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Showing posts with the label statistical differences

Invariance Testing

  Invariance Testing in Psychology Invariance testing is a statistical technique to assist researchers in determining the degree of comparability of a measure, which has been used with different groups. When a measure has been modified, translated, or used with people in various cultures, invariance testing can help determine if the same construct is being measured by the changes to the original measure and how people in different groups may understand the items. Invariance testing is important to ensure a measure functions in the same way (measures the same concept) in different groups. Hypothetical Example : A 16-item measure of forgiveness may have been originally written in American English and tested with college samples. The items are translated into four different languages and administered in ten different locations. One thing a researcher can do is examine the psychometric properties in the different samples. They may also consider correlations with other measures.  Anot

Independent Samples t-test

Independent Samples t- test Researchers use the independent samples t -test to find out if there is a significant difference between two sets of data. In the behavioral sciences, the data are often two sets of scores on tests or survey items. Significance can mean a lot of different things. In behavioral science, it is common to think of significance as a frequently occurring, and thus reliable, difference. Sometimes the language of statistics can be confusing. The independent sample t -test evaluates the differences between the arithmetic mean s of the two groups of scores, and assumes the scores are normally distributed . Usually, a difference needs to be at least large enough that a score difference as large, or larger than the one obtained, occurs only 5% of the time by chance. The calculations are usually done in spreadsheets like Excel or Google Sheets or in a program like SPSS . See the link below for a download about how to calculate a t-test. You wil