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Multilevel and Longitudinal Modeling with IBM SPSS
Abstract
This book demonstrates how to use multilevel and longitudinal modeling techniques available in the IBM SPSS mixed-effects program (MIXED). Annotated screen shots provide readers with a step-by-step understanding of each technique and navigating the program. Readers learn how to set up, run, and interpret a variety of models. Diagnostic tools, data management issues, and related graphics are introduced throughout. Annotated syntax is also available for those who prefer this approach. Extended examples illustrate the logic of model development to show readers the rationale of the research questions and the steps around which the analyses are structured. The data used in the text and syntax examples are available at www.routledge.com/9780415817110. Highlights of the new edition include: Updated throughout to reflect IBM SPSS Version 21. Further coverage of growth trajectories, coding time-related variables, covariance structures, individual change and longitudinal experimental designs (Ch.5). Extended discussion of other types of research designs for examining change (e.g., regression discontinuity, quasi-experimental) over time (Ch.6). New examples specifying multiple latent constructs and parallel growth processes (Ch. 7). Discussion of alternatives for dealing with missing data and the use of sample weights within multilevel data structures (Ch.1). The book opens with the conceptual and methodological issues associated with multilevel and longitudinal modeling, followed by a discussion of SPSS data management techniques which facilitate working with multilevel, longitudinal, and cross-classified data sets. Chapters 3 and 4 introduce the basics of multilevel modeling: developing a multilevel model, interpreting output, and trouble-shooting common programming and modeling problems. Models for investigating individual and organizational change are presented in chapters 5 and 6, followed by models with multivariate outcomes in chapter 7. Chapter 8 provides an illustration of multilevel models with cross-classified data structures. The book concludes with ways to expand on the various multilevel and longitudinal modeling techniques and issues when conducting multilevel analyses. It's ideal for courses on multilevel and longitudinal modeling, multivariate statistics, and research design taught in education, psychology, business, and sociology.
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“Missing data were handled with a full information maximum likelihood (FIML) method (Heck et al., 2013).”
From Mindfulness-Based Stress Reduction Increases Mental Wellbeing and Emotion Regulation During the First Wave of the COVID-19 Pandemic: A Synchronous Online Intervention Study (2021) · cites this paper as Heck et al., 2013
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Items 201–300 of 708
The effectiveness of a classroom-based phonological awareness program for 4–5-year-olds
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Ching, B. H., Li, X., & Chen, T. T. (2023) · Journal of Youth and Adolescence · doi:10.1007/s10964-023-01815-5 · Cited by 23
A Multilevel Analysis of the Relation Between Bullying Roles and Social and Emotional Competencies
Coelho, V. A., & Sousa, V. (2018) · Journal of Interpersonal Violence · doi:10.1177/0886260518801943 · Cited by 32
Capone, C., Tripp, J. C., Trim, R. S., Davis, B. C., Haller, M. M., & Norman, S. B. (2020) · Journal of Traumatic Stress · doi:10.1002/jts.22538 · Cited by 21
Ebisawa, K., Nakayama, S., Pai, C., Kinoshita, R., & Hiroshi, K. O. I. E. (2021) · PLoS ONE · doi:10.1371/journal.pone.0254610 · Cited by 21
A Multimethod Latent State-Trait Model for Structurally Different And Interchangeable Methods
Koch, T., Schultze, M., Holtmann, J., Geiser, C., & Eid, M. (2016) · Psychometrika · doi:10.1007/s11336-016-9541-x · Cited by 19
Morean, M. E., Darling, N. E., Smit, J., DeFeis, J., Wergeles, M., Kurzer-Yashin, D., & Custer, K. (2018) · Journal of Interpersonal Violence · doi:10.1177/0886260518777555 · Cited by 22
The Impact of Secondary School Transition on Self-Concept and Self-Esteem
Coelho, V. A., & Romão, A. M. (2017) · Revista de Psicodidáctica (English ed ) · doi:10.1016/j.psicoe.2016.10.001 · Cited by 21
Januário, L. B., Karstad, K., Rugulies, R., Bergström, G., Holtermann, A., & Hallman, D. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16193610 · Cited by 21
“Since DOSES data follow a hierarchical structure, we adopted a multilevel linear mixed model approach for the statistical analyses [43], considering ward and nursing home as random effects.”
MethodBlake, K. R., Dixson, B. J. W., O’Dean, S., & Denson, T. F. (2017) · Hormones and Behavior · doi:10.1016/j.yhbeh.2017.03.005 · Cited by 20
Kooistra, C. M., Hall, T. E., Paveglio, T. B., & Pickering, M. (2017) · Environmental Management · doi:10.1007/s00267-017-0962-9 · Cited by 19
“A large ICC value (i.e., closer to 1 than 0) suggests a low degree of variation among respondents who experienced the same fire but high variation compared with respondents who experienced other fires (Woltman et al. 2012; Heck et al. 2014).”
BackgroundMethodResultInfluentialTremblay, M., Parent‐Rocheleau, X., & Sajadi, P. (2021) · Journal of Leadership & Organizational Studies · doi:10.1177/15480518211041629 · Cited by 21
Madera, J. M. (2017) · Cornell Hospitality Quarterly · doi:10.1177/1938965517730317 · Cited by 20
“and the Level 1 variables were centered on the individuals’ means (i.e., group mean centered; Heck et al., 2013).”
BackgroundMethodInfluentialPlaß-Christl, A., Otto, C., Klasen, F., Wiegand‐Grefe, S., Barkmann, C., Hölling, H., Schulte‐Markwort, M., & Ravens‐Sieberer, U. (2017) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-017-1084-x · Cited by 22
“Correspondingly, linear mixed models were calculated using fullinformation maximum likelihood (FIML) estimation, which allows the inclusion of cases with missing data [26, 27].”
MethodCorcoran, R. P. (2017) · Teaching and Teacher Education · doi:10.1016/j.tate.2017.11.010 · Cited by 25
Tremblay, M., Gaudet, M., & Parent‐Rocheleau, X. (2021) · Journal of Leadership & Organizational Studies · doi:10.1177/1548051821989289 · Cited by 22
Relationship between fruit and vegetable intake and interference control in breast cancer survivors
Zuniga, K. E., MacKenzie, M. J., Roberts, S. A., Raine, L. B., Hillman, C. H., Kramer, A. F., & McAuley, E. (2015) · European Journal of Nutrition · doi:10.1007/s00394-015-0973-3 · Cited by 20
“Predictor variables were grand-mean centered to allow for inference of average predictor effects [30].”
BackgroundRoubinov, D. S., Don, B. P., Blades, R. B., & Epel, E. S. (2022) · Family Process · doi:10.1111/famp.12810 · Cited by 19
Liu, M., Gorgievski, M. J., Zwaga, J., & PAAS, F. G. W. C. (2022) · The International Journal of Management Education · doi:10.1016/j.ijme.2022.100750 · Cited by 24
Grohnert, T., Meuwissen, R., & Gijselaers, W. (2018) · Vocations and Learning · doi:10.1007/s12186-018-9206-2 · Cited by 19
Parent responses to pediatric pain: The differential effects of ethnicity on opioid consumption
Donaldson, C. D., Jenkins, B. N., Fortier, M. A., Phan, M., Tomaszewski, D. M., Yang, S., & Kain, Z. N. (2020) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2020.110251 · Cited by 19
Sproesser, G., Arens-Azevêdo, U., & Renner, B. (2023) · PLOS Sustainability and Transformation · doi:10.1371/journal.pstr.0000086 · Cited by 19
Gruber, R., Häfner, M., & Kachel, S. (2023) · British Journal of Social Psychology · doi:10.1111/bjso.12700 · Cited by 20
Angel investor network pitch meetings: The pull and push of peer opinion
Wood, M., Long, A., & Artz, K. (2020) · Business Horizons · doi:10.1016/j.bushor.2020.03.008 · Cited by 14
Astridge, D. J., Sommerville, A., Verheul, M. H. G., & Turner, A. P. (2020) · European Journal of Sport Science · doi:10.1080/17461391.2020.1857442 · Cited by 19
“001), μoj is the residual variance for the intercepts and εij represents the random variation in estimating sleep quality within individuals (Heck et al., 2013).”
BackgroundInfluentialAwad, R., Shamay‐Tsoory, S., & Palgi, Y. (2023) · International Psychogeriatrics · doi:10.1017/s1041610223000133 · Cited by 19
How within-person research can extend marketing knowledge
Childs, D. R. N., Lee, N. J., Cadogan, J. W., & Dewsnap, B. (2023) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-023-00978-8 · Cited by 21
“Since the data was nested and unbalanced, maximum likelihood estimation was utilized (Heck et al., 2013; Maas & Hox, 2005).”
Green, Z. A. (2024) · Studies In Educational Evaluation · doi:10.1016/j.stueduc.2024.101343 · Cited by 22
Parkin, G. M., Thomas, E. A., & Corey‐Bloom, J. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.105173 · Cited by 18
“28 We fi rst executed a Level 1 variance components model to determine whether between-participant clustering of NfL trajectories was evident, without factoring in participant grouping (such as HD-ISS category).”
MethodTreatment Responsivity of Aggressive Forensic Psychiatric Outpatients
Smeijers, D., Bulten, E., Buitelaar, J. K., & Verkes, R. (2017) · International Journal of Offender Therapy and Comparative Criminology · doi:10.1177/0306624x17747052 · Cited by 18
“The covariance type for the random effects was set at unstructured, which is a completely general covariance matrix (Heck et al., 2013).”
BackgroundInfluentialPorter-Vignola, É., Booij, L., Chartier, G., Garel, P., & Herba, C. M. (2021) · Psychiatry Research · doi:10.1016/j.psychres.2021.113777 · Cited by 18
Isdahl-Troye, A., Villar, P., Domínguez-Álvarez, B., Romero, E., & Deater-Deckard, K. D. (2021) · Research on Child and Adolescent Psychopathology · doi:10.1007/s10802-021-00865-2 · Cited by 20
“05 for defining the presence of substantial clustering (Heck et al., 2014).”
BackgroundThe impact of COVID-19 lockdown on soccer positional and physical demands in the Spanish La Liga
García‐Calvo, T., Fernández-Navarro, J., García, J. D., et al. (2022) · Science and Medicine in Football · doi:10.1080/24733938.2022.2055784 · Cited by 18
Toikko, T., & Rantanen, T. (2017) · Journal of International and Comparative Social Policy · doi:10.1080/21699763.2017.1302892 · Cited by 18
“This ICC value means that 7.4 percent of the variability in abstract attitudes can be attributed to the country level (e.g. Heck et al., 2014).”
BackgroundInfluentialNitsan, G., Banai, K., & Ben-David, B. M. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.841466 · Cited by 17
Metzner, G., Horstmeier, L. M., Bengel, J., Bitzer, E. M., Dreher, E., Frank, F., Göhner, A., Heimbach, B., Himmelsbach, I., Kaier, K., et al. (2023) · BMC Geriatrics · doi:10.1186/s12877-023-03797-2 · Cited by 17
King, J. S., Koppenhafer, L., & Madrigal, R. (2020) · Journal of Public Policy & Marketing · doi:10.1177/0743915619889052 · Cited by 17
Characterizing the resting metabolic rate ratio in ovulatory exercising women over 12 months
Strock, N. C. A., Koltun, K. J., Mallinson, R. J., Williams, N. I., & Souza, M. J. D. (2020) · Scandinavian Journal of Medicine and Science in Sports · doi:10.1111/sms.13688 · Cited by 17
Rent Burden and Depression Among Mothers: an Analysis of Primary Caregiver Outcomes
Elliott, S., West, S., & Castro, A. (2021) · Journal of Policy Practice and Research · doi:10.1007/s42972-021-00040-3 · Cited by 17
The Moderating Effect of State Anger on Treatment Outcome in Female Adolescents With PTSD*
Kaczkurkin, A. N., Asnaani, A., Zhong, J., & Foa, E. B. (2016) · Journal of Traumatic Stress · doi:10.1002/jts.22116 · Cited by 17
Balasundaram, A. P., Athens, J., Schneiders, A. G., McCrory, P. R., & Sullivan, S. J. (2017) · PM&R · doi:10.1016/j.pmrj.2017.01.004 · Cited by 17
“Furthermore, the estimation of the ICC is also needed to substantiate the necessity to use the linear mixed-effects modeling in any study [32].”
MethodTrait anxiety and bystander motivation to defend victims of school bullying
Jungert, T., & Perrin, S. G. (2019) · Journal of Adolescence · doi:10.1016/j.adolescence.2019.10.001 · Cited by 17
Weigand, R., & Jacobsen, T. (2021) · PLoS ONE · doi:10.1371/journal.pone.0248529 · Cited by 21
“For each analysis, we constructed parallel models, following a general “build-up” strategy for model testing [31]: In the first step, we used a null model (i.”
MethodGiovannetti, A. M., Messmer Uccelli, M., Solari, A., & Pakenham, K. I. (2022) · Disability and Rehabilitation · doi:10.1080/09638288.2022.2025926 · Cited by 16
Determined Yet Dehumanized: People Higher in Self-Control Are Seen as More Robotic
Lapka, S., Kung, F. Y. H., brienza, J. P., & Scholer, A. A. (2022) · Social Psychological and Personality Science · doi:10.1177/19485506221093109 · Cited by 17
Raimundi, M. J., Corti, J. F., Pérez-Gaido, M., Álvarez, O., & Castillo, I. (2023) · Sustainability · doi:10.3390/su15065179 · Cited by 16
How Context Matters: The Significance of Political Homogeneity and Language for Political Efficacy
Karv, T., Lindell, M., & Rapeli, L. (2021) · Scandinavian Political Studies · doi:10.1111/1467-9477.12215 · Cited by 17
Stability of psychological resilience of police officers: A three-wave latent class analysis
van der Meulen, E., van Veldhoven, M. J. P. M., & van der Velden, P. G. (2019) · Personality and Individual Differences · doi:10.1016/j.paid.2019.03.006 · Cited by 17
Khoo, S. Y., Correia, V., & Uhrig, A. (2020) · Laboratory Animals · doi:10.1177/0023677219894356 · Cited by 14
“We used a scaled identity covariance matrix type and maximum likelihood estimation to model whether the fixed effects of baselineenrichment, experimental procedures, reinforcer, and supplier affected severity scores from onset to week 2 when all cases were available for analysis.[40] Data are presented as means ± SEM.”
MethodAbdulla, A., Whipp, P. R., McSporran, G., & Teo, T. P. (2022) · PLoS ONE · doi:10.1371/journal.pone.0268098 · Cited by 25
“Repeated measure variability was analysed with the restricted maximum likelihood estimation (RMLE) method, the autoregressive homogeneous (ARH) covariance structure, and the diagonal covariance type for random effect and Wald test [37].”
BackgroundMethodInfluentialO’Connor, M., O’Reilly, G., Murphy, E., Connaughton, L., Hoctor, E., & McHugh, L. (2022) · Behaviour Research and Therapy · doi:10.1016/j.brat.2022.104120 · Cited by 18
Gender and socioeconomic patterning of self-reported sleep problems across European countries
Baranowski, M., & Jabkowski, P. (2023) · European Journal of Public Health · doi:10.1093/eurpub/ckad012 · Cited by 15
Malhotra, N., Sahadev, S., Leeflang, P. S. H., & Purani, K. (2020) · Information Systems Frontiers · doi:10.1007/s10796-020-10046-z · Cited by 18
Nawani, J., Rixius, J., & Neuhaus, B. J. (2016) · International Journal of Science Education · doi:10.1080/09500693.2016.1213456 · Cited by 15
“Here, β represents the partial regression coefficient or unstandardized regression estimates, presented as ‘estimates of fixed effects’ in SPSS (see Model 1, 2 & 3: Table 6) (Bring, 1994; Heck et al., 2013).”
MethodWho benefits from a dietary online intervention? Evidence from Italy, Spain and Greece
Schwarzer, R., Fleig, L., Warner, L. M., Gholami, M., Serra‐Majem, L., Ngo, J., Román‐Viñas, B., Ribas‐Barba, L., Distante, A., Ntzani, E., et al. (2016) · Public Health Nutrition · doi:10.1017/s1368980016002913 · Cited by 15
“0, linear multilevel models were computed using age, BMI and sex as covariates, with two time points crossed in individuals (level 2) with REML (restricted maximum likelihood) estimation((26,27)).”
MethodMultilevel Modeling for Research in Group Work
Selig, J. P., Trott, A., & Lemberger, M. E. (2017) · The Journal for Specialists in Group Work · doi:10.1080/01933922.2017.1282571 · Cited by 15
Smeijers, D., Bulten, E., Franke, B., Buitelaar, J. K., & Verkes, R. (2017) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2017.06.016 · Cited by 16
“One advantage of this analysis is that it is possible to include individuals with incomplete data, without imputing data (Heck et al., 2013).”
BackgroundBuchanan-Pascall, S., Melvin, G. A., Gordon, M. S., & Gray, K. M. (2021) · Child Psychiatry & Human Development · doi:10.1007/s10578-021-01276-6 · Cited by 14
“Best model fit for the null model was determined by the smallest Akaike Information Criterion (AIC) index, and achieved using unstructured covariance structure and maximum likelihood estimation [95].”
MethodBelay, D. G., Asratie, M. H., Gashaw, M., et al. (2022) · BMC Public Health · doi:10.1186/s12889-022-13249-y · Cited by 14
“Moreover, analyzing the hierarchical nature data like the DHS data using single-level analysis leads to incorrect estimation of parameters and standard errors [22].”
Coun, M. J. H., Ruiter, M. D., & Peters, P. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1183203 · Cited by 26
“053) based on the null model with restricted maximum likelihood estimation (Heck et al., 2014).”
MethodChang, Y. J., & Yoo, J. W. (2023) · Sustainability · doi:10.3390/su151813677 · Cited by 14
Advances in Latent Variable Measurement Modeling
Coulacoglou, C., & Saklofske, D. H. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-802219-1.00004-3 · Cited by 17
Diestelkamp, S., Wartberg, L., Kaess, M., Bauer, S., Rummel‐Kluge, C., Becker, K., Eschenbeck, H., Salize, H. J., Moessner, M., Baldus, C., et al. (2019) · Trials · doi:10.1186/s13063-018-3160-z · Cited by 14
Outplaying opponents—a differential perspective on passes using position data
Steiner, S., Rauh, S., Rumo, M., et al. (2019) · German Journal of Exercise and Sport Research · doi:10.1007/s12662-019-00579-0 · Cited by 14
Stuntz, C. P., Grosshans, M., Boghosian, R., Brendel, A., & Williamson, M. (2020) · Psychology of sport and exercise · doi:10.1016/j.psychsport.2020.101657 · Cited by 15
Adeel, A., Notteboom, B., Yasar, A.-U.-H., Scheerlinck, K., & Stevens, J. (2021) · Sustainability · doi:10.3390/su13137451 · Cited by 14
Re-examining the role of mood selection type in Spanish heritage speakers’ subjunctive production
Perez‐Cortes, S. (2021) · Linguistic Approaches to Bilingualism · doi:10.1075/lab.20111.per · Cited by 14
Ünsal, B. C., Demetrovics, Z., & Reinhardt, M. (2022) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-022-02385-w · Cited by 14
Cultural Differences in Values as Self-Guides
Cheung, W., Maio, G. R., Rees, K. J., Kamble, S. V., & Mane, S. R. (2016) · Personality and Social Psychology Bulletin · doi:10.1177/0146167216643932 · Cited by 14
The Effectiveness of a Portuguese Elementary School Social and Emotional Learning Program
Coelho, V. A., Sousa, V., & Figueira, A. P. C. (2016) · The Journal of Primary Prevention · doi:10.1007/s10935-016-0445-4 · Cited by 21
“Intraclass correlations showed that we did not need to include a 4th level (school grouping) in the models, because the explained variance at that level was not sufficient (\.05) to warrant it, as suggested by Heck, Thomas, and Tabata (2013).”
BackgroundSaksena, S., Tran, C. C., & Fox, J. (2018) · Energy Sustainable Development/Energy for sustainable development · doi:10.1016/j.esd.2018.03.001 · Cited by 14
O impacto da transição escolar para o 2.° ciclo sobre o autoconceito e a autoestima
Coelho, V. A., & Romão, A. M. (2017) · Revista de Psicodidáctica · doi:10.1016/j.psicod.2016.10.001 · Cited by 14
Schwarzer, R., Warner, L. M., Fleig, L., Gholami, M., Salvatore, S., Cianferotti, L., Ntzani, E., Román‐Viñas, B., Trichopoulou, A., & Brandi, M. L. (2017) · British Journal of Health Psychology · doi:10.1111/bjhp.12288 · Cited by 20
Smeijers, D., Bulten, E., Buitelaar, J. K., & Verkes, R. (2018) · International Journal of Offender Therapy and Comparative Criminology · doi:10.1177/0306624x17750340 · Cited by 16
Examining Perceptions of Teammates’ Burnout and Training Hours in Athlete Burnout
Appleby, R., Davis, P., Davis, L., & Gustafsson, H. (2018) · Journal of Clinical Sport Psychology · doi:10.1123/jcsp.2017-0037 · Cited by 14
Lebois, L. A. M., Wolff, J. D., Hill, S. B., Bigony, C. E., Winternitz, S., Ressler, K. J., & Kaufman, M. L. (2018) · Journal of Trauma & Dissociation · doi:10.1080/15299732.2018.1547807 · Cited by 16
Opie, G. M., Otieno, L. A., Pourmajidian, M., Semmler, J. G., & Sidhu, S. K. (2019) · Neuroscience · doi:10.1016/j.neuroscience.2019.11.022 · Cited by 14
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Effects of creativity on aesthetic experience7
Stojilović, I. Z. (2017) · Psihologija · doi:10.2298/psi1703319s · Cited by 13
Analysis of Treatment-Control Pre-Post-Follow-up Design Data
Sharpe, D., & Cribbie, R. A. (2023) · The Quantitative Methods for Psychology · doi:10.20982/tqmp.19.1.p025 · Cited by 21
Banik, A., Schwarzer, R., Pawlowska, I., Boberska, M., Cieślak, R., & Łuszczyńska, A. (2017) · Health and Quality of Life Outcomes · doi:10.1186/s12955-017-0645-5 · Cited by 14
“Using the SPSS 24 MIXED procedure, linear multilevel models were computed with maximum likelihood (ML) estimation [33] using family cancer history and gender as the main predictors.”
MethodMultiple Appearances of Parental Interactions and Math Achievement on TIMSS International Assessment
Shapira‐Lishchinsky, O., & Zavelevsky, E. (2019) · International Journal of Science and Mathematics Education · doi:10.1007/s10763-018-09949-w · Cited by 13
Barbosa, F., Matos, A. D., Voss, G., & Eiras, A. (2022) · Health & Social Care in the Community · doi:10.1111/hsc.13754 · Cited by 12
Dahlstrand, J., Fridolfsson, J., Arvidsson, D., Börjesson, M., Friberg, P., & Chen, Y. (2023) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2023.03.006 · Cited by 14
Benor, D. J., Rossiter-Thornton, J., & Toussaint, L. L. (2016) · Journal of Evidence-Based Complementary & Alternative Medicine · doi:10.1177/2156587216659400 · Cited by 13
Gerpott, T. J., May, S., & Nas, G. (2017) · Information & Management · doi:10.1016/j.im.2017.02.003 · Cited by 13
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Longitudinal Stress-Buffering Effects of Social Integration for Late-Life Functional Health
Toyama, M., & Fuller, H. R. (2019) · The International Journal of Aging and Human Development · doi:10.1177/0091415019871196 · Cited by 13
Selvi, K., Parling, T., Ljótsson, B., Welch, E., & Ghaderi, A. (2021) · Scandinavian Journal of Psychology · doi:10.1111/sjop.12684 · Cited by 12
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Ghasemi, F. (2022) · Psychotherapy Research · doi:10.1080/10503307.2022.2131476 · Cited by 11
The role of achievement emotions for text comprehension and metacomprehension
Prinz‐Weiß, A., Lukosiute, L., Meyer, M., & Riedel, J. (2022) · Metacognition and Learning · doi:10.1007/s11409-022-09331-w · Cited by 17
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Cultural Dimensions of Fake News Exposure: A Cross-National Analysis among European Union Countries
Arrese, Á. (2022) · Mass Communication & Society · doi:10.1080/15205436.2022.2123278 · Cited by 17
“…a random intercept (countries) showed that the mean levels of fake news exposure vary significantly among countries (likelihood ratio test [LRT] < .001, and intraclass correlation coefficient (ICC) = .053, in the limit of the .05 that researchers often use as a rough cutoff; Heck et al., 2014).”
Metadata
- Authors
- Heck, R. H.
- Corresponding author
- Heck, Ronald H.
- ORCID
- Heck, Ronald H.
- Venue
- Routledge eBooks
- Publisher
- Routledge
- Published
- 2013-08-22
- OpenAlex
- W4302348542
- Cited by
- 774
- References
- 0
- Citations by year
- 2026: 33 · 2025: 61 · 2024: 72 · 2023: 92 · 2022: 80 · 2021: 85 · 2020: 62 · 2019: 60 · 2018: 54 · 2017: 71 · 2016: 53 · 2015: 22 · 2014: 14 · 2013: 6
- FWCI
- 7.23
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Routledge eBooks (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- multilevel modeling, longitudinal modeling, growth trajectories, covariance structure, personal transformation, regression discontinuity design, quasi-experimental design, missing data, sampling weights, cross-classified data, multivariate outcomes, organizational change
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