Lab 4 Prompt - SUM22

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Feb 20, 2024

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SPSS Lab 4 SPSS Lab 4 Directions: A Phoenix research firm wants to hire you to help them examine the impact of different kinds of conflict communication behavior on relationship satisfaction in romantic couples. The firm thinks it has identified two really important behaviors during conflict that help explain how satisfied couples are in their relationship: how often someone places blame or criticizes their partner and how often someone offers compromise or solutions during conflict. You realize right away that you know how to help them come up with answers using linear regression! In the dataset provided you will find the variables needed to run your tests. Your tasks: 1. Run a linear regression analysis in SPSS with ‘placeblame’ as your predictor variable and ‘relationalsatisfaction’ as the criterion variable and interpret the regression coefficient. a. Create a scatterplot with the regression line for these variables 2. Run another linear regression analysis in SPSS with ‘suggestsolutions’ as your predictor variable and ‘relationalsatisfaction’ as the criterion variable and interpret your findings a. Create a scatterplot with the regression line for these variables. 3. Present your scatterplots for both tests (so 2 scatterplots) and use them to give a brief conclusion about what you found. Regression Output for ‘placeblame’: Copy and paste all the SPSS output boxes from your linear correlation below (3pts)

SPSS Lab 4 Raw Interpretations for ‘placeblame’: Interpret the significance of the regression coefficient and use the unstandardized coefficient to explain how X impacts Y’. ( 8pts ) When t-test was ran, a value of -10.042 was computed, which had a p-value of .001. This means that the regression coefficient is in fact significant because .001 is less than .05. When explaining how X impacts Y, when looking at our “Coefficients” output box, we are able to see that for every one unit change when measuring “placeblame” (X), the “relationshipsatisfaction” (Y) value decreases by .622. Regression Output for ‘suggestsolutions’: Copy and paste all the SPSS output boxes from your linear correlation below (3pts) Raw Interpretations for ‘suggestionsolutions’ Interpret the significance of the regression coefficient and use the unstandardized coefficient to explain how X impacts Y’. ( 8pts ) When our t-test was ran, SPSS computed a value of 1.798 which gave us a p-value of .075. This regression coefficient is considered insignificant as .075 is greater than .05. When comparing the relationship between X and Y, we can see when looking at the unstandardized coefficients that for every one unit change when measuring “suggestsolutions” (X), our Y value, “relationshipsatisfaction” actually increases by .148.

SPSS Lab 4 Scatterplots Copy and Paste the two scatterplots you created (one for each regression). Using the scatterplots and the results of your tests, give the research firm a brief conclusion ( at least 4 sentences ) about what you found in simple non-stats language. ( 8pts ) When analyzing this scatterplot, we are able to see that the relationship between these two variables shows a moderate, negative linear regression. The data is not bunched up at the line, and the Y values gradually makes their way down as the X value increases.

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SPSS Lab 4 When analyzing this scatterplot, we are able to see that the relationship between these two variables reveals a slight, positive linear regression. The data is a bit scattered, but for the most part, most of the data is in the upper right section of the graph. We can see that as the X value increased, the Y value slightly increased as well.

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