A Handbook for Data Analysis in the Behavioral Sciences: Statistical Issues

A Handbook for Data Analysis in the Behavioral Sciences: Statistical Issues

A Handbook for Data Analysis in the Behavioral Sciences: Statistical Issues

A Handbook for Data Analysis in the Behavioral Sciences: Statistical Issues

Synopsis

Statistical methodology is often conceived by social scientists in a technical manner; they use it for support rather than for illumination. This two-volume set attempts to provide some partial remedy to the problems that have led to this state of affairs. Both traditional issues, such as analysis of variance and the general linear model, as well as more novel methods like exploratory data analysis, are included. The editors aim to provide an updated survey on different aspects of empirical research and data analysis, facilitate the understanding of the internal logic underlying different methods, and provide novel and broader perspectives beyond what is usually covered in traditional curricula.

Excerpt

Science is supposed to be an ever changing enterprise. Yet "change is not made without inconvenience, even from worse to better" (quoted by Johnson in the preface to the English Dictionary). This inherent resistance to change may account (at least partly) for the recent claim made by Aiken,West,Sechrest, and Reno (1990) that statistical and methodological training of psychologists has barely advanced during the past 20 years. Their conclusions are based, among other things, on a survey conducted in close to 200 psychology departments in North America, and are further supported by examining the leading psychological journals from which it is apparent that the methodology and methods of data analysis have hardly changed. For instance, the conventional null hypothesis testing remains by far the most common and preferred method for analyzing empirical data. The continuous and growing number of articles that appeared in methodological- and statistical-oriented journals such as the Psychological Bulletin point out the pitfalls of null hypotheses testing (see chapter 16 of the Methodological Issues volume) and offer some remedies or alternative methods of data analysis, but apparently had little impact (see chapter 11 of Methodological Issues volume for a more elaborated discussion).

The lack of change is further accompanied by some misunderstandings of the use of statistical tools. Apparently, people are poor intuitive statisticians, (chapters 12 and 13 of the Methodological Issues volume), and even social scientists have been shown to possess some fundamental misunderstandings regarding statistical theory. Unfortunately, statistical methodology is often conceived by social scientists in a technical manner, and its utilization resembles a drunken man's use of lamp posts: for support rather than for illumination. There are several causes that have led to this state of affairs and a few of these are mentioned here.

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