By Kenneth S. Shultz
Size conception in motion: Case reviews and Exercises comprises twenty modules, every one equivalent to whole chapters in commonplace size concept texts. The modules start with introductory recommendations and a overview of facts; growth via conceptions of content material, criterion-related, and build validation, validity generalization and test-bias; and finish with extra complicated issues equivalent to a number of regression and merchandise reaction thought (IRT). each one module consists of an summary, case reviews, routines, web references, and steered additional readings. An wide word list of keyword phrases is additionally supplied for fast reference. An Instructor's assets CD containing workouts and assignments is additionally on hand.
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Extra info for Measurement Theory in Action: Case Studies and Exercises
As a result, we are very limited in the types of data manipulations and statistical analyses we can perform on the data. , category), but not much else. , we have quantitative data), then we could actually scale our construct. In addition, higher levels of measurement allow for more in-depth statistical analyses. With ordinal data, for example, we can compute statistics such as the median, range, and interquartile range. , skew and kurtosis). With interval-level data, it is important to know the shape of the distribution, as different-shaped distributions imply different interpretations for statistics such as the mean and standard deviation.
1986). Introduction to classical and modern test theory (pp. 16–42). Belmont, CA: Wadsworth. Ghiselli, E. , Campbell, J. , & Zedeck, S. (1981). Measurement theory for the behavioral sciences (pp. 31–58). New York: W. H. Freeman. Murphy, K. , & Davidshofer, C. O. (2001). , pp. 67–85). Upper Saddle River, NJ: Prentice Hall. In addition, most introductory (psychological) statistics books will provide a more detailed description of the descriptive statistics discussed in this module, although not as specific to psychological testing as the preceding references.
One way of doing this is by converting each score to a Z score. The formula for a Z score is Z = (Xi – M) / S. 88. By converting our test score to a Z score, we can more readily answer that question. 14. Thus, a score of 84 is only a little more than one tenth of a standard deviation above the mean, not a very large difference. 1. The farther the score is from the mean the larger its Z score will be. Z scores are also useful in identifying outlier cases in a set of data. A typical rule of thumb is any score more than 3 standard deviations away from the mean is considered an outlier.
Measurement Theory in Action: Case Studies and Exercises by Kenneth S. Shultz