What statistical analysis should I use?

Is there a significant difference in the Fear of Stats test scores for par- ticipants in the maths skills group and the confidence building group, while controlling for their scores on this test at Time 1?

Analysis of covariance (ANCOVA) 1+ categorical IVs (two or more levels) 1 continuous covariate one continuous dependent variable

Analysis of covariance

Analysis of covariance is like ANOVA, except in addition to the categorical predictors you also have continuous predictors as well.

What is the relationship between gender and dropout rates from therapy?

Chi square one categorical IV one categorical DV The number of cases in each category is considered, not scores

Which intervention (maths skills/confidence building) is more effective in reducing participants' fear of statistics, measured across three time periods?

Mixed between- within ANOVA one between-groups IV, (two or more levels) one within-groups IV (two or more levels) one continuous DV Two or more groups with different people in each group, each measured on two or more occasions

How much of the variance in life satisfaction scores can be explained by self- esteem, perceived control and optimism? Which of these variables is the best predictor?

Multiple regression two or more continuous IVs one continuous DV

Is there a difference between males and females, across three different age groups, in terms of their scores on a variety of adjustment measures (anxiety, depres- sion, and perceived stress)?

Multivariate ANOVA (MANOVA) 1+ categorical IVs (two or more levels) 2+ related continuous DVs

Is there a difference in optimism scores for people who are under 35yrs, 36-49yrs and 50+ yrs?

One-way between groups ANOVA one categorical IV (three or more levels) one continuous DV Three or more groups: different people in each group

Is there a change in participants' anxiety scores from Time 1,Time 2 and Time 3?

One-way repeated measures ANOVA one categorical IV (three or more levels) one continuous DV 3 or more groups: same people on two different occasions

Is there a change in participants' anxiety scores from Time 1 to Time 2?

Paired samples t-test one categorical IV (two levels) one continuous DV Same people on two different occasions

After controlling for the effects of socially desirable responding bias, is there still a relationship between optimism and life satisfaction?

Partial correlation two continuous variables and one continuous variable you wish to control for

Is there a relationship between age and optimism scores?

Pearson correlation coefficient (r) two continuous variables One sample with scores on two different measures, or same measure at Time 1 and Time 2

Is there a difference in the optimism scores for males and females, who are under 35yrs, 36-49yrs and 50+ yrs?

Two-way between groups ANOVA two categorical IVs (two or more levels) one continuous dependent variable 2+ groups for each IV, diff people in each group

Simple logistic regression

assumes that the outcome variable is binary (i.e., coded as 0 and 1). We have only one variable in the hsb2 data file that is coded 0 and 1, and that is female. We understand that female is a silly outcome variable (it would make more sense to use it as a predictor variable), but we can use female as the outcome variable to illustrate how the code for this command is structured and how to interpret the output. The first variable listed after the logistic command is the outcome (or dependent) variable, and all of the rest of the variables are predictor (or independent) variables. In our example, female will be the outcome variable, and read will be the predictor variable. As with OLS regression, the predictor variables must be either dichotomous or continuous; they cannot be categorical.

One-way MANOVA

like ANOVA, except that there are two or more dependent variables. In a one-way MANOVA, there is one categorical independent variable and two or more dependent variables.

Multiple logistic regression

like simple logistic regression, except that there are two or more predictors. The predictors can be interval variables or dummy variables, but cannot be categorical variables. If you have categorical predictors, they should be coded into one or more dummy variables.

Simple linear regression

look at the linear relationship between one normally distributed interval predictor and one normally distributed interval outcome variable.

Factorial ANOVA

two or more categorical independent variables (either with or without the interactions) and a single normally distributed interval dependent variable. For example, using the hsb2 data file we will look at writing scores (write) as the dependent variable and gender (female) and socio-economic status (ses) as independent variables, and we will include an interaction of female by ses.

Multivariate multiple regression

two or more dependent variables that are to be predicted from two or more independent variables

Multiple regression

very similar to simple regression, except that in multiple regression you have more than one predictor variable in the equation.

Ordered logistic regression

when the dependent variable is ordered, but not continuous. For example, using the hsb2 data file we will create an ordered variable called write3. This variable will have the values 1, 2 and 3, indicating a low, medium or high writing score. We do not generally recommend categorizing a continuous variable in this way; we are simply creating a variable to use for this example. We will use gender (female), reading score (read) and social studies score (socst) as predictor variables in this model.

Factorial logistic regression

when you have two or more categorical independent variables but a dichotomous dependent variable. For example, using the hsb2 data file we will use female as our dependent variable, because it is the only dichotomous variable in our data set; certainly not because it common practice to use gender as an outcome variable. We will use type of program (prog) and school type (schtyp) as our predictor variables.

One-way repeated measures ANOVA

you had one categorical independent variable and a normally distributed interval dependent variable that was repeated at least twice for each subject. This is the equivalent of the paired samples t-test, but allows for two or more levels of the categorical variable.

One-way ANOVA

you have a categorical independent variable (with two or more categories) and a normally distributed interval dependent variable and you wish to test for differences in the means of the dependent variable broken down by the levels of the independent variable.