If a case has missing data on ANY of the specified variables, by default it is excluded from all the calculations. However, there is an option to exclude cases pairwise -- that is, to calculate each correlation coefficient using all cases having valid data on that PAIR of variables.

Ordinarily this program is invoked by the Web interface for the SDA programs, and the user does not have to deal with the keywords given in this document. Output from the program is in HTML, which can be viewed with a Web browser.

It is also possible to run the program directly by preparing a command file, which specifies the variables to be analyzed and the options to use. This document explains how to prepare such a file. The name of this batch command file is specified to the program after the ‘-b’ option flag.

Keyword Possible Specification Default (if no keyword) _____________________________________________________________________STUdy= path of dataset directory Look for variables in current directory only Vars= names of vars to correlate REQUIRED (separated by spaces/commas) Weight= name of weight variable No weighting Filter= name(s) and codes of filter No filter variable(s) GVARCase= LOWER or UPPER No force to lower/upper case MD= Pairwise Cases with any MD are excluded SAvefile= filename to receive output Output sent to screen (overwrite existing file) (standard output) TExt= Yes No text for variables LAnguagefile= Name of file with non-English English labels on labels and messages output RUNtitle= Title or comments for run No title or comments

For each statistic the user can specify the number of desired decimal places (in parentheses, after the name of the statistic). See below for the default number of decimals for each statistic.] Since the default main statistic is the Pearson correlation coefficient, it is not necessary to specify that statistic unless you want a number of decimal places other than the default.

It is possible to reverse the sign of the correlations of one or more of the variables. If you want to reverse the sign of a variable, give its index position after the ‘reverse=’ keyword. A variable’s index position is its relative position after the ‘vars=’ keyword.

Keyword Possible Specification Default (if no keyword) _____________________________________________________________________MAINstat= CORR (ndec) Display correlations, LOGodds (ndec) with default number of decimal places REVerse= list Do not reverse the signs (see example below) of variables

- Standard errors of the correlations.

These statistics are placed in a matrix, beneath the matrix of correlation coefficients. - Univariate statistics.

The statistics available for each variable include its mean, standard deviation, standard error, valid N of cases, and (if there is a weight variable) valid weighted N of cases. - Paired statistics.

These statistics are available if the ‘md=pairwise option’ is specified. The paired statistics available are the same as the univariate statistics. Each statistic is based on the number of valid cases for that pair of variables.

OTHERstats= SECOR (ndec) No standard errors of the correlations (Univariate statistics) MEANs (ndec) No means SD (ndec) No standard deviations SEVAR (ndec) No standard errors Ncases No unweighted N’s WNcases (ndec) No weighted N’s (Paired statistics) PMEANs (ndec) No paired means PSD (ndec) No paired std devs PSEVAR (ndec) No paired std errs PNcases No paired N’s PWNcases (ndec) No paired weighted N’s PSQ= list1 ; list2 (ndec) No P-square statistics (see below)

The calculation of the standard error of the correlation coefficient in each cell is based by default on the UNWEIGHTED number of cases, even if a weight variable has been used for calculating the correlation coefficient. Ordinarily this procedure will generate a more appropriate statistical test than one based on the weighted N in each cell.

The ‘PSQ=’ keyword allows you to specify which items should be used for the rows (list1), and which items should be used as the criterion variables (list2). Each list is a set of numbers, referring to the order in which the variables were specified after the ‘Vars=’ keyword. Each list can consist of single numbers or ranges, separated by commas or blanks. The two lists are separated by a semicolon.

# Basic example study = /sa/testdata vars = spend spend2 spend3 spend4 savefile = mymatrix

----------------------------------- # Use weight and filter variables, and request some # univariate statistics and descriptive text for the variables. vars = spend spend2 spend3 spend4 otherstats = means, ncases weight= wtvar filters= age(18-50) gender(1) text = yes savefile = mymatrix

----------------------------------- # Generate a P-square matrix of the four spend variables, # using age, educ, and sex as the criterion variables. # Also request 3 decimal places. vars = spend spend2 spend3 spend4 age educ sex psq = 1-4; 5-7 (3) runtitle= Test run to demonstrate P-square stats savefile= mypsq ----------------------------------- # Reverse the sign of the correlations involving two of # the four spending variables -- the 2nd and 4th mentioned # after the ‘vars=’ keyword. vars = spend spend2 spend3 spend4 reverse = 2 4 text runtitle= Test run to demonstrate reversing signs savefile= mytest

CSM, UC Berkeley

January 25, 2010