capella NHS-FPX-8070 all asessments (1-4) 2020

Assessment 1 Instructions: Creating and Interpreting a Demographic Table

 

Create a baseline demographic table and a 2-to-3 page narrative summary.

Statistics is the art and science of data collection and interpretation. It is an art because it requires a combination of creativity, an eye for what makes sense, and personal judgment about how to use the end result. It is a science because it requires a systematic way of organizing, transforming, analyzing, describing, and interpreting data. Even if you dislike math, you can still enjoy statistics because it is not just about doing calculations or performing mathematical gymnastics. Most of the mathematics that makes learners uncomfortable is hidden inside statistical technological tools that we can use with relative ease in health care to make important discoveries. So relax—we are going to let technology do most of the work!

In this assessment, we focus on the cornerstone of quantitative research: the variable. One of the many things that makes the health care field so fascinating (and challenging) is the variation we find from one human to the next. Age, gender, eye color, heart rate, ethnicity, emotional response, and food preferences are some of the differences we find in our communities around the globe. In the language of statistics, each of these characteristics is called a variable. Some characteristics, like gender, have little variation, while other characteristics, like age, can have a much larger amount of variation.

Throughout this course, you will see that variables have special names based on their functional roles in the experiment. For example, when a variable is associated with the intervention (such as treatment, where we design the experiment to allow for only two options: practicing yoga versus not), it is referred to as an independent variable.

And when a variable is associated with an outcome in the experiment (for example, stress—which we decide, arbitrarily, will have only three possible levels: high, medium, and low) that is used to measure the direct consequences of the experimental treatment, we refer to this as a dependent variable.

The sneaky thing about statistics is that depending on the circumstances, the independent variable is often referred to in other terms, such as the controlled, explanatory, or predictor variable. If you consider this briefly, the names make sense because you are controlling who gets which treatment, where the treatment really is the key factor in explaining (or predicting) any outcome. The dependent variable may also be referred to as response, outcome, output, or experimental variable. In this course, we will try to be fairly consistent, using the terms independent and dependent.

Overview

The baseline demographic table plays an important role in reporting study results. It summarizes key characteristics of participants numerically (such as age, gender, and ethnicity) at the beginning of a study, before any intervention takes place. Baseline demographic tables are often among the first tables found in the results section of capstone papers, dissertations, and peer-reviewed publications as well. For this assessment, you will create a baseline demographic table and narrative summary using the linked Resources.

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.
  • Write a summary narrative about statistical results.
• Competency 2: Apply appropriate statistical methods using common software tools in the collection and evaluation of health care data.
  • Perform descriptive statistics for selected variables in a data set.
  • Create a demographic table populated with descriptive data for specific treatment groups.
  • Use appropriate statistics for a given data measurement level.
• Competency 3: Interpret the results and practical significance of statistical health care data analyses.
  • Explain the clinical significance of a demographic table.
• 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

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: Baseline Demographic Table

1. Use the Yoga Stress (PSS) Study Data Set [XLSX] for this assessment.
2. Follow the steps described in this assessment to use SPSS for performing a descriptive statistical analysis for the following selected variables from the Yoga and Stress Study data set: Age, Gender, Race, Military Status, Pre-intervention Psychological Stress Score.
3. Using How to Create a Demographic Table [PPTX], create a demographic table and populate the table with the results of descriptive analysis.
   • Use the type of descriptive statistics most appropriate for the particular kind of data measurement level of each variable being reported.
   • I?nclude appropriate univariate statistics for the variables for each treatment group: Age, Gender, Ethnicity, Education, and current Military Status.
   • Use appropriate statistics for a given data measurement level.

Part 2: ?Interpretive Summary

1. Write a summary narrative about statistical results. 
2. Prepare a properly formatted demographic table that includes appropriate univariate statistics for the variables for each treatment group.
   • Explain the practical significance of a demographic table.
3. Use the unit readings, media resources, and collaborative insights from the discussions f?or general guidance in reporting a demographic table.

Additional Requirements

• Length: 2–3 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. See the APA Module for more information. 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.

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

 

 

Assessment 2 Instructions: Assessing the Quantitative Analytical Approaches in Health Care Literature

For this assessment, you will develop a 3-4 page critique of the quantitative design, methods, and results of a scholarly study.

The ability to use quantitative approaches to analyze health care data is a vital skill for today's doctoral prepared professional. You will be expected to have the skills to critically assess the deeper analytical qualities of an article and ultimately comment on its overall validity and practical relevance. This assessment will provide you with an opportunity to demonstrate and hone your ability to analyze and critique the quantitative methods of a research study using an example from the literature.

For the learner who has finished the data collection process for the doctoral project, analysis of that data offers an exciting (and sometimes challenging) opportunity of discovery. One of the most common statistical techniques for examining the relationship of two variables is correlation analysis. The specific kind of correlational technique depends on the combination of the measurement level (that is, categorical, ordinal, or interval or ratio) of the two data variables being examined. Correlation analysis can tell us the direction and strength of relationships between two variables.

Overview

Whether preparing a scholarly document for your doctoral program or simply trying to stay current in your professional field, you must continuously grow in your ability to read research. As an undergraduate, you probably just skimmed over an article's abstract and introduction, focusing most of your attention on the interpretation of the results at the conclusion. As a doctoral-level professional, your colleagues will expect you to have the skills to critically assess the deeper analytical qualities of an article and ultimately comment on its overall validity and practical relevance.

Using the readings, media, and various resources in this course, you have an opportunity to engage in critical thinking to assess the analytical results of a peer-reviewed quantitative study. This assessment parallels and complements the literature critique skill set you have developed previously in your program.

The following will help provide you with potential approaches and frameworks to completing the critique and assessment portions of this assessment:

• Shahnazi, H., Hosseintalaei, M., Esteki Ghashghaei, F., Charkazi, A., Yahyavi, Y., & Sharifirad, G. (2016). Effect of educational intervention on perceived susceptibility self-efficacy and DMFT of pregnant women. Iranian Red Crescent Medical Journal, 18(5), e24960.
• Coughlan, M., Cronin, P., & Ryan, F. (2007). Step-by-step guide to critiquing research. Part 1 – Quantitative research. British Journal of Nursing, 16(11), 658–663.
• The Value of a Research Critique to Translate Evidence Into Practice.

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.
  • Describe the study results for a quantitative study published in scholarly literature.
• Competency 3: Interpret the results and practical significance of statistical health care data analyses.
  • Interpret and critique the analytical testing approach used in a quantitative study described in scholarly literature.
• Competency 4: Assess the quality of quantitative research methods reported in peer-reviewed health care literature.
  • Cite and summarize a selected article.
  • Assess the overall methodological quality of an article using critique guidelines.
• 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

1. Read the article by Shahnazi et al. linked earlier in these instructions.
2. Cite and summarize the article.
   • Include study PICO, goals, intervention, and assessment data collected.
3. Describe, interpret, and critique the statistical testing approach.
   • Include preanalytic normal distribution and post-intervention analytical testing. The article by Shahnazi et al. may be a helpful reference.
4. Describe, interpret, and critique the study’s results from the analysis.
   • Address issues of significance; type I and II errors, confidence intervals, and effect sizes.
5. Assess the overall methodological quality of the article using the step-by-step critique guidelines in the article by Coughlan, Cronin, and Ryan, linked above.

Additional Requirements

• Length: Your paper will be 3–4 double-spaced pages of content plus title and reference pages.
• Font: Times New Roman, 12 points.
• APA Format: Your title and reference pages must follow APA format and style guidelines. See the APA Module for more information. 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.

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

 

Assessment 3 Instructions: Normal Data Distribution and Two-Variable Correlation Testing

For this three-part assessment you will create a histogram or bar graph for a data set, perform assumption and correlation tests, and interpret your graphic and test results in a 2-to-3 page paper.

In this unit we focus on whether two or more groups have important differences on a single variable of interest. For example, for the dependent variable stress score, we may want to know if there is a difference in stress between males and females, or maybe we would like to know if there is a difference in stress levels between people who drink chamomile tea and those who do not, or maybe we would like to determine if a group of expectant parents is less anxious (this is the dependent variable) about the birthing experience after a series of discussions with experienced parents. In each of these examples we have two groups (two groups being compared or the same group being compared before and after), and one dependent variable that is being compared in each group. In this unit you will begin exploring popular statistical techniques (and their assumptions) that are used to compare two or more groups.

The independent t-test, also called unpaired t-test, is typically used in health care to compare two groups of individuals that are entirely unrelated to each other (that is, independent), thus the one group cannot influence the other group. For example, we may wish to compare a drug treatment group to a control group (those not receiving drug treatment) for a specific clinical characteristic (dependent variable) that can be measured at the interval or ratio level (such as cholesterol, depression scale, or memory test).

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.

Additional Requirements

• Length: Your paper will be 2–3 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. See the APA Module for more information. 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.

Please review this assessment's scoring guide before submitting your assessment to ensure that you meet all criteria. You may also wish to review the performance-level descriptions for each criterion to see how your work will be assessed.

RESOURCES FOR ASSESSMT 3

Chi-square is a very flexible data analysis tool to answer relationship or association questions that cannot be answered by the other inferential tests you have learned during this course. Although there is actually a "family" of chi-square tests, we will focus on just two of the most commonly used: the chi-square test for independence (also called chi-square test for association) and the chi-square goodness of fit test. The chi-square test for association is used to explore an association or independence between two variables where data for both variables is measured at the category level (also referred to as nominal data). The chi-square test is flexible enough to allow us to test data measured at the ordinal level, but we will stick with categorical data to keep things simple.

Interestingly, whether one variable is dependent or independent is not important in this test. All that matters is that there are two categorical data-type variables. Remember too that when categorical data has only two levels of possible answers (such as yes or no), then we say this is dichotomous data. If you see these terms, they probably refer to categorical data.

Understanding what chi-square is, as well as how to perform the analysis will be helpful as you complete this assessment.

Chi-square Analysis

• Heavey, E. (2019). Statistics for nursing: A practical approach (3rd ed.). Burlington, MA: Jones & Bartlett. Available in the courseroom via the VitalSource Bookshelf link.
  • Chapter 8, "Chi-square."
• Geher, G., & Hall, S. (2014). Straightforward statistics: Understanding the tools of research. New York, NY: Oxford University Press.
  • Read Chapter 13, "Chi-square and Hypothesis Testing With Categorical Variable."
• Dr Nic's Maths and Stats. (n.d.). Analysing data in a two-way table (including chi-squared test) [Video]. Retrieved from https://www.youtube.com/watch?v=jhz0ubW0EWk
• Dr Nic's Maths and Stats. (n.d.). Understanding and calculating the chi-squared statistic in two-way tables [Video]. Retrieved from https://www.youtube.com/watch?v=qfxzG6FgVlM

 

 

Assessment 4 Instructions: Analyzing a Health Care Data Set

 

For this assessment, you will determine the relevant statistical tests to apply to the analysis of a data set, and then write a 3–4 page interpretation of the results of your analysis.

This assessment will ask you to select, apply, and interpret the results of a variety of statistical tests on a health care data set. This may include tests you have learned about or applied previously in the course, or the new nonparametic t-Test which is presented in the resources for this assessment. The challenge is using what you have learned to determine the best course of action to complete the interpretative tasks the assessment lays out for you. This attempts to mirror real-world situations where the data or statistical analysis could be approached in a variety of different ways. To decide which statistical test to use for the various dependent variables to be analyzed, one must first know more about the data type (measurement level) within those variables.

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 assessment is an opportunity to use your cumulative quantitative-analysis skills to address a broad set of real-world research questions.

Demonstration of Proficiency

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

• Competency 2: Apply appropriate statistical methods using common software tools in the collection and evaluation of health care data.
  • Perform the most appropriate parametric or nonparametric test to answer each question.
• Competency 3: Interpret the results and practical significance of statistical health care data analyses.
  • Assess the assumption of normal distribution prior to analysis.
  • Appropriately interpret the statistical output (such as estimate, p-value, confidence interval, and effect size) resulting from each statistical test.
  • Summarize the clinical implications, significance, and potential limitations of the study data and outcomes.
• Competency 4: Assess the quality of quantitative research methods reported in peer-reviewed health care literature.
  • Describe the practical significance of the results of statistical tests.
• 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

Complete the following for this two-part assessment.

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: Yoga and Stress Study Statistical Tests

1. Use the Yoga Stress (PSS) Study Data Set [XLSX] to 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?
2. Before performing any statistical tests, you must d?etermine which tests would be most appropriate for your data type.
   • Perform a preevaluation of the data for outliers (all variables) and normal distribution (only dependent variables) as you have done previously.
   • U?se How to Choose a Statistical Test [PPTX] 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.
3. Perform and interpret an appropriate series of statistical tests (including preanalytical 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.

1. • Is there any association between gender and race in this military study?
     • Perform an appropriate chi-square analysis.
2. 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?
3. Provide the SPSS ".sav" output file that shows your programming and results for this assessment.

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. See the APA Module for more information. 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 assessment.

Please review the assessment 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 FOR ASSESSMET 4

Most of your focus in this course thus far has been on health care situations where it is reasonable to assume that the data you are analyzing is normally distributed. What happens if you find yourself in a situation where you cannot make that assumption about the data? This may happen with interval or ratio data if your sample size is small (fewer than 30) or each group is skewed in opposite directions. You also may not be able to assume normal distribution if the data is measured at the ordinal level, which is less precise than interval or ratio data. If you encounter either of these scenarios, you may need to consider using nonparametric tests.

Fortunately, nonparametric tests are very flexible because they are distribution free! So why not use nonparametric tests all the time? The reason has to do with power. Like a powerful microscope that can magnify tiny differences in small objects, the parametric tests can identify significant differences in small increments of data. Like a toy microscope, nonparametric tests are great for examining bigger objects, but they do not work well on small objects.

The following resources will help add additional information, strategies, and tools to your repertoire to help in completing this assessment.

Nonparametric t-Test

• Heavey, E. (2019). Statistics for nursing: A practical approach (3rd ed.). Burlington, MA: Jones & Bartlett. Available in the courseroom via the VitalSource Bookshelf link.
  • Appendix B, "Working With Small Samples."
• Health Knowledge. (n.d.). Parametric and non-parametric tests for comparing two or more groups. Retrieved from https://www.healthknowledge.org.uk/public-health-textbook/research-methods/1b-statistical-methods/parametric-nonparametric-tests
• Hoskin, T. (n.d.). Parametric and nonparametric: Demistifying the terms [PDF]. Available from https://www.mayo.edu/documents/berd-5-6/doc-20274735
• Nahm, F. S. (2016). Nonparametric statistical tests for the continuous data: The basic concept and the practical use. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4754273/
• Pryjmachuk, S., & Richards, D. A. (2007). Look before you leap and don't put all your eggs in one basket. Journal of Research in Nursing, 12(1), 43–54.
• Laerd Statistics. (n.d.). Wilcoxon signed-rank test using SPSS statistics. Retrieved from https://statistics.laerd.com/spss-tutorials/wilcoxon-signed-rank-test-using-spss-statistics.php
• Laerd Statistics. (n.d.). Mann-Whitney u test using SPSS statistics. Retrieved from https://statistics.laerd.com/spss-tutorials/mann-whitney-u-test-using-spss-statistics.php