By Holmes Finch
The ebook might be designed essentially for graduate scholars (or complex undergraduates) who're studying psychometrics, in addition to pros within the box who want a reference to be used of their perform. we'd think that clients have a few uncomplicated wisdom of utilizing SAS to learn facts and behavior simple analyses (e.g., descriptive information, frequency distributions). additionally, the reader can be acquainted with easy statistical strategies comparable to descriptive data (e.g., suggest, median, variance, normal deviation), percentiles and the rudiments of speculation checking out. they need to actually have a passing familiarity with matters in psychometrics similar to reliability, validity and test/survey scoring. we won't suppose from now on than simple familiarity with those concerns, and may dedicate a section of every one bankruptcy (as good because the complete first bankruptcy) to reviewing lots of those uncomplicated rules for these no longer accustomed to them. We envision the ebook as being necessary both as a major textual content for a direction on utilized dimension the place SAS is the most platform for guide, or as a complement to a extra theoretical textual content. We additionally expect that readers operating in executive firms answerable for checking out and dimension matters on the neighborhood, nation and nationwide degrees, and personal checking out, survey and industry learn businesses, in addition to college participants desiring a pragmatic source for psychometric perform will function a marketplace for the publication. in brief, the readership would come with graduate scholars, school individuals, facts analysts and psychometricians chargeable for research of survey reaction info, in addition to academic and mental checks. The target of the e-book is to supply readers with the instruments invaluable for assessing the psychometric traits of academic and mental measures in addition to surveys and questionnaires. each one bankruptcy will hide a topic pertinent to psychometric and dimension perform, with an emphasis on program. subject matters may be in brief mentioned from a theoretical/technical viewpoint with a view to give you the reader with the heritage essential to competently use and interpret the statistical analyses that would be provided as a consequence. Readers will then be offered with examples illustrating a selected idea (e.g., reliability). those examples will comprise a dialogue of the actual research, besides the SAS code essential to behavior them. The ensuing output will then be mentioned intimately, targeting the translation of the implications. eventually, examples of the way those effects could be written up may also be incorporated within the textual content. it really is was hoping that this combination of conception with examples of tangible perform will serve the reader either as a pedagogical device and as a reference paintings.
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Additional info for Applied Psychometrics using SAS
There are a few new points to note here. First of all, in this case we do not need the correlation between the two half scores to print out, so we can suppress that with the noprint command in proc corr. Secondly, we will create a new output dataset from proc corr that we call halfr. We can name this dataset anything we want, within the limits of SAS naming conventions. This dataset is temporary, meaning that when we exit SAS it will disappear. We then read the halfr output file into a second dataset that we call spearman_brown, where we calculate the SpearmanBrown estimate of reliability.
95); proc iml symsize = 500; start Bonett(N1,items,rxx,confidence,lowerCI,upperCI); * +---------------------------------------------------------------+ Bonett 2002 method of CI computation Inputs to subroutine are number of examinees (N1), number of items (items), sample value of coefficient alpha (rxx), and desired level of confidence (Confidence). Outputs are the upper and lower limits of the confidence interval (upperCI, lowerCI) +----------------------------------------------------------------+; critZ = -1#probit((1-Confidence)/2); transBon_z = log(1-abs(rxx)); if rxx <0 then transBon_z = transBon_z * -1; var_Z = (2#items)/((items – 1) # (n1 – 2)); SE_Bon_Z = sqrt(var_z); lowerCI = 1 – exp(transBon_z + critz#SE_Bon_z); upperCI = 1 – exp(transBon_z–critz#SE_Bon_z); finish; Start Feldt(n1,items,rxx,Confidence,lowerCI,upperCI); * +---------------------------------------------------------------+ Feldt (1965) method of CI computation Inputs to subroutine are number of examinees (N), number of items (items), sample value of coefficient alpha (alpha), and desired level of confidence (Confidence).
For these reasons we must estimate reliability most often with a single or perhaps two administrations. A question of some interest is regarding what these reliability estimates actually mean. Quite often, researchers reporting reliability estimates will describe the reliability of the instrument as if it is a static value independent of the particular sample from which the data were collected. However, more recent discussion of this issue, as well as recommendations for reporting reliability estimates, have suggested that reliability estimates (as opposed to the theoretical construct described above) must be described as being data-specific (Thompson, 2003).