M8A HW

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3500

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Statistics

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Feb 20, 2024

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Name: Module 8 Assignment Instructions Read the problem set information and data below, then complete steps 1, 2, and 3 using SPSS. Use your output and scatterplot to complete step 4. Paste the required information in the spaces indicated in this document for steps 1, 2, and 3. Complete the template paragraph as indicated in step 4. Be sure to use your values and labels as discussed in your learning materials. Problem Set A researcher is interested in the association between the age of the participant and the number of chronic health diagnoses. The chronic health score was created by totaling the number of chronic health diagnoses out of 9. The 9 chronic health disorders included Kidney Disease, Stroke, Cancer excluding skin, COPD, Heart Attack/Myocardial Infarction, Diabetes, Asthma, Arthritis, and Depression. The researcher randomly approaches people at a local park asking them to participate. A total of 25 participants agreed. Each participant was asked their age in years and to indicate for each of the 9 chronic health disorders, what, if any, they have been diagnosed with. Age HealthScore 85 9 76 9 80 8 63 7 71 7 45 5 52 6 51 6 25 0 29 5 37 3 41 4 31 1 49 4 92 8 45 4 30 2 39 6 66 4 22 0 28 7 37 1 46 1 69 8 70 5
Instructions for the Problem Set Step 1: SPSS Data and Variable View 1. Enter the data into SPSS in Data view. 2. Complete the following five tasks in Variable view: a. Name the variables. b. Set the number of decimal places to match the data. c. Label the variables. d. Set the measure. 3. Snip/export both Data View and Variable View from SPSS. 4. Paste Data View here: 5. Paste Variable View here: Step 2: SPSS Output 1. Run a Pearson r Correlation Analysis. a. Include all options used in the tutorial videos. 2. Run a Linear Regression Analysis. a. Include all options used in the tutorial videos. TIP: Make sure you properly identify the IV & DV in the scenario 3. Snip/export the output from SPSS. 4. Paste the output here: Step 3: SPSS Scatterplot 1. In SPSS, run a scatterplot of the data with the regression line based on the regression equation. TIP: Be sure to add the regression equation (as demonstrated in the tutorial video: typed, not handwritten) 2. Include the following settings: a. Y axis starts at 0. b. Title both the x & y axis based on the scenario.
c. No title above the graph. d. Regression line with typed Regression Equation. 3. Snip/export the graph from SPSS. 4. Paste the graph here: Step 4: Results 1. Fill in the blanks and replace any guiding information in the paragraph below for the Pearson r Correlation write-up. As you do so, follow these guidelines: a. If correlation is not significant, omit the third sentence. b. N and degrees of freedom are reported as whole numbers. c. Degrees of freedom are hand calculated as N - 2 . d. r is rounded and reported to two decimal places. e. p is reported to three decimal places. o Only exception: when SPSS reads p = “.000” o Then, you should type p < .001 NOTE: Nothing should be underlined in your write ups. The underlining in the template paragraphs represent blank places that should be filled in with information A Pearson r correlation coefficient was calculated to examine the association between DV and DV ( N = __). The correlation was significant r ( df ) = ___, p = ___. Interpretation of correlation, including correct direction, only if correlation is significant. 2. Based on the research scenario, select the correct Linear Regression paragraph from the two options below. 3. Fill in the blanks and replace any guiding information in the paragraph you selected for the Linear Regression write-up. As you do so, follow these guidelines: a. degrees of freedom (in parentheses) are reported as whole numbers. b. R 2 , F , and β are rounded and reported to two decimal places. c. p is reported to three decimal places. o Only exception: when SPSS reads p = “.000” o Then, you should type p < .001 NOTE: Nothing should be underlined in your write ups. The underlining in the template paragraphs represent blank places that should be filled in with information
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Option 1: Significant A linear regression analysis examined whether IV can predict DV . The regression was significantly different from zero, R 2 = __, F ( df regression, df residual) = __, p = __. DV changed (increased or decreased) β for each unit of IV . Option 2: Not Significant A linear regression analysis was conducted to predict (Y: the dependent variable) based on (X: the independent variable). The regression was not significant, R 2 = __, F ( df regression, df residual) = __, p = ___. 4. Paste the completed Pearson r Correlation paragraph here: 5. Paste the completed Linear Regression paragraph here: