Statistics - ANOVA Projects

Use the data from the Case, Weapons and Aggression . Click on the following links to read the Background,

Use the data from the Case, Weapons and Aggression . Click on the following links to read the Background, the Experimental Design, and the Materials used.

You can work from the attached SPSS file …. [In the interests of effective pedagogy, I've added a bit to the data set.]

Here are the variables in the SPSS file:

Var. Number	VARIABLE	MEASURE
1	Subject number	coded 1-32
2	Sex code	1 = female, 2 = male
3	Age	range = 16 - 39
4	Time with Aggressive target word and weapon prime (1)	time in seconds
5	Time with Aggressive target word and non-weapon prime (2)	time in seconds
6	Time with Non-aggressive target word and weapon prime (3)	time in seconds
7	Time with Non-aggressive target word and non-weapon prime (4)	time in seconds
8	Weapons prime effect : (3) – (1)	difference between v6 and v4
9	Non-weapons prime effect : (4) – (2)	difference between v7 and v5
10	Difference - total effect of weapons and aggression	difference between v8 and v9
11	subject's height	in inches
12	subject's weight	in pounds
13	subject's involvement with NRA gun safety course	1 = taken, passed high 2 = taken, passed low 3 = taken, failed 4 = no encounter with course 5 = picketed against course
14	subject's score on Basic Assessment of Emotional Intelligence inventory	score in points (higher = more emotionally mature)

Assignment

WHEN THE DATA FILE IS READY, please conduct the following analyses and write up a conclusion
1.Prepare basic descriptive statistics for each of variables 2-14
Using> RECODE < or just by adding the variable into the data file manually, construct a new 'ordinal' variable representing female =1 or not female = 0. Construct a new 'ordinal' variable for "age categories" with three levels coded 1, 2, and 3, dividing the sample into three groups: ages 16-19, 20-25, and 26-39.
Using a 't-test,' test for differences between the difference scores (V10) when the grouping variable is V2 (1=female, 2=males).
Conduct a basic one-way ANOVA with the difference score (v10) as the dependent variable and the new age-category variable with its three levels as the independent variable (use > Presentation 12 <,
Interpret the output, and transfer it to the final report.
Conduct a > Tukey HSD post-hoc test < if warranted. If conducted, interpret the results.
Some useful predictor variables are, as you know, interval rather than categorical, like Variables 11, 12,. and 14 in the present data set. At present, we have no techniques that we have learned that would enable us to use these variables as predictors in a hypothesis test; both t-tests and ANOVA make use of categorical predictors. Sometimes we find it useful to turn such interval variables into categories for such analyses. The SPSS RECODE command allows you to do this easily. If we are trying to split our subjects into two groups so that we can use a t-test, then the median split is the most common procedure; you can use the "split value" in the t-test procedure to do this by entering the median value of the predictor variable that you have obtained from a descriptive statistics listing. If you want to divide the sample into three or more groups for an ANOVA, then RECODE is the way to go.
As the final part of this exercise, pick two from among Variables 11, 12, and 14 and turn them into categorical variables with two or three categories, using either median split or recode. Use these variables and/or Variable 13 (which is, as you will have noted, already in categorical form) as predictors in t-tests or ANOVAs using the difference score as dependent variable. Present the results of your analyses and say what they tell you, and why you chose to do them.
Transfer your output to Word ^® and format it in an appropriate manner.
Conclude your report with a couple of paragraphs or so of analysis, in which you provide an overall interpretation of your data analysis and draw conclusions of appropriate scope about its value and applicability.