Scholar Practitioner Project Public Health (ADVANCED ANALYSIS OF SECONDARY DATA SPSS)
vwccspt12
Guide8560.docRunning head: SPSS SECONDARY 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
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Descriptive Statistics |
|||||
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|
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 |
Std. Deviation |
|
HEIGHT3 |
7689 |
400.00 |
9999.00 |
5.9065E2 |
760.64424 |
|
WEIGHT2 |
7689 |
78.00 |
9999.00 |
5.2208E2 |
1711.73860 |
|
BMI |
7689 |
.01 |
24.33 |
.9404 |
3.17011 |
|
Valid N (listwise) |
7689 |
|
|
|
|
Descriptive statistics for both the original and new variables after splitting the new data set file based on the variable @_DENTS
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Descriptive Statisticsa |
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|
N |
Minimum |
Maximum |
Mean |
Std. Deviation |
|
HEIGHT3 |
5708 |
400.00 |
9999.00 |
5.9923E2 |
805.65871 |
|
WEIGHT2 |
5708 |
78.00 |
9999.00 |
5.4049E2 |
1759.65992 |
|
BMI |
5708 |
.01 |
24.33 |
.9640 |
3.23752 |
|
Valid N (listwise) |
5708 |
|
|
|
|
|
a. @_DENTS = 1.00 |
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|
|
|
|
Descriptive Statisticsa |
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|
|
N |
Minimum |
Maximum |
Mean |
Std. Deviation |
|
HEIGHT3 |
868 |
405.00 |
7777.00 |
5.4026E2 |
427.85070 |
|
WEIGHT2 |
868 |
82.00 |
9999.00 |
4.9765E2 |
1650.33347 |
|
BMI |
868 |
.02 |
20.00 |
.9611 |
3.23711 |
|
Valid N (listwise) |
868 |
|
|
|
|
|
a. @_DENTS = 2.00 |
|
|
|
|
|
Descriptive Statisticsa |
|||||
|
|
N |
Minimum |
Maximum |
Mean |
Std. Deviation |
|
HEIGHT3 |
1113 |
408.00 |
9999.00 |
5.8593E2 |
723.71695 |
|
WEIGHT2 |
1113 |
85.00 |
9999.00 |
4.4672E2 |
1494.86849 |
|
BMI |
1113 |
.02 |
19.88 |
.8030 |
2.73752 |
|
Valid N (listwise) |
1113 |
|
|
|
|
|
a. @_DENTS = 9.00 |
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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.
