Scholar Practitioner Project Public Health (ADVANCED ANALYSIS OF SECONDARY DATA SPSS)

Due 1/18/2020  8 p.m EST

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ALL DATA ATTACHED, AND PREVIOUS WORK ATTACHED TO USE AS GUIDE

  

Project: Scholar Practitioner Project

This week, you submit the next portion of your Scholar Practitioner Project. Use the techniques of data manipulation such as merging, transposing data, and creating new variables. Create a database that can be used to answer your proposed research questions completely. Please remember to incorporate your Instructor’s feedback from your data analysis plan before you submit your database.

For this Assignment, use your selected data set for your Scholar Practitioner Project and enter it into your SPSS program. You may manipulate the data in Excel and then convert them to a format for your statistical software needs.

· Apply techniques of data manipulation to prepare analysis data to answer your research question. RESEARCH QUESTION (Are people living in poverty more likely affected by obesity?)

· Define the variable names and categories.

Submit your prepared database.

Note from Professor

For last week and this week, students will be required to submit the SPSS databases for review.  The databases need to be in .sav format for me to be able to bring up on my SPSS software.  It will also be necessary to have files in a zip folder when submitting into the assignment section due to the size of the documents.  If no databases are submitted there will be a grade of 0 since the assignment requires the database(s) to be reviewed.  

Running head: SPSS SECO

N

DARY DATA 1

SPSS SECONDARY DATA 2

SPSS Secondary Data

Student’s Name

Course

Date

I chose to convert the variable Age into a categorical and named it New Var (Bryman & Crammer, 2005).

Histogram of Age

Bar Chart of the New Variable

I divided Weight2 by Height3 to create a new variable named

BMI

Descriptive Statistics for Height3 and Weight 2

Descriptive Statistics

N

Minimum

Maximum

Mean

Std

.

Deviation

HEIGHT3

7689

400.00

9999.00

5.9065E2

760.64424

WEIGHT2

7689

78.00

9999.00

5.2208E2

1711.73860

Valid N (listwise)

7689

Descriptive statistics for the new and original variables before spitting the data set.

Descriptive Statistics

N

Minimum

Maximum

Mean

HEIGHT3

7689

400.00

9999.00

5.9065E2

760.64424

WEIGHT2

7689

78.00

9999.00

5.2208E2

1711.73860

7689

Valid N (listwise)

7689

Std. Deviation

BMI

.01

24.33

.9404

3.17011

Descriptive statistics for both the original and new variables after splitting the new data set file based on the variable @_DENTS

N

Minimum

Maximum

Mean

Std. Deviation

HEIGHT3

400.00

9999.00

WEIGHT2

5708

78.00

9999.00

BMI

5708

.01

24.33

Valid N (listwise)

5708

Descriptive Statisticsa

5708

5.9923E2

805.65871

5.4049E2

1759.65992

.9640

3.23752

a. @_DENTS = 1.00

Descriptive Statisticsa

N

Minimum

Maximum

Mean

Std. Deviation

HEIGHT3

WEIGHT2

868

9999.00

BMI

868

Valid N (listwise)

868

868

405.00

7777.00

5.4026E2

427.85070

82.00

4.9765E2

1650.33347

.02

20.00

.9611

3.23711

a. @_DENTS = 2.00

Descriptive Statisticsa

N

Minimum

Maximum

Mean

Std. Deviation

HEIGHT3

9999.00

WEIGHT2

1113

9999.00

BMI

1113

.02

Valid N (listwise)

1113

1113

408.00

5.8593E2

723.71695

85.00

4.4672E2

1494.86849

19.88

.8030

2.73752

a. @_DENTS = 9.00

Rationale for Creating New Variables

New variables are usually created to in order to come up with a scale measure that merges various existing variables into one single variable, for instance, to simplify a phenomenon of interest (Argyrous, 2011). In our case we created a new variable called BMI by dividing the given weight by the height so as to measure the level of fat in the body based on height and weight (Weinberg & Abramowitz, 2008)

.

Interpretation of Results

Before splitting the data sets, the variables Weight2, Hieght3 and BMI had mean of 5.9923E2, 5.2208E2 and 0.9404 respectively and standard deviation 760.64424, 1711.73860 and 3.17011 respectively. However, after the data set was split their mean are 5.9065E2, 5.4049E2 and .9640, while their standard deviation is 805.65871, 1759.65992 and 3.23752 respectively. Based on the results, it can be deduced that splitting of the datasets has significant effects since the means and the standard deviations defer to some extent (Enders, 2010).

References

Argyrous, G. (2011). Statistics for Research: With a Guide to SPSS. Thousand Oaks, CA: SAGE.

Bryman, A., & Cramer, D. (2005). Quantitative Data Analysis with SPSS 12 and 13: A Guide for Social Scientists. Psychology Press.

Enders, C. K. (2010). Applied Missing Data Analysis. New York, NY: Guilford Press.

Weinberg, S. L., & Abramowitz, S. K. (2008). Statistics Using SPSS: An Integrative Approach. Cambridge, CA: Cambridge University Press.

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