Max Gerber] I am often asked whether I agree with the new group selectionists, and the questioners are always surprised when I say I do not. After all, group selection sounds like a reasonable extension of evolutionary theory and a plausible explanation of the social nature of humans.

Here are my own thoughts on this matter. Over the years I have become increasingly uncomfortable with the way many people use the terms "independent variable" and "dependent variable. Researchers commonly associate the term "independent variable" with "cause" and "dependent variable" with "effect.

Let me give you an example. Several years ago I was assisting a doctoral student with his dissertation. He was investigating the correlates of a single outcome variable which was treated as continuous.

Among his predictor variables were several categorical variables. All of the variables were derived from items on a survey given to students at Florida State. No variables were manipulated. I dummy coded the categorical variables and threw them, along with some continuous predictors, into a multiple regression.

A week or so after I sent the results to the doctoral student, he wrote back and told me that the dissertation director wanted the categorical predictors analyzed by ANOVA, not by multiple regression as if there were a difference.

Well, believe it or not, the response I got was that if we did the analysis as an ANOVA, then those predictors would be "independent variables" and we could conclude that they CAUSE changes in the "dependent variable.

I ended up telling my client to simply change the phrase "multiple regression" to "ANOVA" to make the director happy. Is this incident an anomaly? Regretfully not, in my experience. I can't count the number of times that Ph.

On a few occasions I have demonstrated to such persons that the pooled variances independent samples t test is absolutely equivalent to the t used to test the significance of the point biserial r. Their jaws really dropped.

I now advise my students that others will tend to use the term "independent variable" with variables that are manipulated rather than observed, or known rather than predicted, or measured earlier in time rather than later if we predict high school grades from college grades, which variable is "independent?

I explain that the use of the terms "independent variable" and "dependent variable" in nonexperimental research can cause confusion, but confess to doing it myself but I am trying to stop this bad habit.

I suggest alternative terms such as "predictor variable," "factor," in ANOVA, but the American Psychological Association does not like that use"grouping variable," "classification variable," "criterion variable," "outcome variable," and "response variable.

Use the terms "independent variable" and "dependent variable" only with experimental research.

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With nonexperimental research use "predictor variable" and "criterion variable. The way the terms "independent variable" and "dependent variable" are used these days causes much confusion and some mischief.

Being fastidious about mere vocabulary is unlikely to help. Here are the responses I received, unedited, except for my use of bold font. In my intro stats courses, I begin with a discussion of kinds of research as the context in which statistics are used.

I use different names for variables in observational research vs. I tell them that true experiments or "experiments," so that they won't think there's a difference are characterized by random assignment of participants to groups and manipulation of the different groups' experiences.Pearson Correlation Coefficient Handout Essay Understanding the Pearson Correlation Coefficient (r) The Pearson product - moment correlation coefficient (r) assesses the degree that quantitative variables are linearly related in a sample.

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Pearson Product Moment Correlation The Pearson Product Moment Correlation is a parametric test that makes several assumptions concerning the . Box and Cox () developed the transformation. Estimation of any Box-Cox parameters is by maximum likelihood. Box and Cox () offered an example in which the data had the form of survival times but the underlying biological structure was of hazard rates, and the transformation identified this.

Methodology for Chapter 4: Pearson Correlation and Descriptive Statistics In Chapter 4, the Pearson correlation was utilized to find the degree to which the following pairs of variables are linearly associated: (a) GDP and Internet usage, GDP and mobile phone usage and GDP and fixed phone usage (b) population and Internet usage in Middle East, (c) population and mobile usage in Middle East, and (d) .

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