How to Construct an Index for Research

How to Construct an Index for Research An index is a composite measure of variables, or a way of measuring a constructlike religiosity or racismusing more than one data item. An index is an accumulation of scores from a variety of individual items. To create one, you must select possible items, examine their empirical relationships, score the index, and validate it. Item Selection The first step in creating an index is selecting the items you wish to include in the index to measure the variable of interest. There are several things to consider when selecting the items. First, you should select items that have face validity. 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. A second criterion for choosing which items to include in your index is unidimensionality. That is, each item should represent only one dimension of the concept you are measuring. For example, items reflecting depression should not be included in items measuring anxiety, even though the two might be related to one another. Third, you need to decide how general or specific your variable will be. For example, if you only wish to measure a specific aspect of religiosity, such as ritual participation, then you would only want to include items that measure ritual participation, such as church attendance, confession, communion, etc. If you are measuring religiosity in a more general way, however, you would want to also include a more balanced set of items that touch on other areas of religion (such as beliefs, knowledge, etc.). Lastly, when choosing which items to include in your index, you should pay attention to the amount of variance that each item provides. For example, if an item is intended to measure religious conservatism, you need to pay attention to what proportion of respondents would be identified as religiously conservative by that measure. If the item identifies nobody as religiously conservative or everyone as a religiously conservative, then the item has no variance and it is not a useful item for your index. Examining Empirical Relationships The second step in index construction is to examine the empirical relationships among the items you wish to include in the index. An empirical relationship is when respondents’ answers to one question help us predict how they will answer other questions. If two items are empirically related to each other, we can argue that both items reflect the same concept and we can, therefore, include them in the same index. To determine if your items are empirically related, crosstabulations, correlation coefficients, or both may be used. Index Scoring The third step in index construction is scoring the index. 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. Index Validation 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. There are several methods for doing this. One is called item analysis in which you examine the extent to which the index is related to the individual items that are included in it. 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.