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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
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