Home of the original IBM PC emulator for browsers.
[PCjs Machine "ibm5170"]
Waiting for machine "ibm5170" to load....
The STATISTICAL CONSULTANT is an expert system to help you select the right statistical test for your problem. The system asks you a series of questions about the variables and goals of the measurement. Based on your responses, the system chooses a statistical test or measure. Should your problem require a deeper analysis than can be addressed within CONSULTANT, the system indicates references for further study. The program assumes a level of technical knowledge greater than that offered in a first course in statistics.
Disk no 949 Program Title: STATISTCAL CONSULTANT PC-SIG version 1 The STATISTICAL CONSULTANT helps you select the appropriate statistical test for your problem. The system asks you a series of questions, starting with, "how many variables do you have?" Responses lead to the program finding the particular technique you need. Most questions are phrased for yes/no responses. NOTE: This program requires an extensive background and knowledge of statistics. Usage: Statistics System Requirements: 128K memory and one disk drive. How to Start: Type: TYPE STATCON.DOC (press enter) to view documentation and STATCON (press enter) to run the program. Suggested Registration: $8.00 for reference book. File Descriptions: ORDER TXT Order form for accompanying statistics book. STATCON 000 Program overlay (must be on disk with STATCON.COM). STATCON COM Main program. STATCON DOC Documentation for Statistical Consultant. STATCONC 000 Program overlay (must be on disk with STATCON.COM). STATCONC COM Version of STATCON for composite monitors. PC-SIG 1030D E Duane Avenue Sunnyvale Ca. 94086 (408) 730-9291 (c) Copyright 1987 PC-SIG
╔═════════════════════════════════════════════════════════════════════════╗ ║ <<<< Disk no 949 STATISTCAL CONSULTANT >>>> ║ ╠═════════════════════════════════════════════════════════════════════════╣ ║ To print the documentation, Type: COPY STATCON.DOC LPT1: (press enter) ║ ║ ║ ║ To run the program, Type: STATCON (press enter) ║ ╚═════════════════════════════════════════════════════════════════════════╝
ORDER FORM To order copies of A GUIDE FOR SELECTING STATISTICAL TECHNIQUES FOR ANALYZING SOCIAL SCIENCE DATA, SECOND EDITION by Frank M. Andrews, Laura Klem, Terrence N. Davidson, Patrick M. O'Malley and Willard L. Rodgers send this form and a check for $8 per copy (packages of 5 copies are $25) to Publishing Division Institute for Social Research The University of Michigan P.O. Box 1248 Ann Arbor, MI 48106-9973 ALL ORDERS FROM INDIVIDUALS MUS BE PREPAID; make checks payable to the Institute for Social Research. Organizations with a printed purchase order may be billed; actual postage costs will be added to billed orders. PLEASE SEND _____ COPIES OF: A GUIDE FOR SELECTING STATISTICAL TECHNIQUES FOR ANALYZING SOCIAL SCIENCE DATA, SECOND EDITION @ $8 each (or $25 for a package of 5 copies) $__________ Michigan Residents add 4% sales tax $__________ Total amount enclosed $__________ SHIPPING ADDRESS NAME________________________________________________________ ORGANIZATION _______________________________________________ ADDRESS_____________________________________________________ CITY_____________________________ STATE ______ ZIP_________ ______ Please send me information about other volumes published by the Institute for Social Research.
THE STATISTICAL CONSULTANT by Robert Sechrist Dept. of Geography & Regional Planning Indiana University of Pennsylvania Indiana, Pa. 15705 USERS MANUAL The Statistical Consultant is an authorized implementation of A GUIDE FOR SELECTING STATISTICAL TECHNIQUES FOR ANALYZING SOCIAL SCIENCE DATA, SECOND EDITION, by Frank M. Andrews, Laura Klem, Terrence N. Davidson, Patrick M. O'Malley, and Willard L. Rodgers (Ann Arbor: Institute for Social Research, The University of Michigan, 1981). Copyright 1981 by the University of Michigan, All Rights Reserved. Use in this software release is by permission of the Institute for Social Research. Copies of the bound volume A GUIDE FOR SELECTING STATISTICAL TECHNIQUES FOR ANALYZING SOCIAL SCIENCE DATA, SECOND EDITION, may be ordered from the publisher by writing to: Book Sales Section, Institute for Social Research, The University of Michigan, P.O. Box 1248, Ann Arbor, Michigan 48106 (telephone: 313-764-8271) or by using the form found in the file order.txt on your distribution diskette. THE STATISTICAL CONSULTANT The Statistical Consultant is an expert system designed to assist you in selecting the appropriate statistical test for your problem. The system will ask you a series of questions, starting with, how many variable do you have? Responses to questions leads to the identification of a particular technique. Most questions are phrased for yes/no responses. In these cases one need only type y or n. The Consultant is constructed so that any answer other than 'y' will be taken as 'n'. Occasionally other responses may be required. Follow the prompts and there will be no problem. A few minutes experimentation with the Consultant should answer all remaining questions. SYSTEM REQUIREMENTS You will need an IBM-pc (or compatible) with 128kb memory and one floppy disk drive. The Consultant is constructed to take advantage of a color monitor, but a monochrome will suffice. If you are using a composite monitor, then you should execute statconc. No data files are accessed by the Consultant. INSTALLATION As always make a backup immediately. There are five files on the supplied diskette. These are statcon.com, statconc.com, statcon.doc, statcon.000, and statconc.000. Statcon.doc is this file, statcon.com and statconc.com are the program files. Statcon.000 and Statconc.000 are required overlay files. Either the statcon or statconc files should be copied to your work diskette or hard disk. You should print a copy of statcon.doc using whatever method you normally employ. A good method for printing statcon.doc is the command 'copy statcon.doc prn'. INVOCATION AND PROGRAM OPERATION From the system prompt type statcon (or statconc). For example, A>statcon You should not call statcon from another drive (A>b:statcon, for example) because the program will, under some conditions, look for the file statcon.000 on the calling drive. If statcon.000 is not found the program will abort. You will be asked through a series of questions (most of which require a yes or no response) to identify how and what you wish to measure, verify, or determine. In some cases the questions have been phrased so that the negative response would not be readily identifiable were it not enclosed in parentheses. Where this is the case, additional indicators are present, and afterwards your selection is echoed back to the screen in red. When answering questions either upper or lower case responses are acceptable. The first choice you must make involves telling the consultant the number of variables you will be dealing with. From there continue answering the questions the program asks you. If you do not know what you are being asked, you should seek the assistance of someone who has a better grasp of statistics than yourself. The consultant assumes that you know something of statistics (about the equivalent of an introductory course). The references suggested can be helpful in improving your knowledge of a particular test. The full citations for references given by the program can be found below. USER NOTES AND WARNINGS The user should please note the following. 1. Information in red (or highlight for monochrome) after a suggestion should be taken as a warning that may apply in your case. Notes after the references in yellow (or dim for monochrome) are also warnings, but tend to be more informative, still they should be seriously considered before proceeding with the measure or test. 2. Weighted data, small sample sizes, complex sample designs and capitalization on chance in fitting a statistical model are sources of potential problems in data analysis. If one of these situations exists, the CONSULTANT should be used with caution. 3. The statistical measures recommended are descriptive of the particular sample being examined. For some statistical measures, the value obtained will also be a good estimate of the value in the population as a whole, whereas other statistics may underestimate (or overestimate) the population value. In general the amount of bias is relatively small and sometimes there are adjustments which can be made for it. These adjustments are discussed in good statistics texts (but are not offered by the CONSULTANT). If a statistic is a biased estimator of the population value, it is marked in the offered solution with an asterisk. (*) 4. In principle, a confidence interval may be placed around any statistic. Methods for doing this are not indicated in the CONSULTANT. Formulas for computing confidence intervals for commonly used measures are given in standard textbooks. 5. The CONSULTANT does not explicitly consider possible transformation of the data such as bracketing, using logarithms, ranking, etc. Transformations may be used to simplify analysis or bring data into line with assumptions (It is, for example, often possible to transform scores so that the transformed scores correspond to a normal distribution, constitute an interval scale, or relate linearly to another variable.) Occasionally it may be wise to eliminate cases with extreme values. 6. In many situations it is possible to make alternative decision about the nature of one`s variables, relationships, and/or goals, and these may result in the alternative selections at various points in the decision (interrogation) process of the CONSULTANT. It is always possible to use techniques that require less stringent assumptions than the techniques originally considered. Two-point nominal variables meet the definition of intervally scaled variables. 7. Information in blue (or underlined for monochrome) after a suggestion indicates a scenario beyond the scope of the CONSULTANT. 8. Most importantly, the CONSULTANT is a guide, it is not the only source of information available. If you are a novice talk to an expert before committing yourself to an inappropriate test. GLOSSARY OF TERMS USED BY THE CONSULTANT ADDITIVE. A situation in which the best estimate of a dependent variable is obtained by simply adding together the appropriately computed effects of each of the independent variables. Additivity implies the absence of interactions. See also INTERACTION. AGREEMENT. Agreement measures the extent to which two sets of scores (e.g., scores obtained from two raters) are identical. Agreement involves a more stringent matching of two variables than does covariation, which implicitly allows one to change the mean (by adding a constant) and /or to change the variance (by multiplying by a constant) for either or both variables before checking the match. BIAS. The difference between the expected value of a statistic and the population value it is intended to estimate. See EXPECTED VALUE. BIASED ESTIMATOR. A statistic whose expected value is not equal to the population value. See EXPECTED VALUE. BIVARIATE NORMALITY. A particular form of distribution of two variables that has the traditional "bell" shape (but not all bell-shaped distributions are normal). If plotted in three-dimensional space, with the vertical axis showing the number of cases, the shape would be that of a three-dimensional bell (if the variances were unequal). When perfect bivariate normality obtains, the distribution of one variable is normal for each and every value of the other variable. See also NORMAL DISTRIBUTION. BRACKETING. The operation of combining categories or ranges of values of a variable so as to produce a small number of categories. Sometimes referred to as "collapsing" or "grouping." CAPITALIZATION ON CHANCE. When one is searching for a maximally powerful prediction equation, chance fluctuations in a given sample act to increase the predictive power obtained; since data from another sample from the same population will show different chance fluctuations, the equation derived for one sample is likely to work less well in any other sample. CASUAL MODEL. An abstract quantitative representation of real-world dynamics (i.e., of the causal dependencies and other interrelationships among observed or hypothetical variables.) COMPLEX SAMPLE DESIGN. Any sample design that uses design that uses something other than simple random selection. Complex sample designs include multi-stage selection, and/or stratification, and/or clustering. For information on the calculation of sampling errors of statistics from complex designs, see note 9 in Appendix C. COVARIATE. A variable that is used in an analysis to correct, adjust, of modify the scores on a dependent variable before those scores are related to one or more independent variables. For example, in an analysis of how demographic factors (age, sex, education, etc.) relate to ware rates, monthly earnings might first be adjusted to take account of (i.e., remove effects attributable to) number of hours worked, which in this example would be the covariate. COVARIATION. Covariation measures the extent to which cases (e.g., persons) have the same relative positions on two variables. See also AGREEMENT. DEPENDENT VARIABLE. A variable which the analyst is trying to explain in terms of one or more independent variables. The distinction between dependent and independent variables is typically made on theoretical grounds - in terms of a particular causal model or to test a particular hypothesis. Synonym: criterion variable. DESIGN MATRIX. A specification, expressed in matrix format, of the particular effects and combinations of effects that are to be considered in an analysis. DICHOTOMOUS VARIABLE. A variable that has only two categories. Gender (male/female) is an example. See also TWO-POINT SCALE. DUMMY VARIABLE. A variable with just two categories that reflects only part of the information actually available in a more comprehensive variable. For example, the four-category variable Region (Northeast, Southeast, Central, West) could be the basis for a two-category dummy variable that would distinguish Northeast from all other regions. Dummy variables often come in sets so as to reflect all of the original information. In our example, the four-category region variable defines four dummy variables: (1) Northeast vs. all other; (2) Southeast vs. all other; (3) Central vs. all other; and (4) West vs. all other. Alternative coding procedures (which are equivalent in terms of explanatory power but which may produce more easily interpretable estimates) are effect coding and orthogonal coefficients. EXPECTED VALUE. A theoretical average value of homogeneity of variance. See HOMOGENEITY OF VARIANCE. HETEROSCEDASTICITY. The absence of homogeneity of variance. See also HOMOGENEITY OF VARIANCE. HIERARCHICAL ANALYSIS. As used on page 26 of the Guide, a hierarchical analysis is one in which inclusion of a higher order interaction term implies the inclusion of all lower order terms. For example, if the interaction of two independent variables is included in an explanatory model, then the main effects for both of those variables are also included in the model. HOMOGENEITY OF VARIANCE. A situation in which the variance on a dependent variable is the same (homogeneous) across all levels of the independent variables. In analysis of variance applications, several statistics are available for testing the homogeneity assumption (see Kirk, 1968, Page 61); in regression applications, a lack of homogeneity cam be detected by examination of residuals (see Draper and Smith, 1966, page 86). In either case, a variance-stabilizing transformation may be helpful (see Kruskal, 1978, page 1052). Synonym: homoscedasticity. Antonym: heteroscedasticity. HOMOSCEDASTICITY. See HOMOGENEITY OF VARIANCE. INDEPENDENT VARIABLE. A variable used to explain a dependent variable. Synonyms: predictor variable, explanatory variable. See also DEPENDENT VARIABLE. INTERACTION. A situation in which the direction and/or magnitude of the relationship between two variables depends on (i.e., differs according to) the value of one or more other variables. When interaction is present, simple additive techniques are inappropriate; hence, interaction is sometimes thought of as the absence of additivity. Synonyms: nonadditivity, conditioning effect, moderating effect contingency effect. See also PATTERN VARIABLE, PRODUCT VARIABLE. INTERVAL SCALE. A scale consisting of equal-sized units (dollars, years, etc.). On an interval scale the distance between any two positions is of known size. Results from analytic techniques appropriate for interval scales will be affected by any non-linear transformation of the scale values. See also SCALE OF MEASUREMENT. INTERVENING VARIABLE. A variable which is postulated to be a predictor of one or more dependent variables, and simultaneously predicted by one or more independent variables. Synonym: mediating variable. KURTOSIS. Kurtosis indicates the extent to which a distribution is more peaked or flat-topped than a normal distribution. LINEAR. The form of a relationship among variables such that when any two variables are plotted, a straight line results. A relationship is linear if the effect on a dependent variable of a change of one unit in an independent variable is the same for all possible such changes. MATCHED SAMPLES. Two (or more) samples selected in such a way that each case (e.g., person) in one sample is matched (i.e., identical within specified limits) on one or more preselected characteristics with a corresponding case in the other sample. One example of matched samples is having repeated measures on the same individuals. Another example is linking husbands and wives. Matched samples are different from independent samples, where such case-by-case matching on selected characteristics has not been assured. MEASURE OF ASSOCIATION. A number (a statistic) whose magnitude indicates the degree of correspondence (i.e., strength of relationship) between two variables. An example is the Pearson product-moment correlation coefficient. Measures of association are different from statistical tests of association (e.g., Pearson chi-square, F test) whose primary purpose is to assess the probability that the strength of a relationship is different from some preselected value (usually zero). See also STATISTICAL MEASURE, STATISTICAL TEST. MISSING DATA. Information that is not available for a particular case (e.g.,person) for which at least some other information is available. This can occur for a variety of reasons, including a person's refusal or inability to answer a question, nonapplicability of a question, etc. For useful discussions of how to overcome problems caused by missing data in surveys see Hertel (1976) and Kim and Curry (1977). MULTIVARIATE NORMALITY. The form of a distribution involving more than two variables in which the distribution of one variable is normal for each and every combination of categories of all other variables. See Harris (1975, page 231) for a discussion of multivariate normality. See also NORMAL DISTRIBUTION. NOMINAL SCALE. A classification of cases which defines their equivalence and non-equivalence, but implies no quantitative relationships or ordering among them. Analytic techniques appropriate for nominally scaled variables are not affected by any one-to-one transformation of the numbers assigned to the classes. See also SCALE OF MEASUREMENT. NONADDITIVE. Not additive. See also ADDITIVE, INTERACTION. NORMAL DISTRIBUTION. A particular form for the distribution of a variable which, when plotted, produces a "bell" shaped curve--symmetrical, rising smoothly from a small number of cases at both extremes to a large number of cases in the middle. Not all symmetrical bell-shaped distributions meet the definition of normality. See Hays (1973,page 296). NORMALITY. See NORMAL DISTRIBUTION. ORDINAL SCALE. A classification of cases into a set of ordered classes such that each case is considered equal to, greater than, or less than every other case. Analytic techniques appropriate for ordinally scaled variables ore not affected by any monotonic transformation of the numbers assigned to the classes. See also SCALE OF MEASUREMENT. OUTLYING CASE (OUTLIER). A case (e.g., person) whose score on a variable deviates substantially from the mean (or other measure of central tendency). Such cases can have disproportionately strong effects on statistics. PATTERN VARIABLE. A nominally scaled variable whose categories identify particular combinations (patterns) of scores on two or more other variables. For example, a party-by-gender pattern variable might be developed by classifying people into the following six categories: (1) Republican males, (2) Independent females, (3) Democratic males, (4) Republican females, (5) Independent females, (6) Democratic females. A pattern variable can be used to incorporate interaction in multivariate analysis. PRODUCT VARIABLE. An intervally scaled variable whose scores are equal to the product obtained when the values of two other variables are multiplied together. A product variable can be used to incorporate certain types of interaction in multivariate analysis. RANKS. The position of a particular case (e.g., person) relative to other cases on a defined scale - as in "1st place," "2nd place," etc. Note that when the actual values of the numbers designating the relative positions (the ranks) are used in analysis they are being treated as an interval scale, not on ordinal scale. See also INTERVAL SCALE, ORDINAL SCALE. SCALE OF MEASUREMENT. As used in this Guide, scale of measurement refers to the nature of the assumptions one makes about the properties of a variable; in particular, whether that variable meets the definition of nominal, ordinal, or interval measurement. See also NOMINAL SCALE, ORDINAL SCALE, INTERVAL SCALE. SKEWNESS. Skewness is a measure of lack of symmetry of a distribution. STANDARDIZED COEFFICIENT. When an analysis is performed on variables that have been standardized so that they have variances of 1.0, the estimates that result are known as standardized coefficients; for example, a regression run on original variables produces unstandardized regression coefficients known as b's, while a regression run on standardized variables produces standardized regression coefficients known as betas. (In practice, both types of coefficients can be estimated from the original variables.) Blalock (1967), Hargens (1976), and Kim and Mueller (1976) provide useful discussions on the use of standardized coefficients. STANDARDIZED VARIABLE. A variable that has been transformed by multiplication of all scores by a constant and/or by the addition of a constant to all scores. Often these constants are selected so that the transformed scores have a mean of zero and a variance (and standard deviation) of 1.0. STATISTICAL INDEPENDENCE. A complete lack of covariation between variables; a lack of association between variables. When used in analysis of variance or covariance, statistical independence between the independent variables is sometimes referred to as a balanced design. STATISTICAL MEASURE. A number (a statistic) that can be used to assess the probability that a statistical measure deviates from some preselected value (often zero) by no more than would be expected due to the operation of chance if the cases (e.g., persons) studied were randomly selected from a larger population. Examples include Pearson chi-square, F test, t test, and many others. Statistical tests are different from statistical measures. See also STATISTICAL MEASURE. TRANSFORMATION. A change made to the scores of all cases (e.g., persons) on a variable by the application of the same mathematical operation(s) to each score. (Common operations include addition of a constant, multiplication by a constant, taking logarithms, ranking bracketing, etc.) TWO-POINT SCALE. If each case is classified into one of two categories (e.g., yes/no. male/female, dead/alive), the variable is a two-point scale. For analytic purposes, two-point scales can be treated as nominal scales, ordinal scales, or interval scales. WEIGHTED DATA. Weights are applied when one wishes to adjust the impact of cases (e.g., persons) in the analysis, e.g., to take account of the number of population units that each case represents. In sample surveys weights are most likely to be used with data derived from sample designs having different selection rates or with data having markedly different subgroup response rates. REFERENCES CITED Andrews, D.F., Bickel, P.J., Hampel, F.R., Huber, P.J., Rogers,W.H., and Tukey, J.W. ROBUST ESTIMATES OF LOCATION: SURVEY AND ADVANCES. Princeton: Princeton University Press, 1972. Andrews, F.M., and Messenger, R.C. MULTIVARIATE NOMINAL SCALE ANALYSIS. Ann Arbor: Institute for Social Research, The University of Michigan, 1973. Andrews, F.M., Morgan, J.N., Sonquist, J.A., and Klem, L. MULTIPLE CLASSIFICATION ANALYSIS. Second edition. Ann Arbor: Institute for Social Research, The University of Michigan, 1973. Blalock, H.M., Jr. Casual inferences, closed populations, and measures of association. AMERICAN POLITICAL SCIENCE REVIEW 61 (1967): 130-136. Blalock, H.M., JR. Can we find a genuine ordinal slope analogue? IN SOCIOLOGICAL METHODOLOGY 1976, edited by D.R. Heise. San Francisco: Jossey-Bass, 1975. Blalock, H.M., Jr. SOCIAL STATISTICS. Second edition, revised, New York: McGraw-Hill, 1979. [BMDP] Dixon, W.J., editor. BDMP STATISTICAL SOFTWARE 1981 MANUAL. Berkeley, California: University of California Press, 1981. Bock, R.D., and Haggard, E.A. The use of multivariate analysis of variance in behavioral research. In HANDBOOK OF MEASUREMENT AND ASSESSMENT IN BEHAVIORAL SCIENCES, edited by D.K. Whitla. Reading, Massachusetts: Addison-Wesley, 1968 Bock, R.D., and Yates, G. MULTIQUAL: LOG-LINEAR ANALYSIS OF NOMINAL OR ORDINAL QUALITATIVE DATA BY THE METHOD OF MAXIMUM LIKELIHOOD. User's Guide. Chicago: National Educational Resources, 1973. Borg, I., and Lingoes, J.C. A model and algorithm for multidimensional scaling with external constraints on the distances. PSYCHOMETRIKA 45 (1980): 25-38. Bowker, A.H., A test for symmetry in contingency tables. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 43 (1948): 572-574. Bradley, D.R., Bradley, T.D., McGrath, S.G., and Cutcomb, S.D. Type 1 error rate of the chi-square test of independence in RxC tables that have small expected frequencies. PSYCHOLOGICAL BULLETIN 86 (1979): 1290-1297. Bradley, J.V. DISTRIBUTION-FREE STATISTICAL TESTS. Englewood Cliffs, New Jersey: Prentice-Hall, 1968. Brown, M.B., and Forsythe, A.B. The small sample behavior of some statistics which test the equality of several means. TECHNOMETRICS 16 (1974a): 129-132. Brown, M.B., and Forsythe, A.B. Robust tests for the equality of variances. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 69 (1974b): 364-367. Camilli, G., and Hopkins, K.D. Applicability of chi-square to 2x2 contingency tables with small expected cell frequencies. PSYCHOLOGICAL BULLETIN 85 (1978): 163-167. Carroll, J.D., and Chang, J.J. Analysis of individual differences in multidimensional scaling via and N-way generalization of "Eckart-Young" decomposition. PSYCHOMETRIKA 35 (1970): 283-319. Carroll, J.D., Pruzansky, S., and Kruskal, J.B. CANDELINC: a general approach to multidimensional analysis of many-way arrays with linear constraints on parameters. PSYCHOMETRIKA 45 (1980): 3-24. Cohen, J. A coefficient of agreement for nominal scales. EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT 20 (1960): 37-46. Cohen, J. Weighted Kappa: nominal scale agreement with provision for scaled disagreement or partial credit. PSYCHOLOGICAL BULLETIN 70 (1968): 213-220. Conover, W.J. PRACTICAL NONPARAMETRIC STATISTICS. New York: John Wiley, 1971. Cooley, W.W., and Lohnes, P.R. MULTIVARIATE DATA ANALYSIS. New York: Wiley, 1971. D'Agostino, R.B. Simple compact portable test of normality: Geary's test revisited. PSYCHOLOGICAL BULLETIN 74 (1970): 138-140. Darlington, R.B. Reduced variance regression. PSYCHOLOGICAL BULLETIN 85 (1978): 1238-1255. Dempster, P., Schatzoff, M., and Wermuth, N. A simulation study of alternatives to ordinary least squares. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 72 (1977): 77-102. Dixon, W.J., and Massey, F.J., Jr. INTRODUCTION TO STATISTICAL ANALYSIS. Third edition. New York: McGraw-Hill, 1969. Draper, N.R., and Smith, H. APPLIED REGRESSION ANALYSIS. New York: Wiley, 1966. DuMouchel, W.H. The regression of a dichotomous variable. Unpublished. Survey Research Center Computer Support Group, Institute for Social Research, University of Michigan, 1974. DuMouchel, W.H. On the analogy between linear and log-linear regression. Technical Report No. 67. Unpublished. Department of Statistics, University of Michigan, March 1976. Feinberg, S.E. THE ANALYSIS OF CROSS-CLASSIFIED DATA. Cambridge, Massachusetts: The MIT Press, 1977. Fennessey, J., and d'Amico, R. Collinearity, ridge regression, and investigator judgement. SOCIOLOGICAL METHODS AND RESEARCH 8 (1980): 309-340. Fleiss, J.L., Cohen, J., and Everitt, B.S. Large sample standard errors of kappa and weighted kappa. PSYCHOLOGICAL BULLETIN 72 (1969): 323-327. Freeman, L.C. ELEMENTARY APPLIED STATISTICS FOR STUDENTS IN BEHAVIORAL SCIENCE. New York : Wiley, 1965. Gillo, M.W. MAID: A Honeywell 600 program for an automatised survey analysis. BEHAVIORAL SCIENCE 17 (1972): 251-252. Gillo, M.W., and Shelley, M.W. Predictive modelling of multivariable and multivariate data. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 69 (1974): 646-653. Glass, G.V., and Hakstian, A.R. Measures of association in comparative experiments: their development and interpretation. AMERICAN EDUCATIONAL RESEARCH JOURNAL 6 (1969): 403-414. Glass, G.V., Willson, V.L., and Gottman, J.M. DESIGN AND ANALYSIS OF TIME SERIES EXPERIMENTS. Boulder, Colorado: Colorado Associated University Press, 1975. Gokhale, D.V., and Kullback, S. THE INFORMATION IN CONTINGENCY TABLES. New York: Marcel Dekker, 1978. Goodman, L.A., and Kruskal, W.H. Measures of association for cross classifications. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 49 (1954): 732-764. Goodman, L.A., and Kruskal, W.H. Measures of association for cross classification III: approximate sampling theory. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 58 (1963): 310-364. Goodman, L.A., and Kruskal, W.H. Measures of association for cross classification IV: simplification of asymptotic variances. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 67 (1972): 415-421. Gorsuch, R.L. FACTOR ANALYSIS. Philadelphia: E.B. Saunders, 1974. Gross, A.J., and Clark, V.A. SURVIVAL DISTRIBUTIONS: RELIABILITY APPLICATIONS IN THE BIOMEDICAL SCIENCES. New York: Wiley, 1975. Guttman, L. A general nonmetric technique for finding the smallest coordinate space for a configuration of points. PSYCHOMETRIKA 33 (1968): 469-506. Hannan, M.T., and Tuma, N.B. Methods for temporal analysis. In ANNUAL REVIEW OF SOCIOLOGY: 1979, edited by A. Inkeles. Palo Alto: Annual Reviews, 1979. Hargens, L. A note on standardized coefficients as structural parameters. SOCIOLOGICAL METHODS AND RESEARCH 5 (1976): 247-256. Harris, R.J. A PRIMER OF MULTIVARIATE STATISTICS. New York: Academic Press, 1975. Harshbarger, T.R. INTRODUCTORY STATISTICS: A DECISION MAP. New York: Macmillan, 1971. Harshman, R.A. PARAFAC: Foundations of the PARAFAC procedure- models and conditions for an 'explanatory' multi-model factor analysis. WORKING PAPERS IN PHONETICS 16. Los Angeles: University of California at Los Angeles, 1970. Hartwig, F. EXPLORATORY DATA ANALYSIS. Beverly Hills, California: Sage, 1979. Hays, W.L. STATISTICS FOR THE SOCIAL SCIENCES. Second edition. New York: Holt, Rinehart, and Winston, 1973. Hertel, B.R. Minimizing error variance introduced by missing data routines in survey analysis. SOCIOLOGICAL METHODS AND RESEARCH 4 (1976): 459-474. Isaac, P.D, and Poor, D.D.S. On the determination of appropriate dimensionality in data with error. PSYCHOMETRIKA 39 (1974): 91-109. Joreskog, K.G., and Sorbom. D. LISREL: ANALYSIS OF LINEAR STRUCTURAL RELATIONSHIPS BY THE METHOD OF MAXIMUM LIKELIHOOD. Version IV. User's Guide, Chicago: National Educational Resources, 1978. Kalbfleisch, J.D., and Prentice, R.L. THE STATISTICAL ANALYSIS OF FAILURE TIME DATA. New York: Wiley, 1980. Kelley, T.L. An unbiased correlation ratio measure. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES 21 (1935): 554-559. Kendall, M.G. RANK CORRELATION METHODS. Fourth edition. London: Griffin, 1970. Kendall, M.G., and Stuart, A. THE ADVANCED THEORY OF STATISTICS, Volume 2. New York: Hafner, 1961. Kerlinger, F.N., and Pedhazur, E.J. MULTIPLE REGRESSION IN BEHAVIORAL RESEARCH. New York: Holt, Rinehart and Winston, 1973. Kim, J. Predictive measures of ordinal association. AMERICAN JOURNAL OF SOCIOLOGY 76 (1971): 891-907. Kim, J. Multivariate analysis of ordinal variables. AMERICAN JOURNAL OF SOCIOLOGY 81 (1975): 261-298. Kim, J., and Curry, J. The treatment of missing data in multivariate analysis. SOCIOLOGICAL METHODS AND RESEARCH 6 (1977): 215-240. Kim, J., and Mueller, C.W. Standardized and unstandardized coefficients in causal analysis. SOCIOLOGICAL METHODS AND RESEARCH 4 (1976): 423-438. Kirk, R.E. EXPERIMENTAL DESIGN: PROCEDURES FOR THE BEHAVIORAL SCIENCES. Belmont, California: Brooks/Cole, 1968. Krippendorff, K. Bivariate agreement coefficients for reliability of data. In SOCIOLOGICAL METHODOLOGY: 1970, edited by E.F. Borgatta and G.W. Bohrnstedt. San Francisco: Jossey-Bass, 1970. Kruskal, J.B. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. PSYCHOMETRIKA 29 (1964a): 1-27. Kruskal, J.B. Nonmetric multidimensional scaling: a numerical method. PSYCHOMETRIKA 29 (1964b): 115-130. Kruskal, J.B. Transformations of data. In INTERNATIONAL ENCYCLOPEDIA OF STATISTICS, Volume 2, edited by W.H. Kruskal and J.M. Tanur. New York: Crowell Collier and Macmillan. Originally published 1968. Copyright renewed in 1978 by The Free Press. Kruskal, J.B., and Wish, M. MULTIDIMENSIONAL SCALING. Beverly Hills, California: Sage, 1978. Kruskal, J.B., Young, F.W., and Seery, J.B. How to use KYST, a very flexible program to do multidimensional scaling and unfolding. Unpublished. Bell Laboratories, Murray Hills, New Jersey, 1973. Landis, J.R., Stanish, W.M., Freeman, J.L., and Koch,G.G. A computer program for the generalized chi-square analysis of categorical data using weighted least squares (GENCAT). COMPUTER PROGRAMS IN BIOMEDICINE 6 (1976): 196-231. Langeheine, R. Erwartete fitwerte fur Zufallskonfigurationen in PINDIS. ZEITSCHRIFT FUR SOZIALPSYCHOLOGIE 11 (1980): 38-49. Leinhardt, S., and Wasserman, S.S. Exploratory data analysis: an introduction to selected methods. In SOCIOLOGICAL METHODOLOGY 1979, edited by K.F. Schuessler. San Francisco: Jossey-Bass, 1978. Light, R.J. Measures of response agreement for qualitative data: some generalizations and alternatives. PSYCHOLOGICAL BULLETIN 76 (1971): 365-377. Lingoes, J.C., and Borg, I. Procrustean individual difference scaling. JOURNAL OF MARKETING RESEARCH 13 (1976): 406-407. Lingoes, J.C., Roskam, E.E., and Borg, I. GEOMETRIC REPRESENTATIONS OF RELATIONAL DATA. Second edition. Ann Arbor: Mathesis Press, 1979. MacCallum, R.C., and Cornelius, E.T. A Monte Carlo Investigation of recovery of structure by ALSCAL. PSYCHOMETRIKA 42 (1977): 401-428. Mayer, L.S., and Robinson,J.A. Measures of association for multiple regression models with ordinal predictor variables. IN SOCIOLOGICAL METHODOLOGY 1978, edited by K.F. Schuessler. San Francisco: Jossey-Bass, 1977. McCleary, R., and Hay, R.A.,Jr., with Meidinger, E.E., and McDowall, D. APPLIED TIME SERIES ANALYSIS FOR THE SOCIAL SCIENCES. Beverly Hills, California: Sage, 1980. McNemar, Q. PSYCHOLOGICAL STATISTICS. Fourth edition. New York: Wiley, 1969. [MIDAS] Fox, D.J., and Guire, K.E. DOCUMENTATION FOR MIDAS. Third edition, Ann Arbor: Statistical Research Laboratory, The University of Michigan, 1976. Morrison, D.F. MULTIVARIATE STATISTICAL METHODS. Second edition. New York: McGraw-Hill, 1976. Mosteller, F., and Tukey, J.W. DATA ANALYSIS AND REGRESSION. Reading, Massachusetts: Addison-Wesley, 1977. Neter, J., and Wasserman, W. APPLIED LINEAR STATISTICAL MODELS. Homewood, Illinois: Richard D. Irwin, 1974. Nunnally, J.C. PSYCHOMETRIC THEORY. Second edition. New York: McGraw-Hill, 1978. Olson, C.L. On choosing a test statistic in multivariate analysis of variance. PSYCHOLOGICAL BULLETIN 83 (19760: 579-586. Olsson, U. Maximum likelihood estimation of the polychoric correlation coefficient. PSYCHOMETRIKA 44 (1979): 443-460. Olsson, U. Measuring correlation in ordered two-way contingency tables. JOURNAL OF MARKETING RESEARCH 17 (1980): 391-394. [OSIRIS] Survey Research Center Computer Support Group. OSIRIS IV USER'S MANUAL. Seventh edition. Ann Arbor: Institute for Social Research, The University of Michigan, 1981. Overall, J.E., and Klett, C.J. APPLIED MULTIVARIATE ANALYSIS. New York: McGraw-Hill, 1972. Ramsay, J.O. Maximum likelihood estimation in multidimensional scaling. PSYCHOMETRIKA 42 (1977): 241-266. Rao, C.R. LINEAR STATISTICAL INFERENCE AND ITS APPLICATIONS. New-York: Wiley, 1965. Robinson, W.S. The statistical measurement of agreement. AMERICAN SOCIOLOGICAL REVIEW 22 (1957): 17-25. Rozeboom, W.W. Ridge regression: bonanza or beguilement? PSYCHOLOGICAL BULLETIN 86 (1979): 242-249. Sands, R., and Young, F.W. Component models for three-way data: an alternating least squares algorithm with optimal scaling features. PSYCHOMETRIKA 45 (1980): 39-68. [SAS] SAS Institute, Inc. SAS USER'S GUIDE, 1979 EDITION. Raleigh, North Carolina: SAS Institute, 1979. [SAS] SAS Institute, Inc. THE SAS SUPPLEMENTAL LIBRARY USER'S GUIDE, 1980 EDITION. Cary, North Carolina: SAS Institute, 1980. Siegel, S. NONPARAMETRIC METHODS FOR THE BEHAVIORAL SCIENCES. New York: McGraw-Hill, 1956. Smith, G., and Campbell, F. A critique of ridge regression methods. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 75 (1980): 74-81. Sneath, P.H.A., and Sokal, R.R. NUMERICAL TAXONOMY. San Francisco: W.H. Freeman, 1973. Snedecor, G.W., and Cochran, W.G. STATISTICAL METHODS. Sixth edition. Ames, Iowa: The Iowa State University Press, 1967. Somers, R.H. A new asymmetric measure of association for ordinal variables. AMERICAN SOCIOLOGICAL REVIEW 27 (1962): 799-811. Sonquist, J.A., Baker, E.L., and Morgan, J.N. SEARCHING FOR STRUCTURE. Revised edition. Ann Arbor: Institute for Social Research, The University of Michigan, 1974. Sorbom, D., and Joreskog, K.G. COFAMM: CONFIRMATORY FACTOR ANALYSIS WITH MODEL MODIFICATION. User's Guide. Chicago: National Educational Resources, 1976. Spence, I., and Graef, J. The determination of the underlying dimensionality of an empirically obtained matrix of proximities. MULTIVARIATE BEHAVIORAL RESEARCH 9 (1974): 331-342. Spence, I., and Ogilvie, J.C. A table of expected stress values for random rankings in nonmetric multidimensional scaling. MULTIVARIATE BEHAVIORAL RESEARCH 8 (1973): 511-517. [SPSS] Nie, N.H., Hull, C.H., Jenkins, J.G., Steinbrenner, K., and Bent, D.H. SPSS STATISTICAL PACKAGE FOR THE SOCIAL SCIENCES. Second edition. New York: McGraw-Hill, 1975. [SPSS] Hull, C.H., and Nie, N.H. SPSS UPDATE 7-9: NEW PROCEDURES AND FACILITIES FOR RELEASES 7-9. New York: McGraw-Hill, 1981. Srikantan, K.S. Canonical association between nominal measurements. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 65 (1970): 284-292. Statistical Research Laboratory. ELEMENTARY STATISTICS USING MIDAS. Second edition. Ann Arbor: Statistical Research Laboratory, The University of Michigan, 1976. Statistics Department, University of Chicago. ECTA program: description for users. Mimeographed paper, 1973. Stuart, A. The estimation and comparison of strengths of association in contingency tables. BIOMETRIKA 40 (1953): 105-110. Takane, Y., Young, F.W., and DeLeeuw, J. Nonmetric individual differences multidimensional scaling: an alternating least squares method with optimal scaling features. PSYCHOMETRIKA 42 (1977): 7-67. Tukey, J.W. EXPLORATORY DATA ANALYSIS. Reading, Massachusetts: Addison-Wesley, 1977. Young, F.W., and Torgerson, W.S. TORSCA, a FORTRAN IV program for Shepard-Kruskal multidimensional scaling analysis. BEHAVIORAL SCIENCE 12 (1976): 498. Yule, G.V., and Kendall, M.G. AN INTRODUCTION TO THE THEORY OF STATISTICS. Fourteenth edition. London: Griffin, 1957.
Volume in drive A has no label Directory of A:\ FILES949 TXT 1195 12-16-87 9:01a GO BAT 38 10-19-87 3:56p GO TXT 540 12-11-87 10:49a ORDER TXT 1385 8-05-87 3:53p STATCON 000 48640 8-05-87 2:57p STATCON COM 25960 8-05-87 2:57p STATCON DOC 40534 8-05-87 4:10p STATCONC 000 48640 8-05-87 3:57p STATCONC COM 25678 8-05-87 3:57p 9 file(s) 192610 bytes 123904 bytes free