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Data Collection, Control, and Sample Size

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Structural Equation Models

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 22))

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Abstract

Models are only as good as the data that they analyze—“garbage in, garbage out.” Unfortunately, data-model duality and data-model fit are often overlooked in theory-driven research. This chapter explores the many considerations that are necessary for proper collection of data and credible conduct of research. The chapter covers important concepts in causality, data adequacy, resampling, screening, exploratory data analysis, the nature of latent constructs, and the role of data in research.

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References

  • Bentler, Peter M., and A.B. Mooijaart. 1989. “Choice of Structural Model via Parsimony: A Rationale Based on Precision.” Psychological Bulletin 106 (2): 315.

    Article  Google Scholar 

  • ———. 1989b. “A New Incremental Fit Index for General Structural Equation Models.” Sociological Methods & Research 17 (3): 303–316.

    Article  Google Scholar 

  • Boomsma, A. 1982. “The Robustness of LISREL Against Small Sample Sizes in Factor Analysis Models.” Systems Under Indirect Observation: Causality, Structure, Prediction 1: 149–173.

    Google Scholar 

  • Browne, Michael W., and Robert Cudeck. 1989. “Single Sample Cross-Validation Indices for Covariance Structures.” Multivariate Behavioral Research 24 (4): 445–455.

    Article  Google Scholar 

  • Cattell, Raymond B. 1966. “The Scree Test for the Number of Factors.” Multivariate Behavioral Research 1 (2): 245–276.

    Article  Google Scholar 

  • Cliff, Norman. 1988. The Eigenvalues-Greater-Than-One Rule and the Reliability of Components. Psychological Bulletin 103 (2): 276.

    Article  Google Scholar 

  • Copas, John B., and H.G. Li. 1997. “Inference for Non-random Samples.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 59 (1): 55–95.

    Article  MathSciNet  MATH  Google Scholar 

  • ———. 1974. Econometrics. Berlin: Springer.

    Google Scholar 

  • Ding, L., W.F. Velicer, and L.L. Harlow. 1995. “Effects of Estimation Methods, Number of Indicators Per Factor, and Improper Solutions on Structural Equation Modeling Fit Indices.” Structural Equation Modeling: A Multidisciplinary Journal 2 (2): 119–143.

    Article  Google Scholar 

  • Fisher, Ronald A. 1921. “On the Probable Error of a Coefficient of Correlation Deduced from a Small Sample.” Metron 1: 3–32.

    Google Scholar 

  • Fisher, Ronald A. 1935. The Logic of Inductive Inference. Journal of the Royal Statistical Society 98 (1): 39—82.

    Article  MATH  Google Scholar 

  • Goodhue, Dale L. 1995. “Understanding User Evaluations of Information Systems.” Management Science 41 (12): 1827–1844.

    Article  Google Scholar 

  • Goodhue, Dale L., and Ronald L. Thompson. 1995. “Task-Technology Fit and Individual Performance.” MIS Quarterly 19 (2): 213–236.

    Article  Google Scholar 

  • Goodhue, Dale, William Lewis, and Ron Thompson. 2006. “PLS, Small Sample Size, and Statistical Power in Mis Research.” In Proceedings of the 39th Annual Hawaii International Conference on System Sciences, 2006. Hicss’06. Vol. 8, 202b. Piscataway: IEEE.

    Google Scholar 

  • ———. 2012a. “Comparing PLS to Regression and Lisrel: A Response to Marcoulides, Chin, and Saunders.” Mis Quarterly 36 (3): 703–716.

    Google Scholar 

  • ———. 2012b. “Does PLS Have Advantages for Small Sample Size or Non-normal Data?” Mis Quarterly 36 (3): 981–1001.

    Google Scholar 

  • He, Qinying, and H.N. Nagaraja. 2011. “Correlation Estimation in Downton’s Bivariate Exponential Distribution Using Incomplete Samples.” Journal of Statistical Computation and Simulation 81 (5): 531–546.

    Article  MathSciNet  MATH  Google Scholar 

  • Jöreskog, Karl G., and Arthur S. Goldberger. 1975. “Estimation of a Model with Multiple Indicators and Multiple Causes of a Single Latent Variable.” Journal of the American Statistical Association 70 (351a): 631–639.

    Article  MathSciNet  MATH  Google Scholar 

  • Kaiser, Henry F. 1960. “The Application of Electronic Computers to Factor Analysis.” Educational and Psychological Measurement 20 (1): 141–151.

    Article  Google Scholar 

  • Kendall, Maurice, and J.D.R. Gibbons. 1990. Correlation Methods. Oxford: Oxford University Press.

    MATH  Google Scholar 

  • Leamer, Edward E. 1978. Specification Searches: Ad Hoc Inference with Nonexperimental Data. Vol. 53. New York: Wiley.

    MATH  Google Scholar 

  • Marsh, Herbert W., and Michael Bailey. 1991. “Confirmatory Factor Analyses of Multitrait-Multimethod Data: A Comparison of Alternative Models.” Applied Psychological Measurement 15 (1): 47–70.

    Article  Google Scholar 

  • Nakagawa, Shinichi, and Innes C. Cuthill. 2007. “Effect Size, Confidence Interval and Statistical Significance: A Practical Guide for Biologists.” Biological Reviews 82 (4): 591–605.

    Article  Google Scholar 

  • Nunnally, Jum C., Ira H. Bernstein, and others. 1967. Psychometric Theory. Vol. 226. New York: McGraw-Hill.

    Google Scholar 

  • Podsakoff, Philip M., and Dennis W. Organ. 1986. “Self-reports in Organizational Research: Problems and Prospects.” Journal of Management 12 (4): 531–544.

    Article  Google Scholar 

  • Raspe, Rudolf Erich. 2009. The Surprising Adventures of Baron Munchausen. Auckland: The Floating Press.

    Google Scholar 

  • Samuel, Mari Dominique Drouet Kotz, Dominique Drouet Mari, and Samuel Kotz. 2001. Correlation and Dependence. Singapore: World Scientific.

    Google Scholar 

  • Schechtman, Edna, and Shlomo Yitzhaki. 1999. “On the Proper Bounds of the Gini Correlation.” Economics Letters 63 (2): 133–138.

    Article  MathSciNet  MATH  Google Scholar 

  • Shevlyakov, Georgy L., and Nikita O. Vilchevski. 2002. “Minimax Variance Estimation of a Correlation Coefficient for ε-Contaminated Bivariate Normal Distributions.” Statistics & Probability Letters 57 (1): 91–100.

    Article  MathSciNet  MATH  Google Scholar 

  • Stuart, Alan, Maurice George Kendall, and John Keith Ord. 1991. Classical Inference and Relationship. Oxford: Oxford University Press.

    Google Scholar 

  • Tabachnick, Barbara G., Linda S. Fidell, and Jodie B. Ullman. 2007. Using Multivariate Statistics. Needham Heights: Allyn & Bacon/Pearson Education.

    Google Scholar 

  • Tanaka, Jeffrey S. 1987. “‘How Big Is Big Enough?’: Sample Size and Goodness of Fit in Structural Equation Models with Latent Variables.” Child Development 58: 134–146.

    Article  Google Scholar 

  • Westland, J. Christopher. 2010. “Lower Bounds on Sample Size in Structural Equation Modeling.” Electronic Commerce Research and Applications 9 (6): 476–487.

    Article  Google Scholar 

  • Wolfram, Stephen. 2002a. A New Kind of Science. Vol. 5. Champaign: Wolfram Media.

    Google Scholar 

  • Xu, Weichao, Y.S. Hung, Mahesan Niranjan, and Minfen Shen. 2010. “Asymptotic Mean and Variance of Gini Correlation for Bivariate Normal Samples.” IEEE Transactions on Signal Processing 58 (2): 522–534.

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Information & Decision Systems, University of Illinois at Chicago, Chicago, IL, USA

    J. Christopher Westland

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  1. J. Christopher Westland
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Westland, J.C. (2019). Data Collection, Control, and Sample Size. In: Structural Equation Models. Studies in Systems, Decision and Control, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-030-12508-0_5

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