Demographic Data and Demonstrations of Difference

Abstract

An article in this issue, entitled Executive Function and Behavioral Problems in Students with Visual Impairments at Mainstream and Special Schools, by Heyl and Hintermair, offers a good example of how statistics should be used to illustrate differences in data that might, without the guidance of statistics, be given too much or not enough consideration by readers. In Table 1 of this article, the authors list several demographic variables (measures that describe the participants who took part in the study). It is very useful for authors to provide such a table that includes information about means and standard deviations--not only for descriptive characteristics of their participants, but also for their experimental measures. Providing such information offers readers a sense of the overall scope of the data and sometimes reveals general patterns in the data. Since means and standard deviations are generally the building blocks upon which further statistical testing is based, such information can also offer a way to analyze whether the statistical results are reported accurately and what the clinical or practical meaning of the statistical testing might be. A useful preliminary analysis of data, before the primary statistical testing is performed, is to look at reported demographic variables to see whether there are any important ways in which the participants varied as individuals within the group. Finding such differences might have an effect on how later analyses are conducted or even whether the results are meaningful at all. What Heyl and Hintermair have done in Table 1 is to present the demographic variables they consider might potentially impact the outcomes of the study and judge whether these variables are significantly different for the two main conditions of their study (mainstream schools and special schools) by conducting a [chi square] (pronounced chi square) test on these variables that looks at the frequencies of categorical variables. Any significant differences identified in Table 1 would then have to be taken into account in further analyses or an explanation would have to be offered as to why the differences would not impact the outcomes significantly. When a variable is made of categories (like cold, warm, and hot as opposed to numerical values for temperatures), the [chi square] test compares how well the frequency of occurrence for each of the categories of one variable are similer to the categories of another variable. …