Lab 4 Prompt - SUM22

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Arizona State University, Tempe *

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Economics

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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.
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