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Interval Estimation of the Population Squared Multiple Correlation
Peer reviewed Peer reviewed
Pohlmann, John T.; Moore, James F. – Multiple Linear Regression Viewpoints, 1977
A technique is presented which applies the Neyman theory of confidence intervals to interval estimation of the squared multiple correlation coefficient. A computer program is presented which can be used to apply the technique. (Author/JKS)
Descriptors: Computer Programs, Correlation, Hypothesis Testing, Multiple Regression Analysis
A Note on the Calculation of Degrees of Freedom for Power Analysis Using Multiple Linear Regression Models.
Peer reviewed Peer reviewed
Newman, Isadore; Thomas, Jay – Multiple Linear Regression Viewpoints, 1979
Fifteen examples using different formulas for calculating degrees of freedom for power analysis of multiple regression designs worked out by Cohen are presented, along with a more general formula for calculating such degrees of freedom. (Author/JKS)
Descriptors: Hypothesis Testing, Mathematical Models, Multiple Regression Analysis, Power (Statistics)
Path Analysis from a Regression Perspective.
Peer reviewed Peer reviewed
Williams, John D. – Multiple Linear Regression Viewpoints, 1978
Path analysis is a data analytic technique for estimating the strengths of hypothesized relationships among a group of variables for a particular sample. Strategies for the use of path analysis are discussed in detail in this extensive article. (JKS)
Descriptors: Critical Path Method, Data Analysis, Hypothesis Testing, Mathematical Models
Multiple Comparisons in the Analysis of Covariance Using Multiple Linear Regression.
Peer reviewed Peer reviewed
Williams, John T. – Multiple Linear Regression Viewpoints, 1979
A process is described for multiple comparisons when covariates are involved in the analysis. The method can be accomplished with considerable ease whenever pairwise comparisons are involved. More complex contrasts require the use of full and restricted models of variance. (CTM)
Descriptors: Analysis of Covariance, Comparative Analysis, Hypothesis Testing, Multiple Regression Analysis
Contrasts with Unequal N By Multiple Linear Regression.
Peer reviewed Peer reviewed
Williams, John D. – Multiple Linear Regression Viewpoints, 1979
Some of the more simplified methods for contrasts with equal sample sizes in multiple regression analysis are shown to result in erroneous calculations when applied to data sets with unequal sample sizes. An alternative method is provided. (Author/JKS)
Descriptors: Analysis of Variance, Hypothesis Testing, Multiple Regression Analysis, Research Methodology
Increasing Power and Interpretability in Certain Repeated Measures Designs.
Peer reviewed Peer reviewed
Mouw, John T.; Nu, View – Multiple Linear Regression Viewpoints, 1979
Repeated measures designs often involve dichotomization of a continuous variable in order to be amenable to the analysis of variance nature of such designs. An alternative to that approach wherein the independent variable is kept continuous is presented. (JKS)
Descriptors: Analysis of Covariance, Analysis of Variance, Hypothesis Testing, Multiple Regression Analysis
Using Multiple Regression to Interpret Chi-Square Contingency Table Analysis.
Peer reviewed Peer reviewed
Leitner, Dennis W. – Multiple Linear Regression Viewpoints, 1979
This paper relates common statistics from contingency table analysis to the more familiar R squared terminology in order to better understand the strength of the relation implied. The method of coding contingency tables was shown, as well as how R squared related to phi, V, and chi squared. (Author/CTM)
Descriptors: Correlation, Expectancy Tables, Hypothesis Testing, Multiple Regression Analysis
Increasing Power and Interpretability in Certain Repeated Measures Designs.
Peer reviewed Peer reviewed
Mouw, John T.; Vu, Nu Viet – Multiple Linear Regression Viewpoints, 1979
Repeated measures designs often involve dichotomization of a continuous variable in order to be amenable to the analysis of variance nature of such designs. An alternative to that approach wherein the independent variable is kept continuous is presented. (JKS)
Descriptors: Analysis of Covariance, Analysis of Variance, Hypothesis Testing, Multiple Regression Analysis
A Demonstration of a Type VI Error: An Applied Research Problem.
Peer reviewed Peer reviewed
And Others; Roll, Steve – Multiple Linear Regression Viewpoints, 1979
A Type VI error results from inconsistency between the researchers' question of interest and the statistical procedures employed to analyze the data. An example of a research problem is analyzed to show the increase in statistical power resulting from improved research design, using multiple regression instead of analysis of variance. (CTM)
Descriptors: Analysis of Variance, Error Patterns, Higher Education, Hypothesis Testing