Longitudinal Research: The Theory, Design, and Analysis of Change:
TL;DR: The trade-offs among analytic strategies (repeated measures general linear model, random coefficient modeling, and latent growth modeling), circumstances in which such methods are most appropriate, and ways to analyze data when one is using each approach are discussed.
About: This article is published in Journal of Management.The article was published on 2010-01-01. It has received 1071 citations till now. The article focuses on the topics: Latent growth modeling & Structural equation modeling.
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Journal Article•
1,583 citations
TL;DR: It is found that human capital relates strongly to performance, especially when the human capital in question is not readily tradable in labor markets and when researchers use operational performance measures that are not subject to profit appropriation.
Abstract: Theory at both the micro and macro level predicts that investments in superior human capital generate better firm-level performance. However, human capital takes time and money to develop or acquire, which potentially offsets its positive benefits. Indeed, extant tests appear equivocal regarding its impact. To clarify what is known, we meta-analyzed effects drawn from 66 studies of the human capital-firm performance relationship and investigated 3 moderators suggested by resource-based theory. We found that human capital relates strongly to performance, especially when the human capital in question is not readily tradable in labor markets and when researchers use operational performance measures that are not subject to profit appropriation. Our results suggest that managers should invest in programs that increase and retain firm-specific human capital.
904 citations
Cites methods from "Longitudinal Research: The Theory, ..."
...Building on the guidance offered by Ployhart and Vandenberg (2010), we coded as “longitudinal” those studies wherein performance measures were taken after human capital measures....
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TL;DR: The too-much-of-a-good-thing effect (TMGT) as discussed by the authors is a meta-theoretical principle that suggests that antecedent variables widely accepted as leading to desirable consequences actually lead to negative outcomes.
716 citations
TL;DR: Increases in creative self-efficacy corresponded with increases in creative performance as well, and employees who experienced increased requirements for creativity in their jobs actually reported a decreased sense of efficaciousness for creative work.
Abstract: Building from an established framework of self-efficacy development, this study provides a longitudinal examination of the development of creative self-efficacy in an ongoing work context. Results show that increases in employee creative role identity and perceived creative expectation from supervisors over a 6-month time period were associated with enhanced sense of employee capacity for creative work. Contrary to what was expected, employees who experienced increased requirements for creativity in their jobs actually reported a decreased sense of efficaciousness for creative work. Results show that increases in creative self-efficacy corresponded with increases in creative performance as well.
687 citations
Cites background from "Longitudinal Research: The Theory, ..."
...With regard to response rate over time, Ployhart and Vandenberg (2010) noted that it is not uncommon for the response rate in field studies to drop by half or more between the first and last measurement occasions....
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TL;DR: Work-related well-being predicts general wellbeing in the long-term, and burnout predicts depressive symptoms and life dissatisfaction from T1 to T2 and from T2 to T3, even after adjusting for the impact of burnout.
686 citations
Cites methods from "Longitudinal Research: The Theory, ..."
...we assessed all study variables three times which is considered a prerequisite for a truly longitudinal study (Ployhart and Vandenberg, 2010)....
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...However, we assessed all study variables three times which is considered a prerequisite for a truly longitudinal study (Ployhart and Vandenberg, 2010)....
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References
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Book•
03 Mar 1992TL;DR: The Logic of Hierarchical Linear Models (LMLM) as discussed by the authors is a general framework for estimating and hypothesis testing for hierarchical linear models, and it has been used in many applications.
Abstract: Introduction The Logic of Hierarchical Linear Models Principles of Estimation and Hypothesis Testing for Hierarchical Linear Models An Illustration Applications in Organizational Research Applications in the Study of Individual Change Applications in Meta-Analysis and Other Cases Where Level-1 Variances are Known Three-Level Models Assessing the Adequacy of Hierarchical Models Technical Appendix
23,126 citations
TL;DR: This chapter discusses Hierarchical Linear Models in Applications, Applications in Organizational Research, and Applications in the Study of Individual Change Applications in Meta-Analysis and Other Cases Where Level-1 Variances are Known.
19,282 citations
"Longitudinal Research: The Theory, ..." refers background or methods in this paper
...Following the prior example of four repeated measures and a linear form of change, the simplest way to represent the RCM is with the notation of Raudenbush and Bryk (2002): (1) (2) (3) Level 1 models the intraunit change over time (e.g., the types of curves shown in Figure 1)....
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...Thus, the Level 1 equation models the within-unit variability, and the Level 2 equations model the between-unit variability in change (Raudenbush & Bryk, 2002)....
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...There are many sources describing RCM growth models, ranging from relatively nontechnical (Ployhart et al., 2002; Singer, 1998) to technical (Bliese & Ployhart, 2002; Raudenbush & Bryk, 2002; Singer & Willett, 2003)....
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Book•
01 Jan 1987
TL;DR: This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.
Abstract: Preface.PART I: OVERVIEW AND BASIC APPROACHES.Introduction.Missing Data in Experiments.Complete-Case and Available-Case Analysis, Including Weighting Methods.Single Imputation Methods.Estimation of Imputation Uncertainty.PART II: LIKELIHOOD-BASED APPROACHES TO THE ANALYSIS OF MISSING DATA.Theory of Inference Based on the Likelihood Function.Methods Based on Factoring the Likelihood, Ignoring the Missing-Data Mechanism.Maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse.Large-Sample Inference Based on Maximum Likelihood Estimates.Bayes and Multiple Imputation.PART III: LIKELIHOOD-BASED APPROACHES TO THE ANALYSIS OF MISSING DATA: APPLICATIONS TO SOME COMMON MODELS.Multivariate Normal Examples, Ignoring the Missing-Data Mechanism.Models for Robust Estimation.Models for Partially Classified Contingency Tables, Ignoring the Missing-Data Mechanism.Mixed Normal and Nonnormal Data with Missing Values, Ignoring the Missing-Data Mechanism.Nonignorable Missing-Data Models.References.Author Index.Subject Index.
18,201 citations
Book•
29 Mar 2013
TL;DR: Linear Mixed-Effects and Nonlinear Mixed-effects (NLME) models have been studied in the literature as mentioned in this paper, where the structure of grouped data has been used for fitting LME models.
Abstract: Linear Mixed-Effects * Theory and Computational Methods for LME Models * Structure of Grouped Data * Fitting LME Models * Extending the Basic LME Model * Nonlinear Mixed-Effects * Theory and Computational Methods for NLME Models * Fitting NLME Models
10,715 citations
TL;DR: 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI) are presented and may eventually extend the ML and MI methods that currently represent the state of the art.
Abstract: Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.
10,568 citations
"Longitudinal Research: The Theory, ..." refers background in this paper
...Should one desire to try various approaches for evaluating or imputing values for the missing data, we recommend readers consult several technical sources (Allison, 2001; Little & Rubin, 2002; Newman, 2003; Schafer & Graham, 2002)....
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