*DATA HELP
Type the Number of your choice & Press the [ENTER] key.  Press [ESC] to
 remove a Choice Box without registering a choice.  ESCaping from the first
 Box will return you to the MAIN MENU; ESCaping from any other will return
 you to the previous Choice Box and allow you to select a different option.
*RANDOMNESS
Type the Number of your choice & Press the [ENTER] key.
Choose RANDOM SAMPLING if a definite random sampling scheme was used to draw
 the sample from a pre-defined population.  The sampling scheme could be a
 SIMPLE RANDOM SAMPLE, a SYSTEMATIC RANDOM SAMPLE, or a COMPLEX PROBABILITY
 SAMPLE, such as a stratified random sample or a multi-stage cluster sample.
Choose RANDOM ASSIGNMENT if cases were randomly divided into sub-groups, as
 in a true experimental design or randomized clinical trial.  (If both
 random sampling and random assignment were involved - a rare event - either
 choice '1' or '2' may be selected at your option).
Choose NON-RANDOM SAMPLING if no random process was used to generate the
 sample and neither of the above options applies.  This choice would be
 appropriate if cases represent a CONVENIENCE SAMPLE, AVAILABILITY SAMPLE,
 QUOTA SAMPLE, or a whole POPULATION.  It is also appropriate for QUASI-
 EXPERIMENTAL designs, where cases are divided into naturally occurring
 groups or assigned to treatments according to judgmental criteria.
*INDEPENDENCE
Choose OPTION 1 if BOTH of the following conditions hold:
 a) The analysis will divide the cases into sub-samples for comparison, but
    there will be NO MATCHING of cases across sub-samples AND there was no
    matching inherent in the procedure used to draw the sample.
 b) Each case will be represented ONLY ONCE in the analysis: no before-after
    or repeated measures will be involved.
Choose OPTION 2 if the analysis will compare MATCHED SUB-SAMPLES, e.g.,
 husbands vs. wives or people with & without a certain disease who are
 matched on age and sex.  Choose OPTION 2 if each case will be represented
 more than once in the analysis, as in a before-after, repeated measures, or
 cross-over experiment.
Choose OPTION 3 if the sample will not be split into sub-samples and no
 between-group comparisons will be made.
*QUESTION
Type the Number of your choice & Press the [ENTER] key.
Choose OPTION 1 if you're interested in describing the distribution of only
 one variable AND no comparisons will be made between sub-samples.
Choose OPTION 2 if you're interested in describing the association or
 correlation between two or more variables AND no comparisons will be made
 between sub-samples.
Choose OPTION 3 if you're interested in describing or measuring differences
 across two or more sub-samples.
Choose OPTION 4 if you're interested in sub-sample differences (as in '3'),
 but you also want to control or adjust for some extraneous variable(s) that
 you have measured for the same cases.
*COMPLEX
Type the Number of your choice & Press the [ENTER] key.
Choose OPTION 1 if you have only one variable to describe or summarize AND
 no sub-sample comparisons are involved. [You MUST choose this option if you
 chose "Univariate Analysis" in the previous Choice Box.]
Choose OPTION 2 if exactly two variables will be used in the analysis.  Note
 that each sub-sample breakdown counts as a variable.
Choose OPTION 3 if three or more variables will be used in the analysis AND
 only one of these will be designated as the DEPENDENT variable to be
 explained or predicted by the other (independent) variables.
Choose OPTION 4 if multiple variables will be used in the analysis AND two
 or more of these will be designated as DEPENDENT variables to be explained
 or predicted by the other (independent) variables.
Choose OPTION 5 if multiple variables will be used in the analysis BUT no
 variable(s) will be designated as DEPENDENT.  WATSTAT will assume that all
 variables are INDEPENDENT variables.  [ NOTE: if you choose OPTION 5 here,
 you should also choose "Not Applicable" on the next Choice Box, which
 asks for more information about your DEPENDENT variable.]
*DEP VAR.
 [Levels of Measurement are defined near the end of this message in a
  section labelled BASIC TERMS & CONCEPTS.  Scroll to that section if
  you need a review of terminology.]
Choose OPTION 1 if the dependent variable(s) is measured on a NOMINAL scale
 AND if it would not be appropriate to dichotomize it.
Choose OPTION 2 if the dependent variable(s) is in the form of RANKS or
 if it is measured on an ORDINAL scale that could be transformed to ranks.
 This option also assumes relatively few ties: as a rule of thumb, fewer
 than half the cases should be tied on the dependent variable.
Choose OPTION 3 if the dependent variable(s) is a set of 3 or more ORDINAL
 categories, so that all cases in a given category are tied.  Choose this
 option also if most but not all cases are tied.
Choose OPTION 4 if the dependent variable(s) is measured on an INTERVAL
 scale that produces either a continuous distribution or a distribution of
 3 or more INTERVAL-LEVEL categories.
Choose OPTION 5 if the dependent variable(s) is DICHOTOMOUS, irrespective of
 its level of measurement.
Choose OPTION 6, "Not Applicable", if no variable will be designated as a
 dependent or "outcome" variable.  You will be asked to specify levels of
 measurement for your variables in the next Choice Box.

---------------------- BASIC TERMS & CONCEPTS ----------------------------
Variables measured at the NOMINAL LEVEL identify differences between cases,
 but assume no underlying hierarchy along which cases can be ordered from
 "lowest" to "highest".  Examples of nominal variables: race and hair color.
 (The special case of Dichotomous Nominal Variables is noted below.)
Variables measured at the ORDINAL LEVEL identify differences between cases
 in such a way that they can be ordered from "lowest" to "highest", but do
 not specify how much lower or higher any case is relative to any other.
 Examples of ordinal variables: class rank, tennis seeds, and the order
 in which horses finish a race.
 A "pure" ordinal variable distinguishes each case from every other so they
 can be rank-ordered without any ties.  In practice, ties commonly occur,
 and statisticians have devised ways to compensate for them.  If there are
 relatively few ties simple adjustments to rank-order statistics usually
 suffice, but when there many ties an alternative to rank-order procedures
 may be needed.  Such alternatives are commonly used for "partially ordered"
 scales, which arrange cases into a hierarchy of categories but make no
 distinctions within categories.  With this sort of variable, every
 case is tied with at least one other case.
Variables measured at the INTERVAL LEVEL identify differences between cases
 in such a way that they are not only ordered from lowest to highest but the
 SIZE of their differences can be stated in terms of a UNIT OF MEASUREMENT.
 Examples: Age measured in years and Income measured in dollars.
DICHOTOMIES: If a variable divides cases into only two categories, it is
 often legitimate to assign arbitrary scores to its categories, e.g., 0 & 1,
 and treat it as an Interval Variable even if it was initially defined as a
 Nominal or Ordinal Variable.
*IND VAR.
 [Levels of Measurement are defined near the end of this message in a
  section labelled BASIC TERMS & CONCEPTS.  Scroll to that section if
  you need a review of terminology.]
Choose OPTION 1 if ALL independent variables are Nominal and it is not
 appropriate to dichotomize all of them.
Choose OPTION 2 if ALL independent variables are in the form of RANKS or if
 they are measured on ORDINAL scales that could be transformed to ranks.
 This option also assumes relatively few ties: as a rule of thumb, fewer
 than half the cases should be tied on any independent variable.
Choose OPTION 3 if ALL the independent variables are sets of 3 or more
 ORDINAL categories, so that all cases in a given category are tied.
 Choose this option also if, for all the independent variables, over half
 of the cases are tied.
Choose OPTION 4 if ALL the independent variables are measured on INTERVAL
 scales.  Choose this option also if some or all the independent variables
 are DICHOTOMIES, even if the dichotomies result from NOMINAL or ORDINAL
 scales.
Choose OPTION 5 if the independent variables consist of both NOMINAL and
 ORDINAL scales.  This would be appropriate if each of the variables would
 fit under OPTIONS 1, 2, OR 3.
Choose OPTION 6 if some independent variables would fit under OPTION 4, but
 others would best fit under OPTIONS 1, 2, OR 3.

---------------------- BASIC TERMS & CONCEPTS ----------------------------
Variables measured at the NOMINAL LEVEL identify differences between cases,
 but assume no underlying hierarchy along which cases can be ordered from
 "lowest" to "highest".  Examples of nominal variables: race and hair color.
 (The special case of Dichotomous Nominal Variables is noted below.)
Variables measured at the ORDINAL LEVEL identify differences between cases
 in such a way that they can be ordered from "lowest" to "highest", but do
 not specify how much lower or higher any case is relative to any other.
 Examples of ordinal variables: class rank, tennis seeds, and the order
 in which horses finish a race.
 A "pure" ordinal variable distinguishes each case from every other so they
 can be rank-ordered without any ties.  In practice, ties commonly occur,
 and statisticians have devised ways to compensate for them.  If there are
 relatively few ties simple adjustments to rank-order statistics usually
 suffice, but when there many ties an alternative to rank-order procedures
 may be needed.  Such alternatives are commonly used for "partially ordered"
 scales, which arrange cases into a hierarchy of categories but make no
 distinctions within categories.  With this sort of variable, every
 case is tied with at least one other case.
Variables measured at the INTERVAL LEVEL identify differences between cases
 in such a way that they are not only ordered from lowest to highest but the
 SIZE of their differences can be stated in terms of a UNIT OF MEASUREMENT.
 Examples: Age measured in years and Income measured in dollars.
DICHOTOMIES: If a variable divides cases into only two categories, it is
 often legitimate to assign arbitrary scores to its categories, e.g., 0 & 1,
 and treat it as an Interval Variable even if it was initially defined as a
 Nominal or Ordinal Variable.
*SIZE
NUMBER OF CASES is the number of distinct "units" that make up the sample,
 e.g., people or animals.  NB: If more than one observation is made on each
 case, as in a before-after experiment, "Number of Cases" is not the same as
 "Number of Observations."  If you do not know the exact N of Cases, use a
 best-guess estimate.  Allowed range is 3 to 9999.  If sample size is 10,000
 or over, enter "9999."  [WATSTAT uses N of Cases in deciding on procedures
 to recommend and in deciding when to warn you about potential limitations.]
*N-OF-COMP-VARS
What you enter here depends on the Analytical Focus you specified in Box 3.
   ---------------------------------------------------------------------
If you chose "Univariate Analysis" as your Analytical Focus in Box 3,
 simply enter '1' here.
If you chose "Association Between Variables" in Box 3, enter the number of
 INDEPENDENT VARIABLES you wish to use in your analysis.  The ALLOWED RANGE
 of values you can enter is 1 thru 20.  Enter '1' for a Bivariate Analysis
 and '2' or more for a Multivariate analysis.  Use the maximum '20' if you
 have over 20 Independent variables.
If you chose "Sub-Sample Differences" as your Analytical Focus in Box 3,
 enter the number of COMPARISON VARIABLES, as explained below.
 "COMPARISON VARIABLE" is WATSTAT's name for a variable whose function is to
 identify Sub-Samples to be compared.  It is really an Independent variable,
 and may have any level of measurement.  BUT due to its special function it
 is always treated as a set of Nominal categories.  E.G., 'sex' would be
 a Comparison Variable if you compared the average memory-test scores of men
 and women.  If you also divided the sample into age groups, in order to
 assess age-by-sex differences, 'age' would be a second Comparison Variable.
 The ALLOWED RANGE of values you may enter is 1 thru 20.  At least one
 Comparison Variable is implied if you chose "Sub-sample Differences " in
 Box 3.  Use the maximum '20' if you have over 20, but note that such a
 large No. of Comparison Variables may be unrealistic.  [DON'T CONFUSE the
 No. of Comparison Variables with "No. of Categories" of those variables.]
*SUBSAMPLE
What you enter here depends on the Analytical Focus you specified in Box 3.
   ---------------------------------------------------------------------
If you chose "Sub-Sample Differences" as your Analytical Focus in Box 3,
 AND you specified the No. of Comparison Variables in this Box, you must
 now tell WATSTAT how many Sub-Samples you wish to compare.  The ALLOWED
 RANGE of values you can enter is 2 thru 99, BUT you must have at least 2
 Sub-samples for each Comparison Variable you counted previously.  Enter the
 maximum '99' if you have 100 or more Sub-samples.  Be sure to count ALL the
 sub-samples.  E.G., if comparisons are to be made by sex (2 categories) and
 age (5 categories), you'd enter '10' as the No. of Sub-Samples.  Likewise,
 for experiments with multiple treatment factors, count EACH COMBINATION of
 treatments & treatment levels as a separate sub-sample.  NB: For this item,
 "Sub-samples" are GROUPS of CASES OR OBSERVATIONS, so you should also count
 Before-After observations and other "repeated measures" as sub-samples, even
 though the same cases are involved.]
If you chose "Univariate" or "Association Between Variables" in Box 3, AND
 IF you chose "Nominal," "Partially Ordered," or "Mixed: Nominal & Ordinal"
 levels of measurement Box 6, enter the TOTAL NUMBER OF CATEGORIES for all
 Nominal and "Partially Ordered" INDEPENDENT variables COMBINED.  Don't
 count categories for Dependent variables nor for Independents that are
 "True" Ordinal or Interval variables, even if the latter are grouped.
 The ALLOWED RANGE of values you can enter is 0 thru 99.  [Enter '0' if all
 Independent variables are measured at the Interval and/or 'True' Ordinal
 levels.  Use '99' if you have 100 or more Categories.]  Be sure to count
 ALL Categories; e.g., if data will be cross-tabulated by sex (2 categories)
 and age (5 categories), enter '10' as the No. of Categories.
*MENU HELP
[This message has 9 lines: keep scrolling down to read all of it]
You can Select from the Menu in either of two ways: 1) use the    arrow keys
 to highlight a choice and then press [ENTER], or 2) simply type the Number
 of your desired choice.  Watch the Message Line at the bottom of the
 screen for usage information.  Whenever it says "[F1] for Help" you can
 Press [F1] to bring up a Help Window like this.  Always scroll down with
 the [ ] key until no new lines appear in a Help Window.  [More  ]
When a Help Window is displayed, press [ALT] and [F1] together to expand it
 to full-screen size: this spares you from scrolling through longer help
 messages.  To shrink a full-screen Help Window, press [ALT] & [F1] again.
*RUNIT
Choose this item if you're ready to start running WATSTAT.  The first of a
 series of 'Choice Boxes' will pop onto the screen.
Press [F1] now if you need help in using the Menu, [ESC] to return to it.
*TUTOR
Choose this option if you're new to WATSTAT.  It explains how WATSTAT works
 and how to run it.  It also explains how WATSTAT results should be used.
Press [F1] now if you need help in using the Menu, [ESC] to return to it.
*SOUND
WATSTAT makes a 'trill' sound to alert you when a new Choice Box is popped
 onto the screen.  Choose SOUND from the menu if you wish to turn the trill
 sound ON or OFF.  [WATSTAT also beeps if you make an error, but this sound
 this sound can't be turned off.]  Press [F1] now if you need help in using
 the Menu; press [ESC] to return to the Menu.
*QUITIT
Choose QUIT to terminate WATSTAT and exit to DOS.  A window will pop onto
 the screen and ask for confirmation.  All prior work is erased on exit.
Press [F1] now if you need help in using the Menu, [ESC] to return to it.
*COPYRIGHT
 COPYRIGHT 1991 BY HAWKEYE SOFTWORKS, 300 GOLFVIEW AVE., IOWA CITY, IA, 52246

