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Tukey, J.W. (1958) Bias and Confidence in Not-Quite Large Sample. Annals of Mathematical Statistics, 29, 614.

has been cited by the following article:

  • TITLE: Analysis of the Invariance and Generalizability of Multiple Linear Regression Model Results Obtained from Maslach Burnout Scale through Jackknife Method

    AUTHORS: Tolga Zaman, Kamil Alakus

    KEYWORDS: Jackknife Method, Invariance, Generalizability, Maslach Burnout Scale, Multiple Linear Regression, Backward Elimination Method

    JOURNAL NAME: Open Journal of Statistics, Vol.5 No.7, December 11, 2015

    ABSTRACT: The purpose of this study was to examine the burnout levels of research assistants in Ondokuz Mayis University and to examine the results of multiple linear regression model based on the results obtained from Maslach Burnout Scale with Jackknife Method in terms of validity and generalizability. To do this, a questionnaire was given to 11 research assistants working at Ondokuz Mayis University and the burnout scores of this questionnaire were taken as the dependent variable of the multiple linear regression model. The variable of burnout was explained with the variables of age, weekly hours of classes taught, monthly average credit card debt, numbers of published articles and reports, gender, marital status, number of children and the departments of the research assistants. Dummy variables were assigned to the variables of gender, marital status, number of children and the departments of the research assistants and thus, they were made quantitative. The significance of the model as a result of multiple linear regressions was examined through backward elimination method. After this, for the five explanatory variables which influenced the variable of burnout, standardized model coefficients and coefficients of determination, and 95% confidence intervals of these values were estimated through Jackknife Method and the generalizability of the parameter estimation results of these variables on population was researched.