx.doi.org/10.1007/s11135-011-9651-6.

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23

Breevaart, Kimberley, Arnold B. Bakker, Evangelia Demerouti, and Jörn Hetland. "The Measurement of State Work Engagement." European Journal of Psychological Assessment 28, no. 4 (January 1, 2012): 305–12. http://dx.doi.org/10.1027/1015-5759/a000111.

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While diary studies have gained in popularity, the validity of the measures utilized in such studies remains an underresearched issue. This study examines the factor structure of the Utrecht Work Engagement Scale (UWES) on both between-person (trait) and within-person (state) levels. A multilevel confirmatory factor analysis was performed to confirm that the between-level factor structure also operates on the within-level. Data from 271 employees who filled in a state version of the UWES on five consecutive days were used to perform the analysis. Results showed that the UWES can be used to measure both trait and state work engagement. The three-factor multilevel model appeared to best fit the data. Implications for future research on engagement are discussed.

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24

Son, Sookyoung, and Sehee Hong. "Multiple Group Analysis in Multilevel Data Across Within-Level Groups: A Comparison of Multilevel Factor Mixture Modeling and Multilevel Multiple-Indicators Multiple-Causes Modeling." Educational and Psychological Measurement 81, no. 5 (January 19, 2021): 904–35. http://dx.doi.org/10.1177/0013164420987899.

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The purpose of this two-part study is to evaluate methods for multiple group analysis when the comparison group is at the within level with multilevel data, using a multilevel factor mixture model (ML FMM) and a multilevel multiple-indicators multiple-causes (ML MIMIC) model. The performance of these methods was evaluated integrally by a series of procedures testing weak and strong invariance models and the latent group mean differences testing after holding for factorial invariance. Two Monte Carlo simulation studies were conducted under the following conditions: number of clusters, cluster size, and the design type in groups. A multilevel one-factor confirmatory factor analysis (CFA) model as a research model in Study 1 was investigated to compare the results under different conditions with those of previous studies. A multilevel two-factor CFA model as a research model in Study 2 was evaluated by fitting alternative models that can be applied when the model is complicated. The results indicated that the two methods were reasonable in multilevel multiple groups analysis across within-level groups. However, pros and cons were found between the two methods. In the multilevel one-factor CFA model, ML MIMIC model was slightly better when the sample size is small. In the multilevel complex model, two alternative models of ML FMM were recommended because the weak invariance testing of ML MIMIC was considerably time-consuming. Finally, it was shown that information criteria, which are criteria for determining whether factorial invariance is established, need to be applied differently according to the sample size conditions. Guidelines for this situation are provided.

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25

Long, Shelby K., Tetsuya Sato, Nick Millner, Ray Loranger, Julianna Mirabelli, Violet Xu, and Yusuke Yamani. "Empirically And Theoretically Driven Scales On Automation Trust: A Multi-Level Confirmatory Factor Analysis." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 1829–32. http://dx.doi.org/10.1177/1071181320641440.

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Automation is used to complete a variety of everyday and professional tasks. Trust has been shown to be a critical factor that contributes to successful human-automation interaction. Modern theories of automation trust adapted theories of interpersonal trust and have been tested in a variety of domains. Specifically, a triadic model of trust, with performance, process, and purpose as factors, has emerged. From this theory, Chancey et al. (2017) adapted Madsen and Gregor’s (2000) trust scale to align with Lee and See’s (2004) trust framework. Conversely, Jian et al. (2000) developed a scale empirically with trust and distrust as factors. This study aims to use questionnaire data from previous experiments to explore the relationship between the empirically driven Jian et al. trust scale and the theoretically driven Chancey et al. trust scale. We will perform a multilevel confirmatory factor analysis (CFA) to test the distinctiveness of the two trust measures, as well as their structures and the correlation between the measures. The findings of this work will help researchers understand the relationship between the two trust scales, assess if Jian et al.’s scale contains a three-factor structure, and provide more information about the psychological structure of automation trust.

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26

Gruszczynska, Ewa, Beata A. Basinska, and Wilmar B. Schaufeli. "Within- and between-person factor structure of the Oldenburg Burnout Inventory: Analysis of a diary study using multilevel confirmatory factor analysis." PLOS ONE 16, no. 5 (May 14, 2021): e0251257. http://dx.doi.org/10.1371/journal.pone.0251257.

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The study examined the factor structure of burnout, as measured with the Oldenburg Burnout Inventory. The participants were 235 employees of a public administration agency who assessed their burnout online for 10 consecutive working days. Two models were tested with multilevel confirmatory factor analysis, assuming the same one or two-factor structure at the within- and between-person levels. Both models showed a reasonable fit to the data, but due to a strong correlation between exhaustion and disengagement and low within-person reliability for disengagement, a unidimensional model seems more valid. A cross-level invariance was not confirmed for either of the structures, showing that factor loadings for the same items differ significantly between the levels. This suggests that burnout is not the same latent variable at each level; rather, there are factors other than daily burnout that influence person-level scores and ignoring these across-level discrepancies may lead to biased conclusions.

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27

Ulitzsch, Esther, Jana Holtmann, Martin Schultze, and Michael Eid. "Comparing Multilevel and Classical Confirmatory Factor Analysis Parameterizations of Multirater Data: A Monte Carlo Simulation Study." Structural Equation Modeling: A Multidisciplinary Journal 24, no. 1 (December 7, 2016): 80–103. http://dx.doi.org/10.1080/10705511.2016.1251846.

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28

Li, Fuzhong, Terry E. Duncan, Peter Harmer, Alan Acock, and Mike Stoolmiller. "Analyzing measurement models of latent variables through multilevel confirmatory factor analysis and hierarchical linear modeling approaches." Structural Equation Modeling: A Multidisciplinary Journal 5, no. 3 (January 1998): 294–306. http://dx.doi.org/10.1080/10705519809540106.

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29

Margola, Davide, Valentina Fenaroli, Angela Sorgente, Margherita Lanz, and Giulio Costa. "The Family Relationships Index (FRI)." European Journal of Psychological Assessment 35, no. 3 (May 2019): 335–45. http://dx.doi.org/10.1027/1015-5759/a000427.

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Abstract. Factor analysis of nested data is a challenge for researchers when they need to accurately identify the most appropriate latent configuration of self-report instruments. The present study used a multilevel framework to evaluate the factor structure underlying the 12-item three-factor Family Relationships Index (FRI), while adapting this instrument to the Italian context. By way of separating the two sources of variance (within and between families), results from 231 family members nested in 77 family triads supported a three-factor model (i.e., family cohesion, communication, and conflict resolution). Multilevel confirmatory factor analysis (MCFA) corroborated this model at the family level in particular. A one-factor model was also tested but resulted in being less suitable at both the individual (within) and family (between) level of analysis. Finally, we consider challenges in using such statistical techniques, while taking into account the FRI’s briefness and easiness to complete and score it in a practice-oriented assessment perspective.

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30

Cho, Heetae, Dongoh Joo, and Kyle Maurice Woosnam. "Cross-cultural Validation of the Nostalgia Scale for Sport Tourism (NSST): A Multilevel Approach." Journal of Hospitality & Tourism Research 44, no. 4 (January 20, 2020): 624–43. http://dx.doi.org/10.1177/1096348019899461.

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This study evaluated the cross-cultural generalizability of the Nostalgia Scale for Sport Tourism (NSST), which was originally developed and examined in the context of football tourists in the United States. Data were collected from baseball tourists in South Korea, and multilevel confirmatory factor analysis and multilevel structural equation modelling were used for data analysis. Results supported the reliability and the validity of the scale both at individual and group levels, revealing an identical five-factor structure across the 29-item scale. Additionally, this study found a significant effect of sport nostalgia on revisit intention both at individual and group levels. The findings indicate that the NSST is applicable in understanding sport tourists’ nostalgia across different sport and cultural settings. Implications and suggestions for future nostalgia research are also discussed.

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31

Coromina, Lluís, Germà Coenders, and Tina Kogovšek. "Multilevel multitrait multimethod model." Advances in Methodology and Statistics 1, no. 2 (July 1, 2004): 323–49. http://dx.doi.org/10.51936/ukft4267.

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Our goal in this paper is to assess reliability and validity of egocentered network data using multilevel analysis (Muthén, 1989, Hox, 1993) under the multitrait-multimethod approach. The confirmatory factor analysis model for multitrait-multimethod data (Werts & Linn, 1970; Andrews, 1984) is used for our analyses. In this study we reanalyse a part of data of another study (Kogovšek et al., 2002) done on a representative sample of the inhabitants of Ljubljana. The traits used in our article are the name interpreters. We consider egocentered network data as hierarchical; therefore a multilevel analysis is required. We use Muthén’s partial maximum likelihood approach, called pseudobalanced solution (Muthén, 1989, 1990, 1994) which produces estimations close to maximum likelihood for large ego sample sizes (Hox & Mass, 2001). Several analyses will be done in order to compare this multilevel analysis to classic methods of analysis such as the ones made in Kogovšek et al. (2002), who analysed the data only at group (ego) level considering averages of all alters within the ego. We show that some of the results obtained by classic methods are biased and that multilevel analysis provides more detailed information that much enriches the interpretation of reliability and validity of hierarchical data. Within and between-ego reliabilities and validities and other related quality measures are defined, computed and interpreted.

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32

Sørlie, Mari-Anne, and Silje Sommer Hukkelberg. "Assessing Collective Efficacy in School: A Norwegian Validation Study of Goddard’s 12-item Scale." Nordic Studies in Education 42, no. 3 (October 17, 2022): 272–88. http://dx.doi.org/10.23865/nse.v42.3542.

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The psychometric properties of the short Collective Teacher Efficacy Scale (CTE; Goddard, 2002) were examined in a sample of 1,524 teachers from 65 Norwegian primary schools. Associations between items were investigated using network analysis, and multilevel confirmatory factor analysis was applied to determine the optimal factor structure across the twelve items. Criterion validity was examined by inspecting correlations between CTE, academic achievement, and school problem behavior. The results supported the expected two-factor structure, group competence, and task analysis. Correlations in the expected directions support the instrument’s concurrent and predictive validity. Implications of the results are discussed.

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33

Zhonghua, Zhang, and John Chi-Kin Lee. "Students' Behavioral and Emotional Participation in Academic Activities in the Mathematics Classroom: A Multilevel Confirmatory Factor Analysis." Journal of Experimental Education 86, no. 4 (June 16, 2017): 610–32. http://dx.doi.org/10.1080/00220973.2017.1335684.

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34

Jang, Seulki, Eun Sook Kim, Chunhua Cao, Tammy D. Allen, Cary L. Cooper, Laurent M. Lapierre, Michael P. O’Driscoll, et al. "Measurement Invariance of the Satisfaction With Life Scale Across 26 Countries." Journal of Cross-Cultural Psychology 48, no. 4 (March 22, 2017): 560–76. http://dx.doi.org/10.1177/0022022117697844.

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The Satisfaction With Life Scale (SWLS) is a commonly used life satisfaction scale. Cross-cultural researchers use SWLS to compare mean scores of life satisfaction across countries. Despite the wide use of SWLS in cross-cultural studies, measurement invariance of SWLS has rarely been investigated, and previous studies showed inconsistent findings. Therefore, we examined the measurement invariance of SWLS with samples collected from 26 countries. To test measurement invariance, we utilized three measurement invariance techniques: (a) multigroup confirmatory factor analysis (MG-CFA), (b) multilevel confirmatory factor analysis (ML-CFA), and (c) alignment optimization methods. The three methods demonstrated that configural and metric invariances of life satisfaction held across 26 countries, whereas scalar invariance did not. With partial invariance testing, we identified that the intercepts of Items 2, 4, and 5 were noninvariant. Based on two invariant intercepts, factor means of countries were compared. Chile showed the highest factor mean; Spain and Bulgaria showed the lowest. The findings enhance our understanding of life satisfaction across countries, and they provide researchers and practitioners with practical guidance on how to conduct measurement invariance testing across countries.

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35

Kim, Eun Sook, Yan Wang, and Sarah M. Kiefer. "Cross-Level Group Measurement Invariance When Groups Are at Different Levels of Multilevel Data." Educational and Psychological Measurement 78, no. 6 (November 8, 2017): 973–97. http://dx.doi.org/10.1177/0013164417739062.

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Studies comparing groups that are at different levels of multilevel data (namely, cross-level groups) using the same measure are not unusual such as student and teacher agreement in education or congruence between patient and physician perceptions in health research. Although establishing measurement invariance (MI) between these groups is important, testing MI is methodologically challenging because the groups compared for MI are at different levels with one group nested within the other group. We propose a multilevel confirmatory factor analysis (CFA) model that allows MI testing between cross-level groups at the between level and demonstrated testing MI between students and teachers using the promoting social interaction scale. Along with the demonstration, some methodological issues in implementing the proposed model (e.g., cluster invariance and reliability) and evaluating the model fit of multilevel CFA (e.g., ΔCFI and level-specific fit indices) and alternative approaches to the proposed model are discussed.

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36

Barbour, J. B., and J. C. Lammers. "Measuring professional identity: a review of the literature and a multilevel confirmatory factor analysis of professional identity constructs." Journal of Professions and Organization 2, no. 1 (January 21, 2015): 38–60. http://dx.doi.org/10.1093/jpo/jou009.

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37

Nieto-Guerrero, Manuel, Mirko Antino, and Jose M. Leon-Perez. "Validation of the Spanish version of the intragroup conflict scale (ICS-14)." International Journal of Conflict Management 30, no. 1 (February 11, 2019): 24–44. http://dx.doi.org/10.1108/ijcma-11-2017-0139.

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PurposeThere is increasing evidence about the key role that intragroup conflicts have for teams’ performance and its members’ well-being. However, the existing measures in the Spanish-speaking context to address intragroup conflicts suffer from important theoretical and methodological flaws. In response, this study aims to provide a valid and reliable scale to measure intragroup conflicts in organizational settings: the Intragroup Conflict Scale in its 14-item version (ICS-14: Jehn et al., 2008).Design/methodology/approachIn a cross-sectional survey design, the authors analyze the ICS’s internal consistency and reliability, factor solution and external validity by using a multilevel approach in a sample consisting of 588 workers nested in 55 production teams from a Spanish company.FindingsResults indicated that the ICS-14 exhibited good Cronbach’s alpha (0.62-0.95), omega (0.63-0.95) and multilevel alpha coefficient (0.82-0.98). In addition, in line with the theoretical conception of three types of intragroup conflicts, results from a multilevel confirmatory factor analysis revealed that a three multilevel-factor solution presented the best fit to the data. Finally, multilevel analyses also showed that intragroup conflicts are associated to burnout, engagement at work and perceived team’s quality of service, which provided additional support for using the ICS-14 in the Spanish context.Practical implicationsThis study offers a reliable and valid measurement of intragroup conflict, considering the whole instrument and its different dimensions, which can be used to develop team strategies and evaluate the effect of specific interventions on conflict.Originality/valueThe authors validate the most recent 14-item version of the ICS-14 by applying a multilevel approach to a group-level construct that overcomes previous methodological flaws.

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38

Randolph, Robert Van de Graaff. "A multilevel study of structural resilience in interfirm collaboration." Management Decision 54, no. 1 (February 8, 2016): 248–66. http://dx.doi.org/10.1108/md-06-2015-0247.

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Purpose – The purpose of this paper is to develop the concept of a high performance alliance macro-culture as a multilevel construct reflective of resilient collaborative systems of exchange within strategic alliances and explores the distinct capabilities of this multilevel approach in predicting alliance outcomes. Design/methodology/approach – The hypotheses developed in this study are tested using primary data collected from 650 members of 15 non-profit organizations in two multi-organizational collaborative networks. Considering the multilevel nature of the study the structural hypotheses are tested using a multilevel confirmatory factor analysis and the predictive hypotheses are tested using multilevel structural equation modeling. Findings – All but one structural hypothesis are supported and all predictive hypotheses are supported suggesting that a multilevel macro-cultural conceptualization is effective in exploring the relationship between collaborative exchange systems and their outcomes. Research limitations/implications – Limitations stem from the generalizability of the data collected as the alliances formed by non-profit firms may not be wholly reflective of the alliance structures and goals of other firm types. Originality/value – This study primarily contributes to multilevel study of strategic alliances and the study of collaborative norms and structures of allied groupings. The results of this study lend support to the importance of taking a network governance perspective and illustrate the limitations of traditional single-level approaches when studying interfirm collaborative networks and structural resilience therein.

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Stapleton, Laura M., and Tessa L. Johnson. "Models to Examine the Validity of Cluster-Level Factor Structure Using Individual-Level Data." Advances in Methods and Practices in Psychological Science 2, no. 3 (August 21, 2019): 312–29. http://dx.doi.org/10.1177/2515245919855039.

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When researchers model multilevel data, often a shared construct of interest is measured by individual-level observations, for example, students’ responses regarding how engaging their instructor’s teaching style is. In such cases, the construct of interest, “engaging teaching,” is shared at the cluster level across individuals, yet rarely are these shared constructs modeled as such. To address this gap, we discuss multilevel confirmatory factor analysis models that have been applied to item-level data obtained from multiple raters within given clusters, focusing particularly on measuring a characteristic at the cluster level. After discussing the parameters in each potential model, we make recommendations as to the appropriate modeling approach and the steps to be taken for model assessment given a set of data and hypothesized construct of interest. In particular, we encourage applied researchers not to use a model without constraints across the within-cluster level and the between-cluster level because such models assume that the average amount of the individual-level construct in a cluster does not differ across clusters. To illustrate this issue, we present simulation results and evaluate a series of models using empirical data from the Trends in International Mathematics and Science Study.

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Murray, Aja Louise, Ingrid Obsuth, Manuel Eisner, and Denis Ribeaud. "Disaggregating Between- and Within-Classroom Variation in Student Behavior: A Multilevel Factor Analysis of Teacher Ratings of Student Prosociality and Aggression." Journal of Early Adolescence 39, no. 7 (September 7, 2018): 993–1019. http://dx.doi.org/10.1177/0272431618797005.

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Teacher ratings of student behaviors vary systematically both at the student and teacher/classroom level. Multilevel confirmatory factor analysis (ML-CFA) can disaggregate between- and within-teacher/classroom variance, identify an optimal psychometric model at each level, and test correlates of the resulting dimensions. In this study, 250 teachers (37% males) rated an average of 4.02 students (51% males; aged 10 years at Time 1 and 11 years at Time 2) from a normative sample of Swiss youth. Substantial and unidimensional between-teacher variation in ratings of both prosociality and aggression were identified, and this was stable across time. These dimensions were not associated at the between-teacher/classroom level with teacher gender or teacher–student relationships, although they were associated with teacher-student relationships at the within-teacher/classroom level. There was little between-teacher/classroom variation observed in student self-reports of prosocial and aggressive behavior, and multilevel CFA was not possible for these ratings. Future research should aim to identify sources of between-teacher/classroom variation. This should include factors that influence negative and positive teacher perceptions of and response biases related to student behavior as well as those that influence student behavior itself.

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Wu, Chia-Huei. "Factor analysis of the general self-efficacy scale and its relationship with individualism/collectivism among twenty-five countries: Application of multilevel confirmatory factor analysis." Personality and Individual Differences 46, no. 7 (May 2009): 699–703. http://dx.doi.org/10.1016/j.paid.2009.01.025.

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Kunovich, Robert M. "The Sources and Consequences of National Identification." American Sociological Review 74, no. 4 (August 2009): 573–93. http://dx.doi.org/10.1177/000312240907400404.

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This article examines national identification from a comparative and multilevel perspective. Building on the identity, nationalism, and prejudice literatures, I analyze relationships between societies' economic, political, and cultural characteristics (e.g., development, globalization, democratic governance, militarism, and religious and linguistic diversity), individual characteristics (e.g., socioeconomic status and minority status), and preferences for the content of national identities. I also examine relationships between national identity content and public policy preferences toward immigration, citizenship, assimilation, and foreign policy, generally. I use confirmatory factor analysis and multilevel modeling to analyze country-level data and survey data from 31 countries (from the International Social Survey Program's 2003 National Identity II Module). Results suggest that individual and country characteristics help account for the variable and contested nature of national identification. Moreover, the content of national identity categories has implications for public policy and intergroup relations.

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Carretero-Dios, Hugo, Michael Eid, and Willibald Ruch. "Analyzing multitrait-mulitmethod data with multilevel confirmatory factor analysis: An application to the validation of the State-Trait Cheerfulness Inventory." Journal of Research in Personality 45, no. 2 (April 2011): 153–64. http://dx.doi.org/10.1016/j.jrp.2010.12.007.

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Zhang, Zhonghua, John Chi-Kin Lee, and Ping Ho Wong. "Multilevel structural equation modeling analysis of the servant leadership construct and its relation to job satisfaction." Leadership & Organization Development Journal 37, no. 8 (November 7, 2016): 1147–67. http://dx.doi.org/10.1108/lodj-07-2015-0159.

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Purpose The purpose of this paper is to address the statistical issues associated with the hierarchically structured data in previous studies that focused on servant leadership. To resolve these issues, multilevel modeling methods were applied to re-visit the construct validity of the servant leadership questionnaire developed by Barbuto and Wheeler (2006) and investigate the relationship between servant leadership and job satisfaction under a multilevel framework. Design/methodology/approach The survey data was obtained from a sample of 2,089 teachers from 117 primary and secondary schools in Hong Kong. The analyses were conducted using multilevel confirmatory factor analysis (MLCFA) and multilevel structural equation modeling (MLSEM). Findings The results revealed the significant and non-trivial variances that were explained at the organization level in the items measuring servant leadership, which justified the use of MLCFA and MLSEM. The results of MLCFA provided empirical support for the multidimensional construct as well as the second-order factorial structure of servant leadership measures at both the individual and organization levels. In addition, the positive relationships between servant leadership and the followers’ job satisfaction were found to vary at different levels. Originality/value This study reiterates the importance of using appropriate methods to capture a solid definition of the construct of servant leadership and provides new insights into the conceptual framework of servant leadership as well as the effects of servant leadership on individual and organizational outcomes.

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He, Jia, Joanne M. Chung, and Fons J. R. van de Vijver. "An Examination of Different Scale Usage Correction Procedures to Enhance Cross-Cultural Data Comparability." Journal of Cross-Cultural Psychology 51, no. 10 (October 13, 2020): 794–813. http://dx.doi.org/10.1177/0022022120960815.

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This study aims to examine different scale usage correction procedures that are meant to enhance the cross-cultural comparability of Likert scale data. Specifically, we examined a priori study design (i.e., anchoring vignettes and overclaiming) and post hoc statistical procedures (i.e., ipsatization and extreme response style correction) in data from the 2012 Programme for International Student Assessment across 64 countries. We analyzed both original item responses and corrected item scores from two targeted scales in an integrative fashion by using multilevel confirmatory factor analysis and multilevel regressions. Results indicate that mean levels and structural relations varied across the correction procedures, although the psychological meaning of the constructs examined did not change. Furthermore, scores were least affected by these procedures for females who did not repeat a grade and students with higher math achievement. We discuss the implications of our findings and offer recommendations for researchers who are considering scale usage correction procedures.

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Aljaberi, Musheer A., Kuo-Hsin Lee, Naser A. Alareqe, Mousa A. Qasem, Abdulsamad Alsalahi, Atiyeh M. Abdallah, Sarah Noman, Ala’a B. Al-Tammemi, Mohamed Izham Mohamed Ibrahim, and Chung-Ying Lin. "Rasch Modeling and Multilevel Confirmatory Factor Analysis for the Usability of the Impact of Event Scale-Revised (IES-R) during the COVID-19 Pandemic." Healthcare 10, no. 10 (September 24, 2022): 1858. http://dx.doi.org/10.3390/healthcare10101858.

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Background: Several instruments are currently used to assess Coronavirus Disease 2019 (COVID-19) -induced psychological distress, including the 22-item Impact of Event Scale-Revised (IES-R). The IES-R is a self-administered scale used to assess post-traumatic stress disorder (PTSD). The current study aimed to examine the construct validity of the IES-R, based on the Rasch model, with COVID-19-related data, as well as to test the multilevel construct validity of the IES-R within and among countries during the pandemic crisis. Methods: A multi-country web-based cross-sectional survey was conducted utilizing the 22-item IES-R. A total of 1020 participants enrolled in our survey, of whom 999 were included in the analyses. Data were analyzed using Rasch modeling and multilevel confirmatory factor analysis (MCFA). Results: The Rasch modeling results of the IES-R demonstrated that the IES-R is a satisfactory instrument with the five-point Likert scale, asserting that its 22 items are significant contributors to assessing PTSD as a unidimensional construct covered by the items of the IES-R. The MCFA confirmed that the 22-item IES-R, with its three factors, including intrusion, avoidance, and hyperarousal, demonstrates adequate construct validity at the within- and among-country levels. However, the results of the Akaike information criterion (AIC) model determined that the 16-item IES-R is better than the 22-item IES-R. Conclusion: The results suggested that the 22-item IES-R is a reliable screening instrument for measuring PTSD related to the COVID-19 pandemic, and can be utilized to provide timely psychological health support, when needed, based on the screening results.

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Finster, Matthew, and Anthony Milanowski. "Teacher perceptions of a new performance evaluation system and their influence on practice: A within- and between-school level analysis." education policy analysis archives 26 (April 2, 2018): 41. http://dx.doi.org/10.14507/epaa.26.3500.

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Teacher performance evaluation systems (PESs) are central to policy efforts to increase teacher effectiveness and student learning. We argue that for these reforms to work, PESs need to be treated as coherent systems, in which teachers perceive that there are linkages between the PES components. Using teacher survey data from a large, midwestern school district, this article explores the linkages between teacher perceptions of a new PES using confirmatory factor analysis (CFA), structural equation modeling (SEM), and multilevel CFA. We also examine whether a strong evaluation climate developed in this district. The CFA and SEM analysis demonstrate that teacher perceptions of PES are interrelated and linked to perceptions of changes in teaching practices and to the potential impact on student learning. The multilevel CFA demonstrates cross-level noninvariance, with fewer factors being identified at the school levels. These results suggest a need for a school‐level theory of action with corresponding school‐level constructs. While we did not find evidence of a shared strong evaluation climate, the results of the analysis illustrate the importance of examining within-school agreement, both to assess the reliability of between-school differences in average teacher perceptions and to assess whether schools are developing a strong evaluation climate.

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Fonseca-Pedrero, E., M. Debbané, J. Ortuño-Sierra, R. C. K. Chan, D. C. Cicero, L. C. Zhang, C. Brenner, et al. "The structure of schizotypal personality traits: a cross-national study." Psychological Medicine 48, no. 3 (July 17, 2017): 451–62. http://dx.doi.org/10.1017/s0033291717001829.

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BackgroundSchizotypal traits are considered a phenotypic-indicator of schizotypy, a latent personality organization reflecting a putative liability for psychosis. To date, no previous study has examined the comparability of factorial structures across samples originating from different countries and cultures. The main goal was to evaluate the factorial structure and reliability of the Schizotypal Personality Questionnaire (SPQ) scores by amalgamating data from studies conducted in 12 countries and across 21 sites.MethodThe overall sample consisted of 27 001 participants (37.5% males,n= 4251 drawn from the general population). The mean age was 22.12 years (s.d.= 6.28, range 16–55 years). The SPQ was used. Confirmatory factor analysis (CFA) and Multilevel CFA (ML-CFA) were used to evaluate the factor structure underlying the SPQ scores.ResultsAt the SPQ item level, the nine factor and second-order factor models showed adequate goodness-of-fit. At the SPQ subscale level, three- and four-factor models displayed better goodness-of-fit indices than other CFA models. ML-CFA showed that the intraclass correlation coefficients values were lower than 0.106. The three-factor model showed adequate goodness of fit indices in multilevel analysis. The ordinalαcoefficients were high, ranging from 0.73 to 0.94 across individual samples, and from 0.84 to 0.91 for the combined sample.ConclusionsThe results are consistent with the conceptual notion that schizotypal personality is a multifaceted construct and support the validity and utility of SPQ in cross-cultural research. We discuss theoretical and clinical implications of our results for diagnostic systems, psychosis models and cross-national mental health strategies.

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Ferreira, Aristides I., Luis F. Martinez, José Pereira Lamelas, and Rosa I. Rodrigues. "Mediation of job embeddedness and satisfaction in the relationship between task characteristics and turnover." International Journal of Contemporary Hospitality Management 29, no. 1 (January 9, 2017): 248–67. http://dx.doi.org/10.1108/ijchm-03-2015-0126.

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Purpose Employees’ turnover intention is a key problem that hotel managers face daily. This is partially explained by the inevitability of performing tasks with little significance and low identity. This study aims to understand how job embeddedness and job satisfaction could lessen the undesirable effect of task characteristics on turnover intentions. Design/methodology/approach A sample of 525 employees operating in 46 Portuguese hotels was used in this study. The questionnaire included demographic variables and four reliable instruments used to measure job satisfaction, job characteristics, job embeddedness and turnover intentions. The study used a multilevel statistical approach considering both the individual and the hotel levels of analysis. Findings Through multilevel statistics, the findings suggest that both at the individual level and the hotel level of analysis, job satisfaction and job embeddedness fully mediated the relationship between different task characteristics (significance and identity) and turnover intentions. Research limitations/implications Despite a possible absence of common method variance, due to the confirmatory factor analysis, social desirability bias may exist because of the self-reported nature of the survey. Practical implications Managers should increase the perceived costs of employees leaving the hotel by introducing training programs and plans for career development. Also, to increase job embeddedness, managers should also rethink the organizational dynamics of this industry. Originality/value This research provides empirical evidence of the antecedents and mediators of employees’ intentions to leave the hotel industry both at the individual and at the hotel level (multilevel approach).

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Höfling, Volkmar, Karin Schermelleh-Engel, and Helfried Moosbrugger. "Analyzing Multitrait-Multimethod Data." Methodology 5, no. 3 (January 2009): 99–111. http://dx.doi.org/10.1027/1614-2241.5.3.99.

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Assessing construct validity is a core task in psychology. Since Campbell and Fiske’s (1959) seminal article on multitrait-multimethod (MTMM) analysis, several different methodological approaches for the analysis of convergent and discriminant validity of MTMM data have been developed. In this article, two MTMM approaches are transferred to the general framework of confirmatory factor analysis and compared with the extended version of the correlated trait-correlated method minus one model ( Nussbeck, Eid, Geiser, Courvoisier, & Lischetzke, 2009 ): The multilevel MTMM model ( Maas, Lensvelt-Mulders, & Hox, 2009 ) and the three-mode model ( Oort, 2009 ). Assessing the construct validity of a German Big Five MTMM data set these three MTMM approaches are compared with regard to convergent and discriminant validity estimates and with regard to method effects. Advantages and limitations of each methodological approach will be discussed in detail.

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