Multilevel and Longitudinal Modeling with IBM SPSS

By R. Heck, S. Thomas, L. Tabata. 2010

1,127 citations recorded by the source corpus.

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. Ideal as a supplementary text for graduate courses on multilevel and longitudinal modeling, multivariate statistics, and research design taught in education, psychology, business, and sociology, this books practical approach also appeals to researchers in these fields. The book provides an excellent supplement to Heck & Thomass An Introduction to Multilevel Modeling Techniques, 2nd Edition; however, it can also be used with any multilevel and/or longitudinal modeling book or as a stand-alone text.

Subfields: Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/af36ae06e03609fbe7521c503e13ded3be31913d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

Citations in the corpus

State Authenticity in Everyday Life (2016, methodology): “Still, we chose this covariance structure rather than an unstructured matrix in the interest of parsimony and power (Dedrick et al., 2009; Heck et al., 2010).”
Implementing Performance Appraisal: Exploring the Employee Experience (2013, background): “Much extant literature treats employees as if they are independent of the broader organizational climate, which ignores the complexity of the clustered nature of individuals in higher-level groupings (Heck et al., 2010).”
Students' level of boredom, boredom coping strategies, epistemic curiosity, and graded performance (2016, background): “The ICC refers to “the proportion of variance that is common to each unit” (Heck et al., 2010, p. 74).”
Mediation of job embeddedness and satisfaction in the relationship between task characteristics and turnover: A multilevel study in Portuguese hotels (2017, methodology): “------ INSERT TABLE 1 ABOUT HERE ------ Methodological strategy and data aggregation Due to the multilevel characteristics of the data, we used SPSS 20.0 with hierarchical linear modeling (Heck et al., 2010).”
Teacher and school determinants of teacher job satisfaction: a multilevel analysis (2020, positive): “In order to study teacher job satisfaction, it is, therefore, relevant to partition the variance of predictors across hierarchical levels and to specify the level of each predictor (Heck et al., 2010).”
Situational job characteristics and job satisfaction: The moderating role of national culture (2015, background): “This even contradicts arguments found in the literature: in masculine cultures, job security was assumed to be less important since, according to Hofstede’s definition, in societies low in masculinity, individuals prefer having the security to work for their firm for as long as they want to. This pr”
Opportunity Evaluation as Rule‐Based Decision Making (2014, methodology): “Hierarchical Linear Modelling (HLM) is an analytic technique designed for modelling multi-level effects (Heck et al., 2010) and has been widely used in published conjoint studies in entrepreneurship (Haynie et al.”
Making it personal: : Opportunity individuation and the shaping of opportunity beliefs (2014, methodology): “Hierarchical linear modeling (HLM) has proven a worthy analytic technique for modeling such effects (Heck et al., 2010), and has been widely used in published conjoint studies in entrepreneurship (e.”
Influence of contextual variables on styles of play in soccer (2018, methodology): “ML estimation was employed for model comparison as chi-square likelihood ratio tests requires this type of estimation (Field, 2013; Heck et al., 2014).”