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STRUCTURAL EQUATION MODELING AND META-ANALYSIS: A MARRIAGE MADE IN. Ronald S. Landis Illinois Institute of Technology. 1+1=??. Peanut Butter & Chocolate Food & Wine Movies LeBron James & Dwayne Wade The Whole is More, Equal to, or Less than the Sum of Its Parts. Plan for Today.

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Structural equation modeling and meta analysis a marriage made in

STRUCTURAL EQUATION MODELING AND META-ANALYSIS:A MARRIAGE MADE IN ...

Ronald S. Landis

Illinois Institute of Technology


1+1=??

  • Peanut Butter & Chocolate

  • Food & Wine

  • Movies

  • LeBron James & Dwayne Wade

  • The Whole is More, Equal to, or Less than the Sum of Its Parts


Plan for today
Plan for Today

  • Meta-Analysis (MA) FAQ

  • Structural Equation Modeling (SEM) FAQ

  • MASEM

    • Research Questions & Focus

    • Steps/Decisions/Frustrations

  • Odds & Ends


Ma faq
MA FAQ

  • Research Questions

    • Summarize available (published & unpublished) evidence

    • Estimate degree of relation between variables

    • Identify conditions in which effect differs


Ma faq1
MA FAQ

  • Foundational Issues & Pragmatic Concerns

    • Starting with a clear purpose

      • Why not just collect and code everything?

      • “HARK”ing

    • Nature of available evidence

      • Samples, research designs, measures

    • When is there enough information?

      • k’s and n’s

    • Adequate construct development

      • What am I looking for and how do I know if I have found it?

    • File Drawers

      • What if I’m missing something?


Sem faq
SEM FAQ

  • Research Questions

    • To what degree are a set of indicators associated with a given set of latent variables (CFA)?

    • Are hypothesized causal relations between variables consistent with collected data (PA/Full SEM)?

    • Are there systematic patterns of change across time (LCM)?

      All require a priori specification of a particular model (or set of models)


Sem faq1
SEM FAQ

  • Foundational Issues & Pragmatic Concerns

    • Starting with a clear purpose

      • Why not just collect some data and start testing models?

      • “HARK”ing

    • Nature of available evidence

      • Samples, research designs, measures

    • When is there enough information?

      • n’s and identification

    • Adequate construct development

      • What am I looking for and how do I know if I have found it?

    • Everything relevant is included

      • What if I have omitted some important variables?


Masem
MASEM

  • Possible Combinations

    • SEM used for MA (Cheung, 2008)

    • MA as input for SEM [TODAY]

  • Technical/Practical Focus

    • What are the underlying mathematical/statistical elements?

    • How do I undertake these analyses in my own work? [TODAY]

  • Primary Questions

    • Can I Integrate SEM and MA?

      • YES

    • How Do I Integrate SEM and MA?

      • HOPEFULLY A CLEARER UNDERSTANDING

    • Should I Integrate SEM and MA?

      • THE $64,000 QUESTION – IT DEPENDS


General steps proposed by viswesvaran ones 1995
General Steps Proposed by Viswesvaran & Ones (1995)

  • ID Core Constructs and Relations

  • ID Measures Used to Assess Each Construct

  • Obtain Relevant Studies

  • Conduct Meta-Analysis

  • Test Measurement Model

  • Estimate Construct Correlations

  • Apply SEM to Test Specified Relations


Two stage structural equation modeling tssem proposed by cheung chan 2005
Two-Stage Structural Equation Modeling (TSSEM)Proposed by Cheung & Chan (2005)

STEP 1

  • Test for Homogeneity of Correlations/Covariances

    • Using multiple groups CFA

  • Calculate Pooled Correlation/Covariance Matrix

  • Create Asymptotic Covariance Matrix

    STEP 2

  • Apply SEM to Test Specified Relations

    BENEFIT

  • Accounts for second order sampling error, leading to more accurate chi-square and fit indices


General approach model testing
General Approach – Model Testing

  • Confirmatory Factor Analysis

    • Same measures/indicators across studies

    • Covariances

  • Path/Structural Models

    • Different measures/indicators across studies

    • Correlations


Logic
Logic

  • First Step

    • Test of homogeneity

    • Build input matrix

  • Second Step

    • Model testing


An example earnest allen landis 2011
An Example –Earnest, Allen, & Landis (2011)

  • Student milestone project

  • Interest in Realistic Job Previews (RJPs)

  • How do we build on current MA’s?

    • Increased number of studies

    • Increased number of moderators

  • Conducted Meta-Analysis

  • Consideration of potential mediators via SEM



Example matrix
Example Matrix

Construct the correlation matrix


Issues in constructing the correlation matrix
Issues in Constructing the Correlation Matrix

  • What about blank cells?

    • Listwise deletion?

    • Previous meta-analyses

    • Studies included in current meta-analysis

    • Meta-analytic values from ‘outside’ studies

  • Different sample sizes in cells

    • Which one do we use?

  • Illogical values/npd matrices

    • What if my matrix can’t occur?

  • Presence of moderators

    • What do I do if I have heterogeneity in the correlation matrices?

  • Correlations instead of covariances

    • Can I still run SEM?


Odds ends
Odds & Ends

  • But…

    • Causality and MASEM make strange bedfellows

      • Yes, but this is not unique to MASEM

    • Lack of comfort with combining r’s from different studies into SEM analysis

      • Why?

    • What if latent variables differ across primary studies

      • Problem with MA, not MASEM


A few references
A Few References

  • Burke, M.J. & Landis, R.S. (2003). Methodological and conceptual challenges in conducting and interpreting meta-analyses. In K.R. Murphy (Ed.), Validity Generalization: A Critical Review (pp. 287-309). Mahwah, NJ: Erlbaum.

  • Cheung, M.W.L. (2009). TSSEM: A LISREL syntax generator for two-stage structural equation modeling (Version 1.11) [Computer software and manual]. Retrieved from http://courses.nus.edu.sq/course/psycwlm/internet/tssem.zip.

  • Cheung, M.W.L. & Chan, W. (2005). Meta-analytic structural equation modeling: A two-stage approach. Psychological Methods, 10, 40-64.

  • Cheung, M.W.L. & Chan, W. (2009). A two-stage approach to synthesizing covariance matrices in meta-analytic structural equation modeling. Structural Equation Modeling, 16, 28-53.

  • Christian, M. S., Bradley, J.C., Wallace, J.C., & Burke, M.J. (2009). Workplace safety: A meta-analysis of the roles of person and situation factors. Journal of Applied Psychology, 94, 1103-1127.


A few references1
A Few References

  • Colquitt, J. A., LePine, J. A., & Noe, R. A., toward an integrative theory of training motivation: A meta-analytic path analysis of 20 years of research. Journal of Applied Psychology, 85, 678-707.

  • Earnest, D.R., Allen, D.G., & Landis, R.S. (2011). A meta-analytic path analysis of the mechanisms linking realistic job previews and turnover. Personnel Psychology, 64, 865-897.

  • Klein, H.J., Wesson, M.J., Hollenbeck, J.R., Wright, P.M., & DeShon, R.P. (2001). The assessment of goal commitment: A measurement model meta-analysis. Organizational Behavior and Human Decision Processes, 85, 32-55.

  • Robbins, S. B., Oh, I., Le, H., Button, C. (2009). Intervention effects on college performance as mediated by motivational, emotional, and social control factors: integrated meta-analytic path analyses. Journal of Applied Psychology, 94, 1163-1184.

  • Viswesvaran, C. & Ones, D.S. (1995). Theory testing: Combining psychometric meta-analysis and structural equations modeling. Personnel Psychology, 48, 865-885.


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