Cropper, C. 1977. "Recovery of Patients from Stroke." OzDASL – Australasian Data and Story Library. Accessed from http://www.statsci.org/data/oz/stroke.html on Dec. 15, 2015.
Essenberg, C. J., R. A. Easter, R. A. Simmons, and D. R. Papaj. 2015. The value of information in floral cues: bumblebee learning of floral size cues. Unpublished raw data.
Hanley, J. A., and Shapiro, S. H. 1994. Sexual Activity and the Lifespan of Male Fruitflies: A Dataset That Gets Attention. Journal of Statistics Education 2(1). Accessed from http://www.amstat.org/publications/jse/v2n1/
Rasmussen, Marianne. "Activities of Dolphin Groups." OzDASL – Australasian Data and Story Library. Accessed from http://www.statsci.org/data/general/dolpacti.html on December 15, 2015.
Wilson, Richard J. "Pulse Rate Before and After Exercise." OzDASL – Australasian Data and Story Library. Accessed from http://www.statsci.org/data/oz/ms212.html on December 15, 2015.
Analyze > Nonparametric Tests > Legacy Dialogs > Binomial
Requires one variable with two values, and an expected proportion. In SPSS, the variable must be stored as a numeric type and either nominal or scale measure.
If the data are available only as a frequency table, and not as a column with values as shown above, you will have to enter the data as a weighted table, with one categorical (numeric) variable and a count (integer) variable containing the frequency. The table will have two rows, one for each of the two values. See instructions for applying the frequency weight for your table.
Analyze > Nonparametric Tests > Legacy Dialogs > Chi Square
Requires one categorical variable with values of expected frequencies. In SPSS, the variable must be stored as a numeric type and either nominal or scale measure.
If the data are available only as a frequency table, and not as a column with values as shown above, you will have to enter the data as a weighted table, with one categorical variable and a count (scale) variable containing the frequency. The table will have two rows, one for each of the two values. See instructions for applying the frequency weight for your table.
Analyze > Descriptive Statistics > Crosstabs
Requires two categorical variables, with two or more possible values.
If the data are available only as a frequency table, and not as a column with values as shown above, you will have to enter the data as a weighted table, with two categorical (numeric) variables and a count (integer) variable containing the frequency. The table will have one row for each possible combination of the two categorical variables; for example, if both categorical variables have three possible values, there will be 9 (3 x 3) rows. See instructions for applying the frequency weight for your table.
Analyze > Descriptive Statistics > Crosstabs
Requires two categorical variables with two possible values each.
If the data are available only as a frequency table, and not as a column with values as shown above, you will have to enter the data as a weighted table, with one categorical variable and a count (integer) variable containing the frequency. The table will have four rows, one for each combination of the two variables. See instructions for applying the frequency weight for your table.
Analyze > Compare Means > One-Sample T Test
Requires one normally distributed numerical variable and a hypothesized mean. See instructions for checking for normality.
Analyze > Nonparametric Tests > Legacy Dialogs > Binomial
Requires one numerical variable and a hypothesized median. The numerical variable does not need to be normally distributed.
Analyze > Compare Means > Independent-Samples T Test
Requires one normally distributed, numerical variable and one grouping variable with two values. The grouping variable may be numeric-type or string-type. See instructions for checking for normality.
Analyze > Compare Means > Paired-Samples T Test
Requires two numerical variables that are paired. Paired samples are matched in some way; often they represent the same object or respondent tested at different points in time.
Analyze > Compare Means > One-Way ANOVA
Requires one normally distributed, numerical response variable and one categorical grouping variable with two or more values. See instructions for checking for normality. Note that the grouping variable must be numeric. If it is a string variable, like TREATMENT in the image below, you will need to create a numeric-type variable using Automatic Recode.
Analyze > Compare Means > One-Way ANOVA
Requires one numerical response variable and one grouping variable with two values. See instructions for checking for normality. Note that the grouping variable must be numeric. If it is a string variable, like TREATMENT in the image below, you will need to create a numeric-type variable using Automatic Recode.
Analyze > General Linear Models > Univariate
Requires one normally distributed numerical response variable and two categorical grouping variables with two or more values. See instructions for checking for normality. Note that the grouping variable must be numeric. If it is a string variable, like TREATMENT in the image below, you will need to create a numeric-type variable using Automatic Recode.
Analyze > Nonparametric > Legacy Dialogs > 2 Independent Samples
Requires one numerical or ordinal variable, and one grouping variable with two values. The grouping variable must be numeric. If it's not numeric, use Automatic Recode to create a new numeric variable. The continuous variable does not need to be normally distributed.
Analyze > Nonparametric > Legacy Dialogs > K Independent Samples
Requires one numerical or ordinal variable, and one grouping variable with two or more values. If it is a string variable, like TREATMENT in the image below, you will need to create a a numeric variable using Automatic Recode.
Analyze > Regression > Linear
Requires two numerical variables. See also instructions for creating regression plots.
Analyze > Correlate > Bivariate
Requires two numerical variables. See setup for Simple linear regression above.
Analyze > Correlate > Bivariate.
Requires two numerical variables. See setup for Simple linear regression above.
Analyze > General Linear Model > Univariate
Requires one numerical dependent variable and any combination of independent numerical or categorical numeric variables.