HELP PLEASE!! End of the semester and am struggling to finish this last assignment!!

Assignment instructions: Using the SPSS data set on Canvas, 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 your SPSS guide and/or your instructors.

Looks long but only 4 problems/analyses, just lots of data information!!

Data:

Data Set Background & Problems

Infants Subject Sex Food Quality Aggression Long Calls Mean FACs Large Quantity FACs 20 25 Small Quantity FACs 16 21 17 Medium Quantity FACs 15 19 17 17 1 2 3 4 5 6 5 Male High Male Medium Male Low Female High Female Medium Female Low 8 4 3 113 17 21.7 17.7 17.7 20.7 14 18 18 20 2 13 DAN Juveniles Subject Sex Aggression Large Quantity FACs Medium Quantity FACs Quality Small Quantity FACs Long Calls Mean FACs 7 11 8 16 Male High Male Medium Male Low Female High Female Medium Female Low 9 10 11 12 19 15 16 ca 17.3 18.3 16.3 12.3 17 17 12 10 18 14 17 128 Adults Subject Sex Aggression Food Quality Large Quantity FACs Medium Quantity FACs Small Quantity FACS Long Calls Mean FACs 13 15 14 10 14 15 24 15 16 Male High Male Medium Male Low Female High Female Medium Female Low 16 13 18 18 14 14.7 16.3 15.7 17 12.7 24 11 2 1 18 14 14 10 14 Data set background The attached data are based on the dissertation research of Dr. Addington (your fearless leader). 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. 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 measures the number of FACs given per hour in the presence of each quantity. This is a 'within-subjects' manipulation - that is, I 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 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). 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 Tukev test on within-subiects 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. Infants Subject Sex Food Quality Aggression Long Calls Mean FACs Large Quantity FACs 20 25 Small Quantity FACs 16 21 17 Medium Quantity FACs 15 19 17 17 1 2 3 4 5 6 5 Male High Male Medium Male Low Female High Female Medium Female Low 8 4 3 113 17 21.7 17.7 17.7 20.7 14 18 18 20 2 13 DAN Juveniles Subject Sex Aggression Large Quantity FACs Medium Quantity FACs Quality Small Quantity FACs Long Calls Mean FACs 7 11 8 16 Male High Male Medium Male Low Female High Female Medium Female Low 9 10 11 12 19 15 16 ca 17.3 18.3 16.3 12.3 17 17 12 10 18 14 17 128 Adults Subject Sex Aggression Food Quality Large Quantity FACs Medium Quantity FACs Small Quantity FACS Long Calls Mean FACs 13 15 14 10 14 15 24 15 16 Male High Male Medium Male Low Female High Female Medium Female Low 16 13 18 18 14 14.7 16.3 15.7 17 12.7 24 11 2 1 18 14 14 10 14 Data set background The attached data are based on the dissertation research of Dr. Addington (your fearless leader). 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. 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 measures the number of FACs given per hour in the presence of each quantity. This is a 'within-subjects' manipulation - that is, I 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 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). 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 Tukev test on within-subiects 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