Irfan Ahmed, PhD

Ideally, the measurements that we take with a scale would always replicate perfectly. However, in the real world there are a number of external random factors that can affect the way that respondents provide answers to a scale. A particular measurement taken with the scale is therefore composed of two factors: the theoretical "true score" of the scale and the variation caused by random factors. Reliability is a measure of how much of the variability in the observed scores actually represents variability in the underlying true score. Reliability ranges from 0 to 1. In psychology it is preferred to have scales with reliability greater than 0.7. The reliability of a scale is heavily dependent on the number of items composing the scale. Even using items with poor internal consistency, you can get a reliable scale if your scale is long enough. For example, 10 items that have an average inter-item correlation of only .2 will produce a scale with a reliability of .714. Ho...

The chi-square test allows us to determine if the pairs of categorical variables in research   are related. But what if you want to test a model using two or more independent variables? Most of the inferential procedures we have discussed so far require that the dependent variable be a continuous variable. The most common inferential statistics in research such as t-tests, regression, and ANOVA, require that residuals have a normal distribution, and that the variance is equal across conditions. Both of these assumptions are likely to be seriously violated if the dependent variable is categorical. The answer is to use logistic regression, which does not make these assumptions and so can be used to determine the ability of a set of continuous or categorical independent variables to predict the value of a categorical dependent variable. However, standard logistic regression assumes that all of your observations are independent, so it cannot be directly used to test within-subject...

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A chi-square is a nonparametric test used to determine if there is a relationship between two categorical variables. Let.s take a simple example. Suppose a researcher brought male and female participants into the lab and asked them which color they prefer.blue or green. The researcher believes that color preference may be related to gender. Notice that both gender (male, female) and color preference (blue, green) are categorical variables. If there is a relationship between gender and color preference, we would expect that the proportion of men who prefer blue would be different than the proportion of women who prefer blue. In general, you have a relationship between two categorical variables when the distribution of people across the categories of the first variable changes across the different categories of the second variable. To determine if a relationship exists between gender and color preference, the chi-square test computes the distributions across the combination of your two ...