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statistics/cf_choose_a_statistical_test (1) (1).pptx

		Independent Variable [IV] (number of groups)
Dependent Variable [DV] (measurement level)		Two Groups		Three + Groups
		Independent (“unpaired”)	Dependent (“paired”)	Independent (“unpaired”)	Dependent (“paired”)
Categorical	Non-parametric Tests	Chi-square	McNemar’s	Chi-square	Cochran’s Q
Ordinal		Mann-Whitney U	Wilcoxon Signed ranks	Kruskal Wallis H	Friedman’s

Interval / Ratio (continuous)	Parametric Tests	Independent t-test	Dependent t-test	ANOVA	RM-ANOVA

“What is the effect of TREATMENT (IV) on our OUTCOME (DV) of interest?”

Example: TREATMENT independent groups (placebo versus drug), OUTCOME interval/ratio (blood pressure)

Example: TREATMENT dependent group (pre/post yoga therapy), OUTCOME ordinal (back pain levels)

Example: TREATMENT independent 3+ groups (yoga therapy, none, aerobics), OUTCOME categorical (pass/fail of driving test)

Correlations

Phi coefficient

Spearman’s rho

Pearson’s r

		Independent Variable (number of groups)
Dependent Variable (measurement level)		Two Groups		Three + Groups
		Independent (“unpaired”)	Dependent (“paired”)	Independent (“unpaired”)	Dependent (“paired”)
Categorical	Non-parametric Tests	Chi-square	McNemar’s	Chi-square	Cochran’s Q
Ordinal		Mann-Whitney U	Wilcoxon Signed ranks	Kruskal Wallis H	Friedman’s

Interval / Ratio (continuous)	Parametric Tests	Independent t-test	Dependent t-test	ANOVA	RM-ANOVA

STEP #1

Check what measurement level your DV is.

STEP #2

Choose the column related to the number Groups in your study.

STEP #3

Choose the column where intervention groups are either “paired” or “unpaired.”

STEP #4

Match your column with the row to find which test to run.

STEP #1

Look at your Dependent Variable or outcome.

The data that we are looking at here is from the instruments you used to measure the effect of your intervention. Maybe you chose to measure stress with a commonly used psychological questionnaire or maybe you measured cholesterol levels or test scores.

What is its measurement level?

Categorical (such as yes or no; dead or alive; pass or fail).

Ordinal (such as health status – poor, average, excellent).

Interval ratio (for instance blood pressure, cholesterol level, rates of infection, or workplace satisfaction scores on a scale of 0-100).

STEP #2

Next you will look for the column that corresponds to the number of groups you have for your Independent Variable (also called experimental or predictor variable).

Remember, the independent variable is the thing in your study that was controlled by you (such as a medical intervention, or training initiative, or implementation of a modified protocol) for the purpose of making a change on some outcome in the population you are studying.

So…how many groups were involved in this intervention?

For example, if you were testing the effect of an evidence-based training initiative on employee workplace satisfaction or happiness, you might be interested in comparing the training initiative in one group to no training in another group. Here, then, you would have two groups being studied.

But, maybe you wish to modify your intervention so that you also have training and massage compared to training alone, and both of these compared to no training or massage. Here you would have three groups where you would be measuring the DV (that is, workplace satisfaction).

STEP #3

Before we decide on the statistical test to use, we must examine another part of the Independent Variable columns that correspond to the number of groups you have in your study. We are now interested in a characteristic called Independent (unpaired) and Dependent (paired) groups.

To explain this, we will use the same evidence-based training initiative example from the previous slide. Let us assume you have two groups that you want to use in studying employee workplace satisfaction and happiness.

For the sake of this illustration, we will say that one group, which comes from a unit X at the hospital you selected, will be compared to unit Y that will not receive any training. Clearly, you now have two groups (one with training, and one without training) that you will be evaluating on your dependent variable called satisfaction and happiness. What is important here is to recognize that each group of individuals comes from entirely distinct units. While they might know each other, there is no particular way that they might influence each other on the satisfaction and happiness test. In fact, you have to be sure that you do not have a husband and wife split between these units because of their potential to influence each other. In this kind of study, you create two independent groups.

For a different study design, you may decide to use only one group, testing them for satisfaction and happiness before the training, and then right after the training. You would again have two groups being tested, but this time the groups are composed of the same people (that is, the same persons tested twice) and would therefore be considered “dependent.”

This step would appear to be the easiest. You have basically one test option that shows up at the intersection of the chosen IV column and DV rows. In reality, however, the selection of the appropriate inferential test is not quite so simple. But we will leave most of the exceptions to a statistician to figure out.

All dependent variables that are either ordinal or categorical in nature must have nonparametric testing. Interval or ratio data, on the other hand, may require a bit more evaluation since it can be tested with either parametric tests or nonparametric tests. Here is the most likely scenario you may encounter:

If you have determined that your data is at the interval or ratio type of measurement level (that is, continuous), you still must determine if it is distributed normally before you select the statistical test. In this course, you will only be responsible for testing to see if your interval or ratio data is normally distributed. Your readings and resources have more on this topic.

If your distribution test comes back saying that your outcome data is not normally distributed, then you will likely need to use a nonparametric test. To find the appropriate nonparametric equivalent tests for non-normal interval or ratio data, you simply move to the row just above the parametric test you selected. In other words, if you had selected an independent t-test for your intervalor ratio data, you would use the Mann-Whitney U test if the data turns out to fail the assumption of normal distribution.

STEP #4

statistics/Instructions.docx

Analyzing a Health Care Dataset

Overview

Public health researchers are often involved in collaborating in the design, development, and analysis of community initiatives of varying complexity. While this course alone will not provide sufficient training for you to act as a statistical consultant, it does offer a broad and practice-based analytic foundation that can position you to better understand and more fully contribute to real-world project teams. Building on the basic statistical concepts and analytical techniques of the previous units, this assignment is an opportunity to use your cumulative quantitative-analysis skills to address a broad set of real-world research questions.

Instructions

Complete the following for this two-part assignment:

Part 1: Yoga and Stress Study Statistical Tests

1. Using the dataset linked in Resources, determine the measurement level of data of the dependent or outcome variable (Psychological Stress Score) you are analyzing.

. Is the data categorical, ordinal, or interval or ratio?

· Before performing any statistical tests, you must determine which tests would be most appropriate for your data type.

. First, perform a pre-evaluation of the data for outliers (all variables) and normal distribution (only dependent variables) as you have done previously.

. Then, use How to Choose a Statistical Test (linked in Resources) as general guidance in helping you to decide which test to use.

. Use the readings, media, resources, and textbook as guides to perform an analysis of the selected variables.

· Perform and interpret an appropriate series of statistical tests (including pre-analytical testing for outliers and normal distribution of data) that answer the following research questions:

. How would you quantitatively describe the study population?

. Summarize the primary demographic data using descriptive statistics.

· Is there any association between gender and race in this military study?

. Perform an appropriate chi-square analysis.

· Perform preliminary assessment of the data, then compare pretest to post-test scores.

· In total population being studied, what was the effect of the yoga intervention on stress?

· Provide the Excel output file that shows your programming and results for this assignment.

Part 2: Interpretive Report

1. Summarize the clinical implications related to the statistical outcomes for each of the questions above.

2. Describe potential limitations of the study (Part 1, number 3).

Additional Requirements

· Length: Your paper will be 3–4 typed, double-spaced pages of content plus title and reference pages.

· Font: Times New Roman, 12 points.

· APA Format: Your title and reference pages must conform to APA format and style guidelines. The body of your paper does not need to conform to APA guidelines. Do make sure that it is clear, persuasive, organized, and well written, without grammatical, punctuation, or spelling errors. You also must cite your sources according to APA guidelines.

Refer to the helpful links in Resources as you prepare your assignment.

Please review the assignment scoring guide before completing your submission. The requirements outlined above correspond to the grading criteria in the scoring guide, so be sure to address each point. In addition, you may want to review the performance-level descriptions for each criterion to see how your work will be assessed.

Resources

· Analyzing a Health Care Dataset Scoring Guide .

· How to Choose a Statistical Test [PPTX] .

· Yoga Stress (PSS) Study Dataset [XLSX] .

· APA Module .

statistics/U8A1 - Analyzing a Health Care Dataset Scoring Guide (1) (1).pdf

3/1/2019 Analyzing a Health Care Dataset Scoring Guide

https://courserooma.capella.edu/bbcswebdav/institution/NHS/NHS8070/190100/Scoring_Guides/u08a1_scoring_guide.html 1/1

Analyzing a Health Care Dataset Scoring Guide

Due Date: End of Unit 8 Percentage of Course Grade: 22%.

CRITERIA NON-PERFORMANCE BASIC PROFICIENT DISTINGUISHED

Assess the assumption of normal distribution prior to analysis.

10%

Does not assess the assumption of normal distribution prior to analysis.

Assesses the assumption of normal distribution prior to analysis but the assumption test is incomplete or performed inaccurately.

Assesses the assumption of normal distribution prior to analysis.

Assesses the assumption of normal distribution prior to analysis and outlines the steps taken to perform the test, including determination of the measurement level of data for each variable.

Perform the most appropriate parametric or nonparametric test to answer each question.

35%

Does not perform the most appropriate parametric or nonparametric test to answer each research question.

Performs the most appropriate parametric or nonparametric test to answer each research question, but the test is performed incorrectly.

Performs the most appropriate parametric or nonparametric test to answer each research question.

Performs an appropriate alternative nonparametric test (Mann-Whitney U or Wilcoxon) and outlines the steps taken to perform the test.

Appropriately interpret the statistical output (such as estimate, p- value, confidence interval, and effect size) resulting from each statistical test.

25%

Does not appropriately interpret the statistical output (such as estimate, p- value, confidence interval, and effect size) resulting from each statistical test.

Appropriately interprets the statistical output (such as estimate, p-value, confidence interval, and effect size) resulting from each statistical test. but the interpretation is incomplete or inaccurate.

Appropriately interprets the statistical output (such as estimate, p-value, confidence interval, and effect size) resulting from each statistical test.

Appropriately the statistical output (such as estimate, p-value, confidence interval, and effect size) resulting from each statistical test. Justifies or explains the basis for the interpretation.

Describe the practical significance of the results of statistical tests.

10%

Does not describe the practical significance of the results of statistical tests.

Describes the practical significance of the results of statistical tests, but the description is inappropriate, incomplete, or otherwise flawed.

Describes the practical significance of the results of statistical tests.

Describes the practical significance of the statistical result for each research question. Provides at least one citation of research that supports the explanation.

Write clearly, accurately, and professionally.

15%

Does not write clearly, accurately, and professionally.

Writes clearly, accurately, and professionally, but with frequent errors or lapses.

Writes clearly, accurately, and professionally.

Writes clearly, accurately, and professionally, and the information is logically and appropriately organized.

Cite sources appropriately, using APA formatting.

Does not cite sources appropriately, using APA formatting.

Cites sources using APA formatting, but with some errors.

Cite sources appropriately, using APA formatting.

Cites sources appropriately, using APA formatting, and paraphrases accurately when appropriate.

statistics/Yoga_Stress (PSS) study dataset (1) (1).xlsx

HDAP 8070-02 DATA

Patient ID	AGE	GENDER	RACE	EDUCATION	MIL_STATUS	PRE_PSS	POST_PSS
3001	23	Male	African American	Graduate education or above	active duty	25	20
3002	26	Male	Asian	College graduate	active duty	22	15
3003	33	Male	Caucasian	Some college	active duty	17	16
3004	35	Male	Hispanic	Some college	active duty	32	25
3005	48	Male	African American	Graduate education or above	active duty	22	14
3006	51	Female	African American	College graduate	active duty	18	16
3007	22	Female	African American	Some college	active duty	14	12
3008	18	Female	Asian	Some college	active duty	22	16
3009	44	Female	Caucasian	College graduate	active duty	23	20
3010	40	Female	Native American	Some college	active duty	33	36
4001	30	Male	Native American	College graduate	US Civilian	22	21
4002	55	Male	Two or more races	Less than HS	US Civilian	25	15
4003	57	Female	African American	College graduate	US Civilian	13	10
4004	47	Female	African American	Less than HS	US Civilian	12	12
4005	39	Male	Asian	HS graduate	US Civilian	17	12
4006	29	Male	Caucasian	HS graduate	US Civilian	10	10
4007	33	Male	Caucasian	Graduate education or above	US Civilian	34	22
4008	44	Female	Hispanic	College graduate	US Civilian	18	12
4009	55	Female	Hispanic	College graduate	US Civilian	12	10
4010	60	Female	Native American	College graduate	US Civilian	16	9