The main data analysis techniques used in research in social sciences and leisure

If you are going to do research on leisure or social sciences, here are the main data analysis techniques you can use:

– Chi-square test. This test, represented by the symbol X2, is used to show the relationship between two nominal variables, which are variables that describe something, such as a person’s gender or age. This test is designed to show whether or not the relationship is significant, and if so, the null hypothesis of no difference will be rejected. The test is done by examining the counts or percentages in the cells of a table and comparing the actual counts to the expected count that would occur if there were no difference according to the null hypothesis, such as if there were an equal number of people from two different racial groups. in a study of participation in two different leisure activities. One would expect the same number of members of different racial groups in each activity if there is no difference, but if one activity is more popular with one group and the other activity is more popular with the other group, then there would be a difference. The Chi-Square test consists of adding the differences between the counts or percentages and the expected counts or percentages, so that the greater the total, the greater the Chi-square value. In other words, this value results from adding the squared values ​​of the differences.

– T test. This test consists of comparing two means to determine if the differences between them are significant, by rejecting the null hypothesis that there is no difference and accepting the alternative hypothesis that there is a difference. For example, the test could look at the average income of people who participate in different recreational activities, such as golf versus bowling, to see if there is a difference between them, which might be expected, since golf is a fairly expensive sport, while that bowling is a relatively cheap sport. The test can be used as a paired-samples test or as an independent-samples test. In the paired samples test, the means of two variables are compared, such as two different activities for everyone in the full sample, such as the amount of time they spend on the Internet and the amount of time they watch television. In contrast, the independent samples test compares the means of two sample subgroups on a single variable to see if there are differences between them, such as the time teens and their parents spend online.

– One-way analysis of variance or ANOVA test. This test is used to compare more than two means in a single test, such as comparing the means of men and women in participating in a variety of activities, such as eating out, spending time on the Internet, watching television, shopping, participating in an active sport, or going to spectator sports. The test examines whether the mean of each variable in the test is different from the overall mean, which is the alternative hypothesis, or is the same as the overall mean, which is the null hypothesis. The test not only considers the differences between the mean for the general population and for the different subgroups, but it also considers the differences that occur between the means, which is called “variance”. This variance is determined by adding the differences between the individual means and the overall mean to obtain the results that are interpreted in this way. The higher the between-group variance, the more likely there is a significant between-group difference, while the higher the within-group variance, the less likely there is a significant between-group difference. The F-score represents the analysis of these two variance difference measures to show the relationship between the two types of variance: the between-group variance and the within-group variance. In addition, the number of groups and the size of the samples must be taken into account, which determine the degrees of freedom for that particular test. The result of these calculations produces an F-score, and the lower the F-score, the more likely it is that there is a significant difference between the group means.

– Factor analysis of variance. This is another ANOVA test, which is based on analyzing the means of more than a single variable, such as examining the relationship between participating in an activity and the gender and age of the participants. In effect, this test involves cross-tabulating the means of different groups to determine if they are significant by comparing both the group means and the degree of dispersion between the groups. Therefore, degrees of freedom are also taken into account in this test along with the sum of squares to produce a mean square and then an F-score. Again, the lower the score, the greater the probability of a significant difference between the means of the groups.

– Correlation coefficient (usually designated by “r”). This coefficient goes from 0 when there is no correlation to +1 if the correlation between two variables is perfect and positive or -1 if the correlation between the variables is perfect and negative. Numbers between 0 and +1 or -1 indicate the degree of positive or negative correlation between the variables. The size of r is determined by taking the mean of each variable and examining how far each data point is on the x and y axes from the mean in a positive or negative connection. The two differences are then multiplied and sample size is taken into account to determine how significant r is at a predetermined significance level (usually the 95% or 5% level).

– Linear regression. This approach is used when there is a sufficiently consistent correlation between two variables that a researcher can predict one variable knowing the other. (Veal, p. 358). To this end, a researcher creates a model of this relationship by developing an equation that states what this relationship is. This equation is usually stated as y = a + bx, where “a” is a constant and “b” refers to the slope of the line that best indicates the fit or correlation between the two variables being measured. .

– Non-linear regression. This refers to a situation that occurs when two variables are not linearly related, so a single straight line cannot be used to express their relationship. Such a nonlinear regression could occur if there is a curved relationship, such as when there is a gradual growth of interest in an activity, followed by a burst of enthusiasm, and then a plateau of interest. Another example might be a bimodal distribution or a cyclical relationship, such as when there is a pattern of interest in an activity twice a year or growth of interest up and down, such as when there is a spike in interest after the introduction of a new program several times a year, followed by a decline in interest until a new program is reintroduced.