Normal Data Distribution and Two-Variable Correlation Testing

The dependent t-test, also called paired t-test, compares two groups for a dependent variable measured at the interval or ratio level as well; however, these two groups are in reality just one group. But because they are measured before and after an intervention, we consider them as two groups for analytical purposes. This group is considered dependent because nothing is expected to vary in the nature of the individuals being measured except as a result of the intervention, as the group is composed of the same individuals.

Overview

One of the most important steps along the researcher’s path to data analysis is to become familiar with the character of the raw data collected for the project. Before weaving the strands of data into an analytical story that is related to a study’s goals, researchers typically inspect the completeness and quality of the data with various visualization techniques (graphics), summary tables, and mathematical tests of quality (assumption tests), as discussed in Assessment 2. One of these latter tests is a correlation analysis. With this approach, the researcher performs a very basic series of exploratory tests on variable pairs to identify any potentially interesting (yet unknown) relationships between groups of data (variables). Correlational analyses are often later performed as part of the predetermined data analysis plan to answer a specific research question.

Demonstration of Proficiency

By successfully completing this assessment you will address the following scoring guide criteria, which align to the indicated course competencies.

• Competency 1: Describe underlying concepts and reasoning related to the collection and evaluation of quantitative data in health care research. 

  Interpret the overall clinical meaning and limitations of the relationship of two variables, based on a correlation analysis and literature regarding age and stress.

• Competency 2: Apply appropriate statistical methods using common software tools in the collection and evaluation of health care data. 

  Create a histogram and scatter plot for variables tested for normal distribution.
  Perform a normal distribution assumption test for two variables to determine if data is normally distributed.
  Perform an appropriate correlation test to determine the direction and strength or magnitude of the relationship between two variables.

• Competency 3: Interpret the results and practical significance of statistical health care data analyses. 

  Interpret the effect size for correlation analysis results.

• Competency 5: Address assignment purpose in a well-organized text, incorporating appropriate evidence and tone in grammatically sound sentences. 

  Articulate meaning relevant to the main topic, scope, and purpose of the prompt. 
  Apply APA formatting to in-text citations and references.

Instructions

For this three-part assessment, complete the following, referring to 

Yoga Stress (PSS) Study Data Set [XLSX]

, which you have used previously, as needed.

Software

The following statistical analysis software is required to complete your assessments in this course:

IBM SPSS Statistics Standard or Premium GradPack, version 22 or higher, for PC or Mac.

You have access to the more robust IBM SPSS Statistics Premium GradPack.

Please refer to the

Statistical Software

page on Campus for general information on SPSS software, including the most recent version made available to Capella learners.

Part 1: Graphic Representation of the Data from the Yoga Stress (PSS) Study Data Set

1. Create a histogram or bar graph (according to the measurement level of the data) of the following variables: Age, Education, Pre-intervention Psychological Stress Score (PSS). 

   Refer to the following resources as needed while creating your histogram: 

   SPSS Tutorials. (n.d.). What is a histogram? Retrieved from https://www.spss-tutorials.com/histogram-what-is-it/
   SPSS Tutorials. (n.d.). Creating histograms in SPSS. Retrieved from https://www.spss-tutorials.com/creating-histograms-in-spss/
   Creating Histograms in SPSS.

2. Create a scatter plot of the following pair of variables: Age versus Pre-intervention Psychological Stress Score (PSS). 

   Refer to the following resources, as needed, while creating your scatterplot: 

   Displaying Relationships: Scatterplot.
   Interpreting Scatterplots.

Part 2: Statistical Tests

1. Perform a preanalysis assumption test for a normal distribution test to determine if the data you intend to use for the correlation tests passes the assumption of being normally distributed. 

   You will use this test for Age and Pre-intervention Psychological Stress Score (PSS).

2. Perform the appropriate correlation test to determine the direction and strength or magnitude of the relationship between these two variables from Step 1. 

   Remember, we are not concerned about causation at this point and want to determine only if there is a statistical association.

Part 3: Yoga Stress (PSS) Study Paper

• Include the histogram and scatter plot graphics you created earlier for Age and Pre-intervention Psychological Stress Score (PSS). 

  Provide an interpretation for these graphics.

• Report the statistical outcome of the correlation analysis using appropriate scholarly style, including a brief interpretation of the effect size of the correlation.
• Interpret the practical, real-world meaning (and limitations of the interpretation) of the relationship of these two variables based on the correlation analysis you performed.
• Include the SPSS “.sav” output file that shows your programming and results from Parts 1 and 2 for this assessment.
• Provide at least one evidence-based scholarly or peer-reviewed article that supports your interpretation.

HDAP 8070-02 DATA

01

Male

active duty

Male

active duty

Male

Some college active duty

25

Male African American Graduate education or above active duty 22

African American College graduate active duty

16

22 Female African American Some college active duty 14

18 Female Asian Some college active duty 22 16

Female Caucasian College graduate active duty 23 20

Female

Some college active duty 33

30 Male Native American College graduate

22

Male

US Civilian 25 15

Female African American College graduate US Civilian

10

Female African American Less than HS US Civilian 12 12

Male Asian

US Civilian 17 12

Male Caucasian HS graduate US Civilian 10 10

33 Male Caucasian Graduate education or above US Civilian

22

44 Female Hispanic College graduate US Civilian 18 12

55 Female Hispanic College graduate US Civilian 12 10

Female Native American College graduate US Civilian 16 9

11/27/20, 1:48 PM

Normal Data Distribution and Two-Variable Correlation Testing Scoring Guide

Page 1 of 2https://courserooma.capella.edu/bbcswebdav/institution/NHS-FPX/NHS-FPX8070/200700/Scoring_Guides/a03_scoring_guide.html

Normal Data Distribution and Two-Variable Correlation Testing Scoring Guide

CRITERIA NON-PERFORMANCE BASIC PROFICIENT DISTINGUISHED

Create a histogram
and scatter plot for
variables tested for
normal distribution.

Does not create a
histogram for
variables tested for
normal distribution.

Creates a histogram for
variables tested for normal
distribution, but the
histogram or curve is
flawed.

Creates a
histogram for
variables tested
for normal
distribution.

Creates a histogram for
variables tested for normal
distribution and explains
what the histogram shows.

Perform a normal
distribution
assumption test for
two variables to
determine if data is
normally distributed.

Does not perform
a normal
distribution
assumption test for
two variables to
determine if data is
normally
distributed.

Performs a normal
distribution assumption
test for two variables to
determine if data is
normally distributed, but
the test is performed
incorrectly or is otherwise
flawed.

Performs a
normal
distribution
assumption test
for two variables
to determine if
data is normally
distributed.

Performs a normal
distribution assumption test
for two variables to
determine if data is
normally distributed and
explains how the test was
conducted.

Perform an
appropriate
correlation test to
determine the
direction and
strength or
magnitude of the
relationship between
two variables.

Does not perform
an appropriate
correlation test to
determine the
direction and
strength or
magnitude of the
relationship
between two
variables.

Performs an appropriate
correlation test to
determine the direction
and strength or magnitude
of the relationship
between two variables,
but the test is performed
incorrectly or is otherwise
flawed.

Performs an
appropriate
correlation test to
determine the
direction and
strength or
magnitude of the
relationship
between two
variables.

Performs an appropriate
correlation test to determine
the direction and strength
or magnitude of the
relationship between two
variables, and explains how
the test was conducted.

Interpret the effect
size for correlation
analysis results.

Does not interpret
the effect size for
correlation
analysis results.

Interprets the effect size
for correlation analysis
results, but the
interpretation is inaccurate
or otherwise flawed.

Interprets the
effect size for
correlation
analysis results.

Interprets the effect size for
correlation analysis results
and explains how the effect
size was identified.

Interpret the overall
clinical meaning and
limitations of the
relationship of two
variables, based on a
correlation analysis
and literature
regarding age and
stress.

Does not interpret
the overall clinical
meaning and
limitations of the
relationship of two
variables, based
on a correlation
analysis and
literature regarding
age and stress.

Interprets the overall
clinical meaning and
limitations of the
relationship of two
variables, based on a
correlation analysis and
literature regarding age
and stress, but the
interpretation in
incomplete, inaccurate, or
otherwise flawed.

Interprets the
overall clinical
meaning and
limitations of the
relationship of two
variables, based
on a correlation
analysis and
literature
regarding age
and stress.

Interprets the overall clinical
meaning and limitations of
the relationship of two
variables, based on a
correlation analysis and
literature regarding age and
stress. Explains how the
clinical meaning and
limitations might affect
future decisions.

Articulate meaning
relevant to the main
topic, scope, and
purpose of the
prompt.

Writing is
unrelated to the
assignment
prompt.

Addresses a specific topic
with unclear intent or
insufficient depth.

Articulates
meaning relevant
to the main topic,
scope, and
purpose of the
prompt.

Articulates a focused
response to the assignment
prompt and demonstrates a
thorough understanding of
the main topic, scope, and
purpose.

11/27/20, 1:48 PMNormal Data Distribution and Two-Variable Correlation Testing Scoring Guide

Page 2 of 2https://courserooma.capella.edu/bbcswebdav/institution/NHS-FPX/NHS-FPX8070/200700/Scoring_Guides/a03_scoring_guide.html

Apply APA
formatting to in-text
citations and
references.

Does not apply
APA formatting to
in-text citations
and references.

Applies APA formatting to
in-text citations and
references incorrectly
and/or inconsistently,
detracting noticeably from
good scholarship.

Applies APA
formatting to in-
text citations and
references.

Exhibits strict and nearly
flawless adherence to APA
formatting of in-text
citations and references.

YOGA STRESS STUDY PAPER

Figure

1

–

Age

Histogram

Figure 2 – Education Histogram

Figure 3 – Pre-PSS Histogram

Figure 4 – Scatterplot (Age vs Pre-PSS)

Statistic

df

Sig.

20

20

.200*

20

Table 1 – Normality Tests

Age

PRE_PSS

20

20

Pearson Correlation

-.232

1

Sig. (2-tailed)

.325

N

20

20

Table 2 – Correlation

YOGA STRESS STUDY PAPER

Figure

1

–

Age

Histogram

Figure 2 – Education Histogram

Figure 3 – Pre-PSS Histogram

Figure 4 – Scatterplot (Age vs Pre-PSS)

Statistic

df

Sig.

20

20

.200*

20

Table 1 – Normality Tests

Age

PRE_PSS

20

20

Pearson Correlation

-.232

1

Sig. (2-tailed)

.325

N

20

20

Table 2 – Correlation