Utilities

Programs and Utilities

PARALLEL:

Hays RD. (1987). PARALLEL: A program for performing parallel analysis. Applied Psychological Measurement. 11, 58.
The executable: PARALLEL.EXE .

MULTI:

Hays RD & Wang E. (1992, April). Multitrait Scaling Program: MULTI. Proceedings of the Seventeenth Annual SAS Users Group International Conference, 1151-1156.
Sample program including macro call: MULTI.sas and its output: MULTI.out.

Also see:

Hays, R.D. & Hayashi, T. (1990). Beyond internal reliability: Rationale and User's Guide for Multitrait Scaling Analysis Program on the microcomputer. Behavior Research Methods, Instruments & Computers, 22, 167-175.

GRIP:

Hays RD, Wang E, & Sonksen M. (1995, September). General Reliability and Intraclass Correlation Program (GRIP). Proceedings of the 3rd Annual Conference of Western Users of SAS Software.

6/2/2003: replaced with a version modified by Sally Carson and Karen Spritzer -- adds NREL70, NREL80, NREL90, and NREL95 plus now able to run under SAS 8.0.

2/18/04: updated to fix a problem that came about because SAS 8 is case sensitive with respect to variable names.
The macro and sample call: grip_feb18-04.sas

WKAPPA:

Liu H, & Hays RD. (1999, April). Measurement of interrater agreement: A SAS/IML macro kappa procedure for handling incomplete data. Proceedings of the SAS Users Group International Conference, 1620-1625.

Here's the SAS macro: wkappa.txt.
Link to the paper: http://www2.sas.com/proceedings/sugi24/Stats/p280-24.pdf

Note: this macro was written using SAS/IML version 6 (may need to be modified a little when running in SAS version 8 and above).

Other helpful programs

Scoring the SF-36 version 1.0 (this version uses 1990 General Population norms - see alternative code to use 1998 norms):

Hays RD, Sherbourne CD, Spritzer KL, & Dixon W J. (1996) A Microcomputer Program (sf36.exe) that Generates SAS Code for Scoring the SF-36 Health Survey. Proceedings of the 22^nd Annual SAS Users Group International Conference, 1128-1132.

The executable described in this paper is no longer current, but the above article serves as a good guide to using the following SAS code, sf36.sas, and US general population data, sf36.raw, to analyze your data.
[We provide the executable and other files referenced in the paper for historical purposes only (sf36.exe, sf36.in, sf36b.exe).]

Link to the paper: http://www2.sas.com/proceedings/sugi22/POSTERS/PAPER244.PDF

An alternative set of code that expands upon the scoring of the SF-36 version 1.0 is here (uses 1990 General Population norms, but has the option to use 1998 norms):

Program to score the SF-36 version 1.0. All sections require SF36 version 1.0 items to be named i1-i36 and ID variable named ID (in order to merge the various output datasets); one section (sf36b.sas) requires a variable for gender named MALE (=1 if male, =0 if female) and a variable for AGE (continuous). Note: if you want to run sf36b.sas, you must run sf36a.sas prior to it. Code to calculate Fryback's QWB is in fryback.sas and is called from sf36a.sas. The %include can be commented out if not needed. Similarly, code to calculate Nichol's HUI2 (hui2.sas) is called from sf36c.sas and can be commented out or omitted if not needed.

Download main program (score1.sas) and its components:
   randhsi.sas [RAND-36 HSI score]
   sf36a.sas [SF-36 scores and optionally Fryback's Quality of Well-Being Score],
   sf36b.sas [comparison to US general population], and
   sf36c.sas [SF-36 and SF-12 physical and mental health composite scores and factors (using 1990 General Population norms) and Nichol's Health Utility Index].

Note: to use the Means/SD's from the 1998 General Population, download and use score1-1998.sas and sf36c1998.sas (instead of score1.sas and sf36c.sas).

Note: QWB (not just QWB100) code change in Fryback code (fryback.sas above) and files that call it up (6/30/2008).

Scoring the SF-36 version 2.0:

SAS code to score the SF-36 version 2.0.

code: sf36v2-4_public.sas (3/16/07)
output from test dataset: sf36v2-4_public.lst (3/16/07)

Scoring Brazier's Index:

sf6dus3.sas - written for sf36 version 2, but with option for sf36 version 1.0.

Scoring the SF-12 version 2.0:

SAS code to score the SF-12 version 2.0. Assumes your items are named I1, I2a-b, I3a-b, I4a-b, I5, I6a-c, I7. Rename them in the first data step if they are not.
code: sf12v2-1.sas .
output from test dataset: sf12v2-1.lst (8/24/04)

Mosier's formula: (documentation and example #2 corrected on 3/27/2008 - no change in program itself)

Mosier's formula (Mosier, C.I. (1943). On the reliability of a weighted composite. Psychometrica, 8, 161-168.

Estimation of reliability of composite scores. Download files mosier.exe, mosier.in, and mosier2.in.

Mosier-input.doc is the annotated test input file and mosier.out is the test output.

Reference for Mosier formula is mosier.JPG (taken from: Hays, Ron D. Evaluating Self-Report Data Using Psychometric Methods. Lecture in Quality of Care Course. RAND, Santa Monica CA: February 11, 2004. PowerPoint presentation available for download here ---QOC-feb11-04.ppt.)

MTMM:

Hayashi, T., & Hays, R. D. (1987). A microcomputer program for analyzing multitrait-multimethod matrices. Behavior Research Methods, Instruments, And Computers, 19, 345-348.
Used to evaluate multitrait-multimethod correlation matrices. Download files: mtmm.exe and mtmm.in.

AUC/ROC:

Hays, R. D. (1990). ROC: Estimation of the area under a receiver operating characteristic curve. Applied Psychological Measurement (Computer Program Exchange), 14, 208. Assess the area under the receiver operating characteristic curve. Download files: area.exe, area.in, area2.exe, and area2.in.

Some documentation for area.exe (5/9/2008).

Longitudinal Scalogram Analysis (LSA):

Hays, R. D. & Ellickson, P. L. (1990). Longitudinal scalogram analysis: A methodology and microcomputer program for Guttman scale analysis of longitudinal data. Behavior Research Methods, Instruments & Computers, 22, 162-166.

Hays, R. D. & Ellickson, P.L. (1991). Guttman scale analysis of longitudinal data: A methodology and drug use application. International Journal of the Addictions, 25 (11A), 1341-1352.

Ellickson, P. L., Hays, R. D., & Bell, R. M. (1992). Stepping through the drug use sequence: Longitudinal scalogram analysis of initiation and heavy use. Journal of Abnormal Psychology, 101, 441-451.

Hays, Ron D. (1991). User's Guide for the Longitudinal Scalogram Analysis Program.

LSA Program and test data:.
input - sample input
output - output from analysis
raw.new
lg.out
lg2.out

go.bat - batch file that drives the following executables - requires "RAW as input)
lgint.exe
ll.exe
llll.exe

STEIG:

steig.exe: tests significance of difference of paired correlations.

Note: you can run this as a Windows application, but the results will blip by you too fast to write them down. The preferred approach is to open up a DOS window and run it from there.

In this program, R1 and R2 are the correlations being compared to see if they are significantly different from one another. R3 is the correlation between the variables that are unique to R1 and R2.

For example, if the correlation between x and y (R1) is being compared to the correlation between z and y (R2), then R3 is the correlation between x and z.

CORRDIFF:

corrdiff.bas: tests significance of difference of independent correlations (written in BASIC)
corrdiff.sas: tests significance of difference of independent correlations (written in SAS)

HAYSPOWE:

hayspowe.bas: power analysis program for limited situations
hayspowe.sas: SAS 8.0 version of hayspowe.bas (8/23/04)
hayspowe.out: sample output from hayspowe.sas (8/23/04)

Power is the probability of rejecting the null hypothesis (e.g., two groups do not differ on physical functioning) when the alternative hypothesis (the two groups differ) is true.

Here, we provide a SAS program and output Here we provide a SAS program and output from the program that shows some common power analyses. Specifically, the program provides the sample sizes needed to detect differences between two experimental groups (Tables 1 and 2) and two self-selected groups (Table 3). The SAS program (hayspowe.sas) requires as input (at end of the SAS program just after “%hayspowem”) the title for the power analysis, number of scales in the analysis, the standard deviation of each scale, and each scale’s label. The output shows the sample size needed for a point difference of 2, 5, 10, and 20 points.

The example output file (hayspowe.out) provides power analysis results for three scales (physical functioning, emotional well-being, social functioning). Table 1 indicates, for example, that one would have 80% power (alpha = 0.05) with a sample size of 82 (41 per group) to detect a 10-point difference in physical functioning (SD = 20.10) using a two-tailed t-test if you had a repeated measures design and a correlation of 0.60 between physical functioning scores at the two time points. If physical functioning were measured only at one time point (follow-up), you would need a sample of 126 (63 per group) to have the same power.

ALPHATST:

alphatst.exe: tests significance of difference between alpha coefficients (see alphatst.doc documentation).
alpha.exe: updated version of alphatst.exe. Added to website on 4/11/03.

A description of the formula used to estimate the significance of difference between alpha coefficients can be found in the article:

Feldt, L.S., Woodruff, D.J., and Salih, F.A. (1987) Statistical Inference for Coefficient Alpha. Applied Psychological Measurement, 11, 1, 93-103.

Other odds and ends

Adjusting for Clustering (Non-Independence Among Observations) using SAS - March 28, 2008.

spear.exe: applies Spearman-Brown prophecy formula to reliability estimates

multi_p.sas: derived from multi.sas - pairwise correlations.

Guttman scaling: scalo.exe, gutt.dat, sample.dat, test.dat, scale.out -- this program assesses the extent to which the items fit a response pattern that is consistent with a Guttman scale.

nfact.sas: this is a macro that helps determine the number of factors to rotate in a factor analysis. In general, specify the maximum number of factors you might expect in the NFACT macro to maximize the information you will get to help determine the number of factors to rotate.
2nd step (after the "endsas") does the rotation and creates the factor scores .

Information on lsmeans

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