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Validating scales and indexes

Here's an example of what they look like: Your reading intentions are also stored in your profile for future reference.Quantitative data analysis requires the construction of two types of measures of variables--indices and scales.The final step in constructing an index is validating it.Just like you need to validate each item that goes into the index, you also need to validate the index itself to make sure that it measures what it is intended to measure. One is called in which you examine the extent to which the index is related to the individual items that are included in it.To determine if your items are empirically related, crosstabulations, correlation coefficients, or both may be used.

Third, you need to decide how general or specific your variable will be.These measures are frequently used and are important since social scientists often study variables that possess no clear and unambiguous indicators--for instance, age or gender.Researchers often centralize much of work in regards to the attitudes and orientations of a group of people, which require several items to provide indication of the variables.That is, the item should measure what it is intended to measure.If you are constructing an index of religiosity, items such as church attendance and frequency of prayer would have face validity because they appear to offer some indication of religiosity.Another important indicator of an index’s validity is how well it accurately predicts related measures.For example, if you are measuring political conservatism, those who score the most conservative in your index should also score conservative in other questions included in the survey.To create one, you must select possible items, examine their empirical relationships, score the index, and validate it.The first step in creating an index is selecting the items you wish to include in the index to measure the variable of interest.After you have finalized the items you are including in your index, you then assign scores for particular responses, thereby making a composite variable out of your several items.For example, let’s say you are measuring religious ritual participation among Catholics and the items included in your index are church attendance, confession, communion, and daily prayer, each with a response choice of "yes, I regularly participate" or "no, I do not regularly participate." You might assign a 0 for "does not participate" and a 1 for "participates." Therefore, a respondent could receive a final composite score of 0, 1, 2, 3, or 4 with 0 being the least engaged in Catholic rituals and 4 being the most engaged.


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