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CHAPTER 11

MANAGEMENT RESEARCH Third Edition, 2008 Prof. M. Easterby-Smith, Prof. R. Thorpe, Prof. Paul R. Jackson. CHAPTER 11. Multivariate Analysis. Learning Objectives. To identify what are the variables in a narrative statement of a research question.

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CHAPTER 11

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  1. MANAGEMENT RESEARCH Third Edition, 2008 Prof. M. Easterby-Smith, Prof. R. Thorpe, Prof. Paul R. Jackson CHAPTER 11 Multivariate Analysis

  2. Learning Objectives • To identify what are the variables in a narrative statement of a research question. • To frame research questions according to alternative forms of multivariate statistical models. • To turn a research question into a form that can be analysed statistically. • To identify the appropriate form of multivariate method for a specific research question.

  3. Structure of the chapter • Introduction – the domain of multivariate analysis • Multivariate analysis of measurement models • Multivariate analysis of causal models

  4. Dealing with inter-related influences • Simplification through design – select equal numbers within key sub-groups • Simplification through selecting sub-samples – restrict the sample on a key variable • Simplification through statistical control – use partial correlations • Multivariate analysis – use multiple predictor together

  5. Multivariate analysis of measurement models • Rationale • Structure of measurement models: • Common factors • Specific factors • Reliability – coefficient alpha • Analysis methods: • EFA – exploratory factor analysis • CFA – confirmatory factor analysis

  6. Multivariate analysis of causal models • Rationale • Turning a research proposition into a testable causal model • Analysis of causal models for observed variables • Analysis of observed and latent variables – structural equation modelling

  7. Turning a research proposition into a testable causal model • What are the elements in the narrative description of the study? • What are the dependent and predictor variables? • What is the model being tested?

  8. Analysis of causal models for observed variables • Multiple regression analysis – single continuous DV and one or more predictor variables (PVs) • Analysis of covariance – single DV and both continuous and category PVs • MANOVA / MANCOVA – multiple DVs and both continuous and category PVs • Logistic regression analysis - single category DV and one or more predictor variables (PVs)

  9. SEM - analysis of observed and latent variables • Latent and observed variables • Measurement model and structural model

  10. Steps in structural equation modelling • Define model hypotheses • Specify the model • Estimate model parameters • Evaluate the quality of the model • Consider alternative models

  11. Further Reading • Hair, J. F., Black, B., Babin, B., Anderson, R. E., and Tatham, R. L. (2005) Multivariate Data Analysis. Saddle River, NJ: Prentice Hall. • Tabachnick, B. G. and Fidell, L. S. (2006). Using Multivariate Statistics. 5th edition, Boston: Allyn & Bacon.

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