Psych 210 Spring 2024 SPSS Assignment

Hello, dear friend, you can consult us at any time if you have any questions, add WeChat: daixieit

Psych 210

Spring 2024

SPSS Assignment (20 pts.)

Due date is the final exam time: Friday, May 10^th @ 7:45 AM.

If you would like to turn the assignment in earlier, please turn it in to Dr. Addington’s mailbox (2^nd floor of Psych, around the corner from TA mailboxes. Look for faculty/staff section outside the door to Room 238, the main Psych office).  DO NOT turn in to your TA’s mailbox. 

Assignment instructions

Using the SPSS data set, conduct four statistical analyses using SPSS, and answer the questions associated with each analysis.  Instructions for each analysis are below.  For further information on SPSS, consult SPSS instruction documents and/or your instructors.  

IMPORTANT: Although it is acceptable to discuss your assignment with other students, you MUST enter and analyze data INDEPENDENTLY.  You may NOT use data that another student has entered for you.  Failing to enter/analyze data independently constitutes academic misconduct, and will be treated accordingly.

Data set background

The attached data are based on the dissertation research of Dr. Addington.  She studied captive pygmy marmosets, a small South American monkey species.  These monkeys live in small family groups consisting of a breeding adult male-female pair and their offspring.  Some offspring are older juveniles, while other offspring are infants.  Dr. A was interested in two main areas of behavior: vocal communication and aggression.  The following definitions explain the type of data collected for each column of the data set.

1. Age:  Age of subject, classified in three categories (one table per age group).

· Infant = Offspring less than 6 months of age (n = 6)

· Juvenile = Offspring over 6 months of age (n = 6)

· Adult = Breeding adult over 18 months of age (n = 6)

2. Subject:  Subject number (N = 18 total).

3. Sex:  Sex of subject

4. Food Quality:  Experimental manipulation.  One type of pygmy marmoset vocalization, the food-associated call (FAC), is hypothesized to vary according to the preferability, or quality, of food.  To test this idea, Dr. A presents monkeys with either high quality food (hamburger), medium quality food (grapes), or low quality food (monkey chow), and measures the number of FAC calls per hour that are given in the presence of the food.  The Food Quality column indicates which type of food each monkey was presented with.  This is a ‘between-subjects’ manipulation – that is, each monkey is presented with only one food quality condition.

5. Food Quantity:  Experimental manipulation.  FAC calls are also hypothesized to vary according to how much food the monkey is presented with.  Dr. A. presents each monkey with three conditions: a large quantity of food, a medium quantity of food, and a small quantity of food.  Then she measures the number of FACs given per hour in the presence of each quantity.  This is a ‘within-subjects’ manipulation – that is, each monkey is presented with all three conditions (each monkey is measured repeatedly).  The numbers in the ‘Large Quantity’ ‘Medium Quantity’ and ‘Small Quantity’ columns indicate the number of FACs given in each condition.

6. Long Call:  The second vocalization type that Dr. A. measures.  These calls are long, loud vocalizations that are hypothesized to be used for intergroup communication.  The numbers in the column indicate the number of long calls given per hour.

7. Aggression:  In addition to studying vocalizations, Dr. A. is also interested in aggressive behavior.  The numbers in the column indicate the number of aggressive acts each monkey performs per hour.

8. Mean FACs:  This column shows the average number of FACs given per monkey (averaged across the high, medium, and low food quantity conditions).

Analyses (4 total)

For all analyses, assume a two-tailed test with a = .05.  Provide/print the complete SPSS output for each analysis (means, test statistics, source tables; include all table titles and headings), unless otherwise noted.  Then answer the question(s) regarding the outcome of the statistical test listed for each analysis.  You do not need to write out any hypothesis testing steps for the analyses, nor do any hand calculations.  

1. Choose ONE of the following three analyses:

a. Run an independent-samples t-test that examines whether there is a significant difference in the number of long calls according to sex of subject.

· Is there a significant difference?  (Use the ‘sig’ value for ‘t-test for equality of means, equal variances assumed’.)  How do you know?

· Describe the results in one or two sentences, making sure to mention the IV, the DV, and the direction of the difference between means (that is, make sure to state which mean is higher/lower).

b. Run an independent-samples t-test that examines whether there is a significant difference in the number of aggressive acts according to sex of subject.

· Is there a significant difference?  (Use the ‘sig’ value for ‘t-test for equality of means, equal variances assumed’.)  How do you know?

· Describe the results in one or two sentences, making sure to mention the IV, the DV, and the direction of the difference between means (that is, make sure to state which mean is higher/lower).

c. Run a related-samples t-test that examines whether there is a significant difference in the number of FACs given to medium quantities of food vs. low quantities of food.

· Is there a significant difference?  (Use the ‘sig’ value in the ‘paired samples test’ box.)  How do you know?

· Describe the results in one or two sentences, making sure to mention the IV, the DV, and the direction of the difference between means (that is, make sure to state which mean is higher/lower).

2. Choose ONE of the following two analyses:

a.  Run a single-factor, between-subjects ANOVA that examines whether there is an effect of age on the number of aggressive acts.  Be sure to ask for ‘Descriptives’ (Options button) and for a Tukey test (Post-hoc button) as you run the analysis.  ** Ignore/delete the ‘homogeneous subsets’ box.**

· Is the overall ANOVA significant?  How do you know?

· If the ANOVA is significant, describe the differences between the means (in one or two sentences) that are shown by the Tukey test.  If the ANOVA is not significant, describe the pattern that the means fall into, but state that the differences between the means are not significant.

b.  Run a single-factor, between-subjects ANOVA that examines whether there is an effect of food quality on the number of FACs.  Use the ‘Mean FACs’ column for the dependent variable.  Be sure to ask for ‘Descriptives’ (Options button) and for a Tukey test (Post-hoc button) as you run the analysis.  ** Ignore/delete the ‘homogeneous subsets’ box.**

· Is the overall ANOVA significant?  How do you know?

· If the ANOVA is significant, describe the differences between the means (in one or two sentences) that are shown by the Tukey test.  If the ANOVA is not significant, describe the pattern that the means fall into, but state that the differences between the means are not significant.

3. Run a single-factor, within-subjects ANOVA that examines the effect of food quantity on the number of FACs.  Use the ‘Large Quantity FACs’, ‘Medium Quantity FACs’, and ‘Small Quantity FACs’ columns.  Ask for ‘Descriptives’ (Options button) as you run the analysis.  SPSS does not allow you to run a Tukey test on within-subjects designs.  **Ignore/delete the ‘multivariate tests’, ‘Mauchly’s test of sphericity’, ‘tests of within-subjects contrasts’, and ‘tests of between-subjects effects’ boxes.**

· Is the overall ANOVA significant?  (Use the ‘sig’ value in the ‘tests of within-subjects effects box, and on the ‘sphericity assumed’ line.)  How do you know?

· If the ANOVA is significant, describe the differences between the means (in one or two sentences).  Since you cannot use Tukey results here, just describe the pattern that the means fall into. If the ANOVA is not significant, describe the pattern that the means fall into, but state that the differences between the means are not significant.

4. Run a factorial between-subjects ANOVA that examines the effects of sex and age on the number of long calls.  Ask for ‘Descriptives’ (Options button) as you run the analysis.  

· Which of the three possible effects are significant?  How do you know?

· Draw a graph of the interaction or noninteraction, using a line graph that depicts cell means.

· Describe the pattern of the interaction (or noninteraction) in two or three sentences.