KPerkins-MFT7110-7.2-Assignent

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Feb 20, 2024

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Quantitative Analysis in MFT Northcentral University Assignment 7: Generate t-Test Results Using SPSS In this assignment, you will conduct the three types of t-tests: 1) independent samples, 2) one-sample, and 3) paired samples. You will do so using two datasets: SPSS Dataset.Adolescent FT.sav (for parts 1 and 2) and SPSS Dataset.Fad Comparisons.sav (for part 3). These datasets contain hypothetical data obtained from a family therapy clinic evaluating an integrative model of family therapy provided to adolescents living in female-headed single parent families. Families were recruited to participate in seven sessions of therapy and were assessed at the beginning and end of the study. The data in both datasets were the data gathered at the beginning of the study. The first dataset, SSPS Datast.Adolescent.FT.sav , contains 13 variables: (a) Participant.ID (Each person’s identification number), (b) Fam.ID (Family identification number), (c) Race (1. African American, 2. European American, 3. Mexican American, 4. Multiracial) (d) Fam.Pos (Family position: 1. Mother, 2. Adolescent) (e) Adol.Gender (Gender of adolescent: 1. Female, 2. Male) (f) Ther.ID (Therapist identification number) (g) Pre.Prob (The adolescent’s presenting problem: 1. Depression, 2. Oppositional Defiant Disorder, 3. Anxiety) (h) PHQ9 (Scores on the Patient Health Questionnaire-9, a brief depression screening measure; Kroenke, Spitzer, & Williams, 2001) (i) Anxiety (Scores on a measure of anxiety developed for this study) (j) FAD (Scores on the Family Assessment Device; item scores are averaged, with higher scores representing perceptions of poorer family functioning; Mansfield, Keitner, & Dealy, 2015) (k) Life.Sat (Scores on a measure of life satisfaction created for this study) (l) Alliance (Scores on a measure of the therapeutic alliance) (m) Drop.Out (Did the family drop out prior to the completion of the seven- session study treatment protocol?) The second dataset, SPSS Dataset.FAD Comparisons.sav , includes a subsample of data from the overall study dataset and includes three variables: (a) Fam.ID (Family identification number), (b) FAD.P (Parent’s FAD score), and (c) FAD.C (Child’s FAD score).

A primary assumption of inferential statistics is that the data are randomly selected independent observations (there’s no connection between any members of the sample). Any time you collect data from more than one member of a family, that assumption is violated as these observations are not independent (data from both members of a couple or from a parent and his or her child are related and thus not independent). There are a few ways to handle this issue; most of which are beyond the scope of this course. The simplest is to pull subsamples from the data set and only use independent observations. So, in any one analysis, you can either use data from the parents or the adolescents, but not both. You will do so in parts 1 and 2 of this assignment. Part 1: Independent samples t-test Individuals use independent samples t-tests to examine differences associated with a specific dependent variable using an independent variable with exactly two levels or groups (e.g., do supervisors and supervisees differ in their perceptions of the helpfulness of supervisor interventions during live supervision?). For this part of the assignment, you are interested in whether there are gender differences in adolescent life satisfaction in the population from which you have drawn your sample. a. State the null and alternative hypotheses. There is a trend in the literature for adolescent boys to report higher levels of life satisfaction than adolescent girls, so factor that into your alternative hypothesis. Null Hypothesis: There are no gender differences in adolescent life satisfaction (I’m curious if the specific population would need to be mentioned here?) Alternative Hypothesis: There is a significant difference between adolescent gender in terms of life satisfaction. b. Conduct the independent samples t-test in SPSS (refer to Chapter 7 in the Schwartz et al. text). Your Test Variable is Life Satisfaction and the Grouping Variable is Adol.Gender (note you will have to replace the two question marks after Adol.Gender by clicking on Define Groups and typing in 1 for Group 1 (females are Group 1) and 2 for Group 2 (males are group 2). c. Report the results of the t-test in APA format (model your language after the example in the Schwartz et al. text – page 85) We analyzed the data using an independent samples t-test. Adolescent gender did significantly affect life satisfaction, t (48) = -.501, p = .62, d = .04. Adolescent males (M=17.57, SD=4.290, n=28)

stated significantly higher, life satisfaction than adolescent females (M=16.91, SD=5.061, n=22). d. Indicate what you learned about these two variables after conducting this analysis. I think I learned that due to the significance level being less than .05 shows a statistically significant affect. Part 2: One-sample t-test You are interested in whether your sample of single parents is statistically different from the general population in terms of their self-reported levels of depression. Suppose the average score on the PHQ-9 from a national normative sample of adult females was 4.6. Consider 4.6 the population mean score on the PHQ-9 and conduct a one-sample t-test to evaluate the null hypothesis that this sample came from a population with a mean PHQ-9 score of 4.6. To only calculate these results for the single parents, you will need to exclude the adolescents from the analysis. You do so by only including the single parents in your analysis. To do so, follow these steps: 1. Open the dataset and click on Data in the top menu. 2. Go to the next to last command in the dropdown menu, which is Select Cases . Click on that. 3. Click on the second option (If condition is satisfied) and click on the If button. 4. Move your cursor to the left and highlight Family Position , and then click the arrow to move it into the white box at the top of the page. 5. Next, type =1 so the text in that box reads Fam.Pos=1 6. Click on Continue at the bottom of the box. This will return you to the Select Cases dropdown. Click on the OK button at the bottom. 7. Check the dataset. You should notice a slash in the row number by all the adolescents (Family Position=2). The adolescents will not be included in the analysis. Now you are ready to complete the analysis. a. State the null and alternative hypotheses. Given that the sample is in therapy, do you anticipate they will report higher levels of depression than the general population? Your alternative hypothesis should reflect your response to this question. Null Hypothesis: There is no difference in self-report levels of depression between a single-parent population, and a general population. Alternative Hypothesis: There is a significant difference in self- report levels of depression between a single-parent population and a

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general population. (<< does this need to be stated more specifically? Aka: Levels of single parent self-report depression scores will be higher than general population self-reported depression scores?) b. Conduct the one-sample t-test in SPSS (refer to the following steps): 1. With the data set open, click Analyze  Compare Means  One- Sample T Test. 2. Move variables to the Test Variable(s) area by selecting them in the list and clicking the arrow button. For this analysis you will use the PHQ-9 as the Test Variable. 3. Enter the Test Value (which = 4.6 for this analysis) into the test value box. 4. Select the box next to Estimate effect size. 5. Click Okay to run the analysis. c. Report the results of the t-test in APA format. Your Test Variable is PHQ-9 and the Test Value is 4.6. A one sample t-test was used to analyze whether there was a difference between self-report depression scores (PHQ-9) in a single- parent population as compared to a general population. The mean PHQ-9 (M=7.50, SD=3.436) show a statistically significant difference given that <.001 is less than .05. (<<< I am a little unclear on this and could use some feedback as to whether or not this is correct or written correctly) d. Indicate what you learned about your sample after conducting this analysis. Similar to the first section, I believe I learned that there was a statistically significant difference between PHQ-9 scores of a single-parent population as compared to a general population. Part 3: Paired samples t-test It is generally the norm, rather than the exception, for family members to have different perceptions of how their family functions. For this task, you will conduct a paired samples t-test to evaluate the null hypothesis that there are no differences in FAD scores between parents and their adolescent children. Paired samples t-tests assume the data are linked and not independent, so you will use data from two members of each family in your sample. You will use a different SPSS dataset for this assignment, SPSS Dataset.Week 7b.sav a. State the null and alternative hypotheses. The language in the alternative hypothesis should reflect that you are not sure who might report higher or

lower levels of family functioning (parents or adolescents). Null Hypothesis: There is no difference in the self-report family functioning of parents as compared to their adolescent children. Alternative Hypothesis: There is a significant difference in the self- report family functioning of parents as compared to their adolescent children. b. Conduct the paired samples t-test in SPSS (refer to the One Independent Variable (IV) with Two Levels section in Chapter 7 in the Schwartz et al. text for guidance). You will click on Parent’s FAD score to move it into the Variable 1 box and Child’s FAD score to move it into the Variable 2 box . c. Report the results of the t-test in APA format (model your language after the example in the Schwartz et al. text). A paired samples t-test was used to analyze differences in self-report family functioning in parents (M=2.216, SD= .8539, n=50) in comparison to adolescent children (M=2.582, SD=.9211, n=50). The results showed no statistically significance due to a p value of .245 which is larger than a p-value of .05. d. Indicate what you learned about these parent-child dyads after conducting this analysis. As before, I believe I learned that there was no significant difference in self-report scores between parenst and adolescent children due to the p-value being larger than .05. I am hoping I am interpreting significant differences correctly.

References Kroenke, K., Spitzer, R. L., & Williams, J. B. (2001). The PHQ-9. Journal of General Internal Medicine, 15, 606-613. Mansfield, A. K., Keitner, G. I., & Dealy, J. (2015). The Family Assessment Device: An update. Family Process, 54, 82-93.

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