In the social sciences, scaling is the process of measuring or ordering entities with respect to quantitative attributes or traits. For example, a scaling technique might involve estimating individuals' levels of extraversion, or the perceived quality of products. Certain methods of scaling permit estimation of magnitudes on a continuum, while other methods provide only for relative ordering of the entities. The level of measurement is the type of data that is measured. The word scale, including in academic literature, is sometimes used to refer to another composite measure, that of an index. Those concepts are however different.
Scale construction decisions
What level (level of measurement) of data is involved (nominal, ordinal, interval, or ratio)? What will the results be used for? What should be used - a scale, index, or typology? What types of statistical analysis would be useful? Choose to use a comparative scale or a non-comparative scale. How many scale divisions or categories should be used (1 to 10; 1 to 7; −3 to +3)? Should there be an odd or even number of divisions? (Odd gives neutral center value; even forces respondents to take a non-neutral position.) What should the nature and descriptiveness of the scale labels be? What should the physical form or layout of the scale be? (graphic, simple linear, vertical, horizontal) Should a response be forced or be left optional?
Scale construction method
Scales constructed should be representative of the construct that it intends to measure. It is possible that something similar to the scale a person intends to create will already exist, so including those scale(s) and possible dependent variables in one's survey may increase validity of one's scale.
Begin by generating at least ten items to represent each of the sub-scales. Administer the survey; the more representative and larger the sample, the more credibility one will have in the scales. Review the means and standard deviations for the items, dropping any items with skewed means or very low variance. Run an exploratory factor analysis with oblique rotation on items for the scales - it is important to differentiate them based on their loading on factors to create sub-scales that represents the construct. Request factors with eigenvalues (for calculating eigenvalue for each factor square the factor loading's and sum down the columns) greater than 1. It is easier to group the items by targeted scales. The more distinct the other items, the better the chances the items will load better in one's own scale. “Cleanly loaded items” are those items that load at least .40 on one factor and more than .10 greater on that factor than on any others. Identify those in the factor pattern. “Cross loaded items” are those that do not meet the above criterion. These are candidates to drop. Identify factors with only a few items that do not represent clear concepts, these are “uninterpretable scales.” Also identify any factors with only one item. These factors and their items are candidates to drop. Look at the candidates to drop and the factors to be dropped. Is there anything that needs to be retained because it is critical to one's construct. For example, if a conceptually important item only cross loads on a factor to be dropped, it is good to keep it for the next round. Drop the items, and run a confirmatory factor analysis asking the program to give only the number of factors after dropping the uninterpretable and single-item ones. Go through the process again starting at Step 3. Here various test reliability measures could also be taken. Keep running through the process until one get “clean factors” (until all factors have cleanly loaded items). Run the Alpha in the statistical program with the aim of obtaining a .70 reliability score (internal consistency), and request the Alphas if each item is dropped. Any scales with insufficient Alphas should be dropped, and the process should be repeated from Step 3. Remember that Alphas are not proof of scale quality or content validity. [Coefficient alpha=number of items2 x average correlation between different items/sum of all correlations in the correlation matrix (including the diagonal values)] Run correlational or regressional statistics to ensure the validity of the scale. For better practices, keep the final factors and all loadings of yours and similar scales selected in the Appendix of the created scale.
Multi-Item and Single-Item Scales In most practical situations, multi-item scales are more effective in predicting outcomes compared to single items. The use of single-item measures in research is advised cautiously, their use should be limited to specific circumstances.
Table: Criteria for Assessing the Potential Use of Single-Item Measures
Data types
The type of information collected can influence scale construction. Different types of information are measured in different ways.
Some data are measured at the nominal level. That is, any numbers used are mere labels; they express no mathematical properties. Examples are SKU inventory codes and UPC bar codes. Some data are measured at the ordinal level. Numbers indicate the relative position of items, but not the magnitude of difference. An example is a preference ranking. Some data are measured at the interval level. Numbers indicate the magnitude of difference between items, but there is no absolute zero point. Examples are attitude scales and opinion scales. Some data are measured at the ratio level. Numbers indicate magnitude of difference and there is a fixed zero point. Ratios can be calculated. Examples include: age, income, price, costs, sales revenue, sales volume, and market share.
Composite measures
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