PSYC 6800: Applied Psychology Research Methods

Week 1: Data Collection and Management

Learning Objectives

Required Readings

Johnson, R. B., & Christensen, L. B. (2020). Educational research: Quantitative, qualitative, and mixed approaches (7th ed.). Thousand Oaks, CA: Sage.

• Chapter 7, “Standardized Measurement and Assessment” (pp. 150-155)
• Chapter 8, “Methods of Data Collection in Quantitative, Qualitative, and Mixed Research” (pp. 179-206)

Salkind, N. (2016). Excel Statistics: A Quick Guide (3rd ed.). SAGE Publications, Inc.

ORDER A PLAGIARISM-FREE PAPER NOW

• Preface, “How to Use This Book” (pp. viii-xi)
• Part I, “Using Excel Functions” (pp.1-7)
• Part II, “Using the Analysis ToolPak” (pp. 89-91)

Tubaro, P. (2015, October 18). Research ethics in secondary data: what issues? Data Big and Small. https://databigandsmall.com/2015/10/18/research-ethics-in-secondary-data-what-issues/

Tubaro, P. (2016, May 15). Ethical issues in research with online data. Data Big and Small. https://databigandsmall.com/2016/05/15/ethical-issues-in-research-with-online-data/

Document: Final Project Overview (PDF)

This document will be available in every week of the course for easy access PSYC 6800: Applied Psychology Research Methods.

Document: Final Project Worksheet (Word document)

Study Notes

Document: The Purpose of Research (e.g., basic research, applied research, evaluation research, research and development, and action research) (PowerPoint presentation)

Document: Important Terms in Quantitative Analysis (e.g., independent variable, dependent variable, dichotomous variables, categorical variables, continuous variables) (PowerPoint presentation)

Required Media

Walden University (Producer). (2019). Common fears and anxiety about research [Video file]. Baltimore, MD: Author.

Note: The approximate length of this media piece is 25 minutes

Accessible player

Final Project Datasets

Diebold Dataset and Codebook

Document: Diebold Dataset (Excel worksheet)

Document: Diebold Dataset Codebook (Word document)

School Survey on Crime and Safety Dataset and Codebooks

Document: School Survey on Crime and Safety Dataset (Excel worksheet)

Document: School Survey on Crime and Safety Codebook (Word Document)

Document: School Survey on Crime and Safety Questionnaire Codebook (PDF)

Fictional Working Sample Dataset and Codebook

Document: Fictional Working Sample Dataset (Excel worksheet)

Document: Fictional Working Sample Codebook (Word Document)

Optional Resources

In the event you want to work ahead on your Final Project, please begin reviewing the following resources:

Johnson, R. B., & Christensen, L. B. (2016). Educational research: Quantitative, qualitative, and mixed approaches (6th ed.). Thousand Oaks, CA: Sage.

• Chapter 7, “Standardized Measurement and Assessment” (pp. 162–188)
• Chapter 11, “Validity of Research Results in Quantitative, Qualitative, and Mixed Research”

Note: In Chapter 11, pay special attention to the section on quantitative research.

Weems, G. H., & Onwuegbuzie, A. J. (2001). The impact of midpoint responses and reverse coding on survey data. Measurement and Evaluation in Counseling and Development, 34, 166-176.

Weems, G. H., Onwuegbuzie, A. J., Eggers, S. J., & Schreiber, J. B. (2003). Characteristics of respondents who respond differently to positively- and negatively-worded items on rating scales. Assessment and Evaluation in Higher Education, 28, 587-607. doi:10.1080/0260293032000130234

Ethics

Morrow, V., Boddy, J., & Lamb, R. (2014). Novella working paper: Narrative research in action. Retrieved from https://www.younglives.org.uk/sites/www.younglives.org.uk/files/NOVELLA_NCRM_ethics_of_secondary_analysis.pdf

Msalganik. (2014, July 29). The Belmont Report: Three principles for ethical research [Blog]. Retrieved from https://msalganik.wordpress.com/2014/07/29/the-belmont-report-three-principles-for-ethical-research/ PSYC 6800: Applied Psychology Research Methods

Discussion: Discovering Your Fears and Anxiety about Research

By Day 4

By Day 6

Submission and Grading Information

Grading Criteria

To access your rubric:

Week 1 Discussion Rubric

Post by Day 4 and Respond by Day 6

To participate in this Discussion:

Week 1 Discussion

Assignment: Preparing for Data Analysis

By Day 7 (required but not assessed)

Submission Information

Submit Your Assignment by Day 7

To submit your Assignment:

Week 1 Assignment

Final Project Overview: Connecting Real-World Data through Quantitative Analysis

Next Week

Week 2: Developing Quantitative Research Questions

Required Readings

Johnson, R. B., & Christensen, L. B. (2020). Educational research: Quantitative, qualitative, and mixed approaches (7th ed.). Thousand Oaks, CA: Sage.

• Chapter 4, “How to Critically Review the Literature and Develop Good Research Questions” (pp. 77–105)

Note: Focus on pages 474–482 of the article “Linking Research Questions to Mixed Methods Data Analysis Procedures.”

Walden University. (n.d.). Social change. Retrieved from https://www.waldenu.edu/about/social-change

Document: Final Project Overview (PDF)

Document: Research Question Types Quiz Worksheet (Word document)

Note: You will refer to your completed Research Question Types Quiz Worksheet again in Week 4, in order to complete that week’s Discussion 1.

Document: Working With Datasets Job Aid (PDF)

Note: This document provides step-by-step instructions about how to use Excel to access datasets and conduct statistical analyses, as well as how to post visual displays of those analyses PSYC 6800: Applied Psychology Research Methods.

Study Notes

Document: Inductive Reasoning and Deductive Reasoning (PowerPoint presentation)

Document: Quantitative Research Designs (PowerPoint presentation)

Document: Quantitative Research Questions (PowerPoint presentation)

Required Media

Walden University (Producer). (2019). Research question types quiz [Interactive file]. Baltimore, MD: Author.

Note: Use this interactive quiz for practice identifying types of research questions (i.e., descriptive, relational, or comparative)

Optional Resources

Walden University Library (n.d.-a). Sage research methods online: Introduction to Sage research methods. Retrieved from https://academicguides.waldenu.edu/library/srmo

Walden University Library (n.d.-b). Statistics & data: Statistics & data by topic. Retrieved from https://academicguides.waldenu.edu/library/statistics/statisticsbytopic

Document: Descriptive Statistics (PowerPoint presentation)

As you consider your statistical software, this resource is common to data analysts and accessible in the Week 7 Learning Resources.

Discussion: Research Question(s)

By Day 4

By Day 4

Submission and Grading Information

Grading Criteria

To access your rubric:

Week 2 Discussion Rubric

Post by Day 4 and Respond by Day 6

To participate in this Discussion:

Week 2 Discussion

Assignment: Getting Ready for Data Collection

Final Project: Connecting Real-World Data through Quantitative Analysis

Looking Ahead

Week in Review

Next Week

To go to the next week:

Week 3: Cleaning and Managing Quantitative Data

The artist Michelangelo did not simply haul a slab of marble into his workshop and call it David. He painstakingly chiseled at the granite until the statue was “revealed” to him through his effort. Consider your own creation: your research. You should now have a large amount of data relating to your research question(s). However, you cannot simply sit back and declare correlations or conclusions just yet. The next step of your research project will be to chisel away at the data set for it to “reveal” new information to you PSYC 6800: Applied Psychology Research Methods.

For this week, you will focus on cleaning and managing data and why this is crucial to your research. You will explore the steps to clean your dataset and consider why these steps are necessary. This week, you will also submit Parts 1 and 2 of your Final Project Worksheet, and you will receive feedback from your Instructor in Week 4.

Reference
Accademia.org. (n.d.). Michelangelo’s David. Retrieved September 2, 2019, from http://www.accademia.org/explore-museum/artworks/michelangelos-david/

Learning Objectives

Students will:

• Analyze challenges and solutions to the data cleaning process
• Apply processes to clean and manage quantitative data
• Create research questions
• Describe a research design appropriate for each research question
• Describe dataset and variables related to field/discipline of study

---

Learning Resources

Required Readings

Document: Cleaning and Management Example Dataset (Excel worksheet)

Note: Use this dataset when reviewing the Cleaning Datasets Job Aid.

Document: Cleaning and Management Example Dataset Codebook (Word document)

Document: Cleaning and Management Practice Dataset (Excel worksheet)

Note: Use this dataset to answer Cleaning and Management Quiz (interactive quiz) questions used to practice identifying missing data or data errors in a dataset.

Document: Cleaning Datasets Job Aid (PDF)

Document: Final Project Overview (PDF)

Document: Final Project Worksheet (Word document)

Document: Quantitative Legitimation Model (Word document)

Note: The Quantitative Legitimation Model is a companion document to the Internal Validity and External Validity PowerPoint located in this week’s Learning Resources and should be referenced while viewing the PowerPoint.

 

Credit Line: Onwuegbuzie, A. (2003). Figure 1. Major dimensions of threats to internal and external validity at the three major stages of the research process [Graphic]. Retrieved from https://files.eric.ed.gov/fulltext/ED448205.pdf and Onwuebuzie, A. (2012). Table 1. Typology of mixed methods legitimation types [Graphic].

Study Notes

Document: Reliability (PowerPoint presentation)

Document: Internal Validity and External Validity (PowerPoint presentation)

Required Media

Walden University (Producer). (2019). Cleaning and management quiz [Interactive file]. Baltimore, MD: Author.

Note: Use this interactive quiz for practice identifying missing data or data errors in a dataset.

Optional Resources

American Psychological Association. (2019). Links to datasets and repositories. Retrieved from https://www.apa.org/research/responsible/data-links.aspx

Walden University Library. (2018). Statistics & data: Statistics & data by topic. Retrieved from https://academicguides.waldenu.edu/library/statistics/statisticsbytopic

Microsoft Office (n.d.) Check data entry for invalid entries. https://www.officetooltips.com/excel_2016/tips/check_data_entry_for_invalid_entries.html

Microsoft Office (n.d.) Use conditional formatting to highlight information. https://support.office.com/en-us/article/use-conditional-formatting-to-highlight-information-fed60dfa-1d3f-4e13-9ecb-f1951ff89d7f

---

Discussion: Cleaning Data

Every journey begins with a single step. Before you can become a master researcher providing thoughtful information analysis and meaningful conclusions, you must prepare your workspace and clean your tools. Without the crucial steps of organizing the data, preparing your software for analysis, and sharpening your quantitative skills, your research question will not be answered PSYC 6800: Applied Psychology Research Methods.

For this Discussion, you will learn how to prepare the information you have collected, removing corrupted or missing data in order to begin studying it. You will use the dataset provided to practice identifying challenges that arise during the cleaning process before presenting your findings to the class.

To Prepare:

• Review and use the Cleaning and Management Example dataset and Cleaning and Management Example Dataset Codebook as you review the Cleaning Data Sets Job Aid found in the Learning Resources.
• Access the Cleaning and Management Practice dataset in the Learning Resources to complete the Cleaning and Management Quiz (Note: Allow 30 minutes to complete).
• Complete the Cleaning and Management Quiz by Day 3. Note: This quiz is not assessed, but it must be completed prior to your posting to this Discussion.
• Reflect on the cleaning process when completing the Cleaning and Management Quiz.

By Day 4

Post an explanation of the challenges you might encounter in your experience through the cleaning process and how you might remedy those challenges. Then, explain why it is important to clean and manage data, and why this would be important to generalizability, confidence in accuracy of the results, or psychometric properties.

By Day 6

Respond to one of your colleagues’ posts and, based on the challenges encountered during the cleaning process presented by that colleague, describe whether these challenges would be present in your own field/discipline. Be sure to justify your reasoning.

Be sure to support your postings and responses with specific references to the Learning Resources. Use proper APA format and citations.

Return to this Discussion in a few days to read the responses to your initial posting. Note what you have learned and/or any insights that you have gained as a result of your colleagues’ comments.

Submission and Grading Information

Grading Criteria

To access your rubric:

Week 3 Discussion Rubric

Post by Day 4 and Respond by Day 6

To participate in this Discussion:

Week 3 Discussion

---

Final Project: Connecting the Real-World Data Through Quantitative Analysis

Now that you are familiar with the benefits and challenges of cleaning your datasets, it is time to put what you have learned into practice with the next step of your Final Project.

This week, for Part 2: Research Questions and Design of your Final Project Worksheet, you will create three research questions and identify a research design for each PSYC 6800: Applied Psychology Research Methods.

To Prepare:

• Using the dataset you chose in Part 1, develop one descriptive research question, one comparative research question, and one relational research question.
• Identify the research design for each of your three research questions.
• Explore the dataset’s characteristics (e.g., population characteristics, sample size). As you become familiar with your dataset, be mindful of whether the dataset requires cleaning. If necessary, use the techniques you learned this week to clean your Final Project Dataset.

By Day 7

Final Project Assignment: Final Project Worksheet, Parts 1 & 2

Submit Part 1 and Part 2 of your Final Project Worksheet by Day 7 this week. You will receive feedback on your worksheet and incorporating your Instructor’s feedback will be critical to you as you move forward with your Final Project. 

Note:Failure to submit Part 2 for feedback on time might have serious implications, because without feedback, you will not know whether your research design is viable. This could seriously jeopardize the successful completion of your Final Project.

Submission and Grading Information

To submit your completed Project for review and grading, do the following:

• Please save your Assignment using the naming convention “WK3Proj+last name+first initial.(extension)” as the name.
• Next, from the Attach File area, click on the Browse My Computer button. Find the document you saved as “WK3Proj+last name+first initial.(extension)” and click Open.
• Click on the Submit button to complete your submission.

Submit Your Assignment by Day 7

To submit your Project:

Week 3 Project

---

Week in Review

This week, you examined the challenges and solutions to the data cleaning and management process. You also submitted Parts 1 and 2 of your Final Project Worksheet for feedback from your Instructor.

Next week, you will identify appropriate research designs for specific research questions and examine sample size in order to make generalizations about a given population. You will finish the week by taking a Module Quiz where you demonstrate your understanding of the topics in weeks 1–4 of Module 1: Data Collection and Management PSYC 6800: Applied Psychology Research Methods.

Next Week

To go to the next week:

Week 4

Week 4: Research Design

In research, if you struggle with finding the answers you are looking for, you might not be asking the right question(s). In research, asking the right question(s) is as important as the answer(s). You want the evidence to support conclusions, rather than the other way around. The research question(s) drives the research design and analysis. Sound research design can prevent mistakes being made during data collection or making assumptions from improper sampling. Modern political discourse is saturated with claims and conclusions made from anecdotal evidence derived from improper or uncontextualized datasets. How might you keep this from happening in your own research?

This week, you will focus on research questions and the concept of sample size. You will identify an appropriate research design for each research question. You will also conduct a sample size analysis. Lastly, you will take a Module Quiz about the material presented in Weeks 1–4 of Module 1 of this course.

Learning Objectives

Students will:

• Evaluate research design appropriate for research question(s)
• Analyze sample size
• Analyze consequences of small sample size
• Use power analysis/tables to determine sample size
• Demonstrate an understanding of data collection and management for problem solving PSYC 6800: Applied Psychology Research Methods.

---

Learning Resources

Required Readings

Johnson, R. B., & Christensen, L. B. (2020). Educational research: Quantitative, qualitative, and mixed approaches (7th ed.). Thousand Oaks, CA: Sage.

• Chapter 10, “Sampling in Quantitative, Qualitative, and Mixed Research” (pp. 239–266)
  • In particular review “Determining the Sample Size When Random Sampling Is Used” (pp. 255-257)

Document: Final Project Overview (PDF)

Study Notes

Document: Quantitative Research Designs (PowerPoint presentation)

(Previously read in Week 2.)

Optional Resources

Krejecie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607-610. doi:10.1177/001316447003000308

• Power Analysis or Power Table (p. 608)

Credit Line: Determining Sample Size for Research Activities by Krejcie, R. V., & Morgan, D. W., in Educational and Psychological Measurement, Vol. 30/Issue 3. Copyright 1970 by Sage Publications Inc. Reprinted by permission of Sage Publications Inc. via the Copyright Clearance Center.

Onwuegbuzie, A. J., & Collins, K. M. T. (2007). A typology of mixed methods sampling designs in social science research. The Qualitative Report, 12(2).

---

Discussion 1: Selecting the Right Research Design

Great chess players know their next three moves in advance. A chess master can even know how the match might end in the first few moves. The master does so by knowing not only how the pieces can move, but also how they will move based on external factors. The player creates a plan based on all information available and how that information will factor into the game. Similarly, researchers must plan in advance to foresee obstacles that might trouble their research design.

For this Discussion, you will reflect on the information available to you from previous work in this course to create a research design for answering the three questions you have generated. You will further practice creating research designs by giving colleagues feedback on their own.

To Prepare:

• Reflect on the quantitative research questions on the Research Question Types Quiz Worksheet you completed for the Week 2 Discussion. You learned and recorded on your worksheet which research questions are descriptive, relational, or comparative.
• Select a descriptive research question, a relational research question, and a comparative research question.
• Consider an appropriate research design for each of your three selected questions.

By Day 3

Post and describe an appropriate research design for each of the three different research questions you identified from your Research Question Types Quiz Worksheet. Be sure to include your research questions as part of your discussion. Explain your reasoning for selecting the research design you chose for each of your research questions.

By Day 5

Respond to one of your colleagues’ posts and explain why you agree or disagree with your colleague’s selected research design. Suggest another design if you feel it may be appropriate and explain why.

Be sure to support your postings and responses with specific references to the Learning Resources. Use proper APA format and citations.

Return to this Discussion in a few days to read the responses to your initial posting. Note what you have learned and/or any insights that you have gained as a result of your colleagues’ comments.

Submission and Grading Information

Grading Criteria

To access your rubric:

Week 4 Discussion 1 Rubric

Post by Day 3 and Respond by Day 5

To participate in this Discussion:

Week 4 Discussion 1

---

Discussion 2: Sample Size

Consider this scenario:

You are a researcher investigating risk factors related to pancreatic cancer. In order to promote positive social change, it is important to collect a large enough sample size to justify making generalizations to their population out of people who have pancreatic cancer PSYC 6800: Applied Psychology Research Methods.

In this Discussion, reflect on the number of variables you plan to use and consider the impact that sample size has on generalizability.

To prepare:

• As you consider the scenario, be mindful of the number of variables you, as the researcher, intend to use and the type of research design/analysis to be conducted.
• Also, consider the importance of sample size to generalizability.
• Search the internet and/or the Walden Library for information related to the risk factors associated with pancreatic cancer to complete this Discussion
• Review the Learning Resources, specifically the Power Table in the Johnson and Christensen course text.

By Day 4 (Post First)

Post your response based on the literature from your search: What should be the minimum sample size for this study related to pancreatic cancer in order to justify making generalizations from the sample to the population? What information would you need to know in order to use the Power Table to determine an appropriate sample size?

Further, explain the possible consequences of having too small of a sample size for this study.

By Day 6

Respond to at least one of your colleagues’ posts and discuss whether you agree or disagree with their sample size estimation. Explain your position.

Be sure to support your postings and responses with specific references to the Learning Resources. Use proper APA format and citations.

Note: For this Discussion, you are required to complete your initial post before you will be able to view and respond to your colleagues’ postings. Begin by clicking on the “Post to Discussion Question” link and then select “Create Thread” to complete your initial post. Remember, once you click on Submit, you cannot delete or edit your own posts, and you cannot post anonymously. Please check your post carefully before clicking on Submit!

Return to this Discussion in a few days to read the responses to your initial posting. Note what you have learned and/or any insights that you have gained as a result of your colleagues’ comments.

Submission and Grading Information

Grading Criteria

To access your rubric:

Week 4 Discussion 2 Rubric

Post by Day 4 and Respond by Day 6

To participate in this Discussion:

Week 4 Discussion 2

---

Module 1 Quiz

Modules 1 and 2 of this course provide fundamental information about quantitative data and analysis. At the end of each of these Modules, you will take a quiz to demonstrate your understanding of the topics covered.

This Module’s quiz assesses your knowledge of the assigned course readings and material on Data Collection and Management.

To Prepare:

• Review the Module 1 Learning Resources found in Weeks 1-4 and consider how these resources will help and guide you through your understanding of data collection and management.

Module 1 Quiz instructions:

• This quiz consists of 10 multiple-choice questions.
• Each question is worth 1 point.
• Questions are presented in a random order.
• This quiz allows you to go back to check or change your answers at any point during your allotted time.
• This quiz is open book, so you may use your course readings and other resources to help you.
• You will have unlimited time to finish this quiz by Day 7 of the week.
• You must pass the quiz with a score of at least 90% to demonstrate competency.
• Once the quiz has been submitted, the correct answer will be indicated in instances where you have given an incorrect answer.
• If you need to retake the quiz, a new one will be generated.

By Day 7

Complete and submit your Module 1 Quiz by Day 7.

Submission Information

Submit Your Quiz by Day 7

To submit your Quiz:

Module 1 Quiz

---

Looking Ahead

You will submit Parts 1, 2, and 3 of your Final Project Worksheet in Week 5. Please make sure to incorporate the feedback your Instructor provided you in Week 4 before your final submission in Week 5. Plan your time accordingly PSYC 6800: Applied Psychology Research Methods.

Week in Review

This week, you identified appropriate research designs for specific research questions. You also examined sample size considerations in order to make generalizations about a given population. You finalized this week by completing the Module 1 Quiz where you demonstrated your understanding of the topics in Weeks 1–4 of Module 1: Data Collection and Management.

Next week, you will explore methods for finding, using, and validating assumptions based on the information you have collected in your research. You will also develop a histogram using Excel. As a final step of Module 1, you will also complete and submit Parts 1, 2, and 3 of your Final Project Worksheet.

Next Week

To go to the next week:

Week 5

Week 5: Assumptions

The fictional private detective Sherlock Holmes famously made grand conclusions based on seemingly very limited information. He would combine seemingly trivial observations with contextual assumptions to make (usually) accurate conclusions about the object or person. However, the problem with Holmes’s method of inductive reasoning lays in the assumptions. For example, a tan line on the ring finger does not distinguish between a cheater, a recent divorcee, and a widow. Assumptions, while useful and often accurate, should periodically be examined for validity prior to making conclusions.

For this final week of Module 1, you will explore how to find, use, and validate assumptions based on the information you have already collected. You will create and examine a histogram using Excel. You will also complete and submit Parts 1, 2, and 3 of your Final Project Worksheet PSYC 6800: Applied Psychology Research Methods.

Learning Objectives

Students will:

• Evaluate histograms related to statistical model assumptions (normality, independence)
• Analyze how to check for model assumptions, such as normality, homogeneity, or variance
• Evaluate important measurement assumptions such as score reliability and score validity (trustworthiness and generalizability)
• Create research questions related to field/discipline of study
• Describe a research design appropriate for each research question
• Describe dataset and variables related to field/discipline of study
• Analyze collection and management of data for each research question
• Analyze how to clean collected data for each research question
  

  ORDER NOW

---

Learning Resources

Required Readings

Johnson, R. B., & Christensen, L. B. (2020). Educational research: Quantitative, qualitative, and mixed approaches (7th ed.). Thousand Oaks, CA: Sage.

• Chapter 2, “Quantitative, Qualitative, and Mixed Research” (pp. 37–45)
• Chapter 7, “Standardized Measurement and Assessment” (pp. 155–168)
• Chapter 10, “Sampling in Quantitative, Qualitative, and Mixed Research”
  • Sample Size Table (pp. 256–257) (review)

Salkind, N. (2016). Excel Statistics: A Quick Guide (3rd ed.). SAGE Publications, Inc.

• Excel Quickguide 55, “The Histogram Tool” (pp. 123–124)

Onwuegbuzie, A. J. (2003). Expanding the framework of internal and external validity in quantitative research. Research in the Schools, 10(1), 71–90.

Onwuegbuzie, A. J., & Daniel, L. G. (2002a). A framework for reporting and interpreting internal consistency reliability estimates. Measurement and Evaluation in Counseling and Development, 35(2), 89–103.

Document: Final Project Overview (PDF)

Document: Study Habits Dataset (Excel worksheet)

Document: Study Habits Codebook (Word document)

Document: Working With Datasets Job Aid (PDF)

Note: This document provides step-by-step instructions about how to access and use Excel to conduct statistical analyses, as well as how to post visual displays of those analyses.

Study Notes

Document: Three Standard Statistical Assumptions (i.e., normality; homogeneity of variance; independence) (PowerPoint presentation)

Required Media

Walden University (Producer). (2019). How to develop a histogram from the dataset [Video file]. Baltimore, MD: Author.

Note: The approximate length of this media piece is 19 minutes.

Accessible player 
–Downloads–
Download Video w/CC
Download Audio
Download Transcript

Excel Practice Datasets

Quick Guide Data Sets

SAGE Publications (2020a). Q55.Histogram.xlsx. https://study.sagepub.com/node/22833/student-resources/quick-guide-data-sets

Note: In order to accessthis data set, select the link that reads, “Click here to download all files” and select the files from the extracted file folder.

Check Your Understanding Data Sets

SAGE Publications (2020b). QS55a.xlsx. https://study.sagepub.com/node/22833/student-resources/check-your-understanding-data-sets

SAGE Publications (2020b). QS55b.xlsx. https://study.sagepub.com/node/22833/student-resources/check-your-understanding-data-sets

Note: In order to accessthese data sets, select the link that reads, “Click here to download all files” and select the two files from the extracted file folder.

---

Discussion: Histogram

Different parts of the world have experienced catastrophic droughts over the past 10 years. Imagine you are studying the amount of rainfall received over a set period of time in order to help an area find solutions during times of water shortage. You might collect data on rainfall patterns on a monthly basis for a year. Then, chart your data to provide a graphical image of monthly rainfall rates. The resulting histogram indicates a range of rainfall.

This week, you will generate your own histograms using data from the Study Habits dataset provided in the Learning Resources.

To Prepare:

• Review the Learning Resources Salkind course text and the document Working With Datasets Job Aid for information about how to complete the tasks identified in the To Prepare and Post activities PSYC 6800: Applied Psychology Research Methods.
• Practice creating histograms using the Quick Guide Data Set “Q55. HISTOGRAM.xlsx” and the Check Your Understanding Data Sets “QS55a” and “QS55b”.
• Choose a continuous variable from the Study Habits dataset and use Excel  to create a histogram of this variable. Note: The dataset contains missing data. For this Discussion, do not clean the missing data.
• Review the Working With Datasets Job Aid for instructions on “How to Post a Visual Display to the Discussion Board”, as you cannot copy and paste the histogram directly into the Discussion Board.

By Day 4 (Post First)

Post your histogram and interpret it in terms of normality. Explain your reasoning. Note: Refer to the Learning Resources for assistance.

By Day 6

Respond to one of your colleagues’ posts who analyzed a variable different from yours. Evaluate whether that colleague’s interpretation of the histogram differed from your own interpretation of your colleague’s histogram.

Be sure to support your postings and responses with specific references to the Learning Resources. Use proper APA format and citations.

Note: For this Discussion, you are required to complete your initial post before you will be able to view and respond to your colleagues’ postings. Begin by clicking on the “Post to Discussion Question” link and then select “Create Thread” to complete your initial post. Remember, once you click on Submit, you cannot delete or edit your own posts, and you cannot post anonymously. Please check your post carefully before clicking on Submit!

Return to this Discussion in a few days to read the responses to your initial posting. Note what you have learned and/or any insights that you have gained as a result of your colleagues’ comments.

Submission and Grading Information

Grading Criteria

To access your rubric:

Week 5 Discussion Rubric

Post by Day 4 and Respond by Day 6

To participate in this Discussion:

Week 5 Discussion

---

Final Project: Connecting Real-World Data through Quantitative Analysis

The time has come to submit the information you have gathered for Parts 1–3 of your Final Project Worksheet. Over the last 5 weeks, you have examined the challenges of collecting and cleaning datasets, explored the power and pitfalls of assumptions in data analysis, developed quantitative research questions, and evaluated the proper research design to answer those questions.

Finalize Parts 1, 2, and 3 of your Final Project Worksheet. Incorporate any necessary changes, based on Instructor feedback.

By Day 7

Final Project Assignment: Final Project Worksheet, Parts 1, 2, and 3

Submit Part 1: Selecting a Dataset, Part 2: Research Questions and Design, and Part 3: Collecting and Managing Data of your Final Project Worksheet by Day 7 of this week. 

Submission and Grading Information

To submit your completed Project for review and grading, do the following:

• Please save your Assignment using the naming convention “WK5Proj+last name+first initial.(extension)” as the name.
• Click the Week 5 Project Rubric to review the Grading Criteria for the Assignment.
• Click the Week 5 Project link. You will also be able to “View Rubric” for grading criteria from this area.
• Next, from the Attach File area, click on the Browse My Computer button. Find the document you saved as “WK5Proj+last name+first initial.(extension)” and click Open.
• If applicable: From the Plagiarism Tools area, click the checkbox for I agree to submit my paper(s) to the Global Reference Database.
• Click on the Submit button to complete your submission.

Grading Criteria

To access your rubric:

Week 5 Project Rubric

Check Your Assignment Draft for Authenticity

To check your Project draft for authenticity:

Submit your Week 5 Project draft and review the originality report PSYC 6800: Applied Psychology Research Methods.

Submit Your Assignment by Day 7

To submit your Project:

Week 5 Project

---

Week in Review

This week, you examined the concept of finding, using, and validating assumptions based on the data you have collected in your research. You also created a histogram using Excel to examine and interpret the histogram. Additionally, you completed and submitted Parts 1, 2, and 3 of your Final Project Worksheet.

Next week, as you begin Module 2, you will reflect on your experience with Module 1 and consider how the material you have reviewed might be applied to solving real-world issues. In Week 6, you will begin Parts 4 and 5 of your Final Project Worksheet.

Next Module

To go to the next module:

Module 2

Module 2: Data Analysis

Do you ever find yourself texting friends and watching television while you’re studying? More and more people find themselves multitasking today in the increasingly busy world of work, school, families, etc. Perhaps not surprising is the fact that this approach is limiting your ability to get tasks done efficiently. Rogers and Monsell (1995) did a study and determined that when the brain tries to complete two or more activities simultaneously, it is actually very quickly stopping one action and moving to the others. The more activities, the harder the brain must work at switching back and forth. Rubinstein, Meyer, and Evans (2001) studied young adults completing multiple tasks and determined that the time the brain takes to switch from one task to another gets longer the more tasks that are added. As a result, trying to complete multiple tasks simultaneously actually takes longer than completing one at a time.

In both referenced studies, it was determined that switching back and forth between activities caused more errors, in addition to slowing down the cognitive processes. These important findings came from collecting and analyzing data. The researchers relied on the participation of mostly young adults and conducted multiple experiments to collect their data on how each participant responded. The researchers then analyzed the data and their conclusions changed the previously accepted belief about multitasking.

Module 2: Data Analysisincludes Weeks 6–10 of the course in which you will analyze the data you have collected in order to spot variables and trends, as well as overlay contextual data to identify correlations and draw further conclusions. You will then examine the inferences themselves for conclusions. You will be working on Parts 4 and 5 of your Final Project Worksheet for the entirety of Module 2.

References
Rogers, R. D., & Monsell, S. (1995). Costs of a predictable switch between simple cognitive tasks. Journal of Experimental Psychology: General, 124(2), 207–231.
Rubinstein, J. S., Meyer, D. E., & Evans, J. E. (2001). Executive control of cognitive processes in task switching. Journal of Experimental Psychology: Human Perception and Performance, 27(4), 763–797.

Go to the Week’s Content

Week 6: Descriptive Statistics — Part 1

Before the proliferation of Global Positioning Systems, cellular telephone towers would coordinate between each other to identify (“triangulate”) the position of a phone. This type of comparison of information from multiple sources can often relay stronger and more accurate information. Quantitative analysis includes several methods that assist researchers in “triangulating” data to test the strength and accuracy of their information and their conclusions PSYC 6800: Applied Psychology Research Methods.

As you begin Module 2, you should reflect on what you learned in Module 1 and consider how that material might be applied to solving real-world issues. In this week, you will begin to compare variables within a dataset and reflect on what those comparisons might indicate. You will also begin Parts 4 and 5 of your Final Project Worksheet.

Learning Objectives

Students will:

• Analyze descriptive statistics of variables from a dataset
• Learning Resources

Required Readings

Johnson, R. B., & Christensen, L. B. (2020). Educational research: Quantitative, qualitative, and mixed approaches (7th ed.). Thousand Oaks, CA: Sage.

• Chapter 18, “Descriptive Statistics” (pp. 473–503)

Salkind, N. J. (2016). Excel statistics: A quick guide (3rd ed.). Sage.

• Excel Quickguide 41, “Descriptive Statistics” (pp. 92–93)

Leech, N. L., Onwuegbuzie, A. J., & Daniel, L. G. (2007a). Arithmetic mean. In N. J. Salkind (Ed.), Encyclopedia of Measurement and Statistics (pp. 43–44). Thousand Oaks, CA: Sage.

Leech, N. L., Daniel, L. G., & Onwuegbuzie, A. J. (2007b). Measures of central tendency. In N. J. Salkind (Ed.), Encyclopedia of Measurement and Statistics (pp. 586- 591). Thousand Oaks, CA: Sage.

Document: Final Project Overview (PDF)

Document: Income Data_50 U.S. States and Washington DC_2013-2018 Dataset (Excel worksheet)

Document: Working With Datasets Job Aid (PDF)

Note: This document provides step-by-step instructions about how to access and use Excel to conduct statistical analyses, as well as how to post visual displays of those analyses.

Required Media

Walden University (Producer). (2015). Measures of central tendency [Video file]. Baltimore, MD: Author.

Note: The approximate length of this media piece is 9 minutes.

Accessible player 
–Downloads–
Download Video w/CC
Download Audio
Download Transcript

Walden University (Producer). (2015b). Measures of variability [Video file]. Baltimore, MD: Author.

Note: The approximate length of this media piece is 12 minutes.

Accessible player 
–Downloads–
Download Video w/CC
Download Audio
Download Transcript

Excel Practice Datasets

Quick Guide Data Sets

SAGE Publications (2020). Q41.Descriptive Statistics.xlsx. https://study.sagepub.com/node/22833/student-resources/quick-guide-data-sets

Note: In order to accessthis data set, select the link that reads, “Click here to download all files” and select the files from the extracted file folder.

Check Your Understanding Data Sets

SAGE Publications (2020). QS41a.xlsx. https://study.sagepub.com/node/22833/student-resources/check-your-understanding-data-sets

SAGE Publications (2020). QS41b.xlsx. https://study.sagepub.com/node/22833/student-resources/check-your-understanding-data-sets

Note: In order to accessthese data sets, select the link that reads, “Click here to download all files” and select the two files from the extracted file folder.

---

Discussion: Using Descriptive Statistics to Solve Real-World Problems

Small actions can have big consequences. Consider bicycle gears. Depending on the state of the chain between the foot pedals to the rear wheel, a little effort on the former can mean a large rotation in the latter. The reverse can also be true. There is often a similar relationship in datasets between central tendencies (the core trends) and the variables.

This discussion requires you to apply your knowledge about types of descriptive statistics, specifically, measures of central tendency and measures of variability/dispersion PSYC 6800: Applied Psychology Research Methods.

To Prepare:

• Review the Learning Resources Salkind course text and the document Working With Datasets Job Aid for information about how to complete the tasks identified in the To Prepare and Post activities.
• Practice running descriptive statistics using the Quick Guide Data Set “Q41. DESCRIPTIVE STATISTICS.xls” and the Check Your Understanding Data Sets “QS41a” and “QS41b”.
• Download the Income Data_50 U.S. States and Washington DC_2013-2018 dataset from the Learning Resources and open the file in Excel.
• Select two states (avoid comparing Hawaii and Connecticut). Compute measures of central tendency (i.e., mean, median, and mode) and measures of variability/dispersion (i.e., range and standard deviation) for the income of two states you selected.

By Day 4

Post your response, in which you compare central tendency and variability/dispersion for two states for the complete span of 6 years. What does this comparison indicate? Post tables containing relevant data to support your comparison response.

By Day 6

Respond to one of your colleagues’ posts and explain your initial reaction to the comparison your colleague made. What did you find interesting? How might using descriptive statistics help to solve a problem in your field or discipline?

Be sure to support your postings and responses with specific references to the Learning Resources. Use proper APA format and citations PSYC 6800: Applied Psychology Research Methods.

Return to this Discussion in a few days to read the responses to your initial posting. Note what you have learned and/or any insights that you have gained as a result of your colleagues’ comments.

Submission and Grading Information

Grading Criteria

To access your rubric:

Week 6 Discussion Rubric

Post by Day 4 and Respond by Day 6

To participate in this Discussion:

Week 6 Discussion

---

Final Project: Connecting Real-World Data Through Quantitative Analysis

Consider the dataset you selected in Part 1. Using what you now understand about the relationship between variables and central tendencies, compute and analyze central tendencies for the variables you have selected for your Final Project. What conclusions can you begin to draw to address your comparative or relational research question?

To prepare:

Use your selected Final Project dataset to continue your work on Part 4: Descriptive Statistics and Part 5: Inferential Statistics of your Final Project Worksheet. Begin to compute and analyze the measures of central tendency (i.e., mean, median, and mode) and measures of variability/dispersion (i.e., range and standard deviation) for the variables you have selected for your Final Project.

Parts 4 and 5 of your Final Project Worksheet will be used to complete your Final Project narrative/report due by Day 5 of Week 11.

---

Week in Review

This week, you focused on how the information and skills you learned in Module 1 might be applied toward solving real-world issues. You compared variables from a dataset and considered what that comparison might mean. You also began to work on Parts 4 and 5 of your Final Project Worksheet.

Next week, you will focus on the income dataset you received this week and use it as a foundation to consider frequencies and percentages. You will also keep working on your Final Project.

Next Week

To go to the next week:

Week 7: Descriptive Statistics — Part 2

In 1969, a Gallup Poll in the United States asked, “If your party nominated a woman for President, would you vote for her if she qualified for the job?” Fifty-four percent of Americans surveyed at the time answered yes (Manksy, 2016). Opinion polls have been used to collect data from a representative sample of the population for decades. The results of these opinion polls are based on percentages, which are based on frequencies. Opinion polls are used to answer research questions that represent the field of political science, drawing upon fields such as sociology, economics, law, history, philosophy, geography, psychology, anthropology, and education PSYC 6800: Applied Psychology Research Methods.

For this week, you will reflect on the income dataset that differs slightly from the one provided to you in Week 6. This will provide a basis for you to consider frequencies and percentages. You will also continue working on your Final Project.

Reference
Manksy, J. (2016, September 26). Inside the alluring power of public opinion polls from elections past. Retrieved from https://www.smithsonianmag.com/history/alluring-power-public-opinion-polls-elections-past-180960571/#tOo3oXu25AQmrsbV.99

Learning Objectives

Students will:

• Compute frequencies/percentages of data
• Analyze frequency/percentage data
• Create visual display of frequency/percentage data

---

Learning Resources

Required Readings

Johnson, R. B., & Christensen, L. B. (2020). Educational research: Quantitative, qualitative, and mixed approaches (7th ed.). Thousand Oaks, CA: Sage.

• Chapter 18, “Descriptive Statistics” (pp. 473–503) (review)

Document: Final Project Overview (PDF)

Document: US Demographic Information dataset (Excel worksheet)

Document: Working With Datasets Job Aid (PDF)

Study Notes

Document: Descriptive Statistics (PowerPoint presentation)

---

Discussion: Frequencies/Percentages (Discrete or Non-Continuous)

One of the first examples of applying statistical analysis in the social sciences comes from W.E.B. Du Bois, a renowned African American scholar and civil rights activist. In 1899, he published The Philadelphia Negro, a groundbreaking study of the population of the 7th ward of Philadelphia. Du Bois relied on survey data he collected himself from 5000 residents. By relying on quantitative analyses, Du Bois was able to provide evidence that prevalent, contemporary discriminatory policies along with unequal opportunity created and perpetuated poverty and low socio-economic status. This research challenged the racist views held by many white Philadelphians at the time that African Americans were somehow inferior PSYC 6800: Applied Psychology Research Methods.

Using survey data based on sound research design allows scholar-practitioners to compute frequencies and percentages in order to explore social problems and potentially reshape how people define problems in ways that can effect positive social change.

_Reference:

_{Du Bois, W. E. B. (1899). The Philadelphia negro. Philadelphia, PA: University of Pennsylvania Press.}

For this Discussion, use the US Demographic Information dataset in the Learning Resources.

To Prepare:

• Please note that a red state is the term given to a U.S. state in which a majority of the electorate votes for the Republican candidate in a statewide election, whereas a blue state is the term given to a U.S. state in which a majority of the electorate votes for the Democratic candidate in a statewide election. In contrast, a purple state (or swing state) is the term given to a U.S. state in which both the Democratic and Republican candidates have a good chance of winning and is considered key to the outcome of a presidential election. Using the US Demographic Information dataset, add a column (categorical variable) to your dataset that indicates the political affiliation of each state (i.e., red, blue, or purple). Then, save the dataset with the new column and title it, “US Demographic Information _PA” dataset. You will use this new dataset again in Week 9.
• Search the Internet for a reliable, current source containing the political affiliation for each state. Input the overall political affiliation (i.e., red, blue, or purple) for each state included in your dataset.
• Compute the frequency and percentage by political affiliation of all states.
• Review the Learning Resources document Working With Datasets Job Aid for information about how to complete the tasks identified in the To Prepare and Post activities.

By Day 4 (Post First)

Post your computations of the frequency and percentage by political affiliation, including all states. Report the frequencies and percentages of the data. Report and describe the frequencies and percentages by political affiliation. Next, create a bar chart visual display depicting the variable Political Affiliation in the United States.

Note: The only variable to be involved is political affiliation, which has three levels: red, blue, or purple. You will need to report the frequencies and percentages of the red, blue, and purple states.

Read your colleagues’ postings.

By Day 6

Respond to one of your colleagues’ posts and explain what you found interesting or surprising. Based on what you found interesting or surprising, explain how this type of analysis might help to solve a real-world problem in your field or discipline.

Be sure to support your postings and responses with specific references to the Learning Resources. Use proper APA format and citations.

Note: For this Discussion, you are required to complete your initial post before you will be able to view and respond to your colleagues’ postings. Begin by clicking on the “Post to Discussion Question” link and then select “Create Thread” to complete your initial post. Remember, once you click on Submit, you cannot delete or edit your own posts, and you cannot post anonymously PSYC 6800: Applied Psychology Research Methods. Please check your post carefully before clicking on Submit!

Return to this Discussion in a few days to read the responses to your initial posting. Note what you have learned and/or any insights that you have gained as a result of your colleagues’ comments.

Submission and Grading Information

Grading Criteria

To access your rubric:

Week 7 Discussion Rubric

Post by Day 4 and Respond by Day 6

To participate in this Discussion:

Week 7 Discussion

---

Final Project: Connecting Real-World Data Through Quantitative Analysis

Continue working on Part 4 of your Final Project Worksheet. If you have not already done so, begin creating visual displays of the frequency and percentages of the variables you are examining for your Final Project.

Parts 4 and 5 of your Final Project Worksheet will be used to complete your Final Project narrative/report due by Day 5 of Week 11.

---

Week in Review

This week, you reflected on the dataset you obtained in the previous week and used it to consider frequencies and percentages. You also continued working on your Final Project.

Next week, you will examine the general linear model regarding data. You will also continue working on your Final Project Worksheet.

Next Week

To go to the next week:

Week 8

Week 8: Inferential — Part 1

Broadly speaking, a scientific model seeks to represent phenomena in a logical and systematic way. Although all models essentially are approximations, they can be extremely useful for the advancement of science. In the field of statistics, the most well-known and utilized model is called the General Linear Model. The general linear model is very useful because it covers most of the statistical analyses that are used in quantitative research.

Throughout this course, you focus on the two most basic types of statistical analyses covered by the general linear model: univariate (inferential) analyses and the independent samples t test. If you take any additional statistics courses, you will consider other general linear model analyses that will allow you to answer even more complex research questions.

In this week, you will explore the general linear model as you analyze data. You will also continue working on your Final Project.

Learning Objectives

Students will:

• Analyze types of data analyses that can be conducted using the General Linear Model

---

Learning Resources

Required Readings

Johnson, R. B., & Christensen, L. B. (2020). Educational research: Quantitative, qualitative, and mixed approaches (7th ed.). Thousand Oaks, CA: Sage.

• Chapter 19: “Inferential Statistics” (pp. 504–535)

Document: Final Project Overview (PDF)

Document: General Linear Model Worksheet (Word document)PSYC 6800: Applied Psychology Research Methods

Required Media

Walden University (Producer). (2016). What’s the best hypothesis test? [Video file]. Baltimore, MD: Author.

Note: The approximate length of this media piece is 7 minutes.

Accessible player 
–Downloads–
Download Video w/CC
Download Audio
Download Transcript

---

Discussion: Overview of Inferential Statistics

In the United States, the goal of a criminal trial is to resolve accusations made against a person who is accused of committing a crime. In common law systems, most criminal defendants undergo a trial that is held before a jury and that is prosecuted by the prosecuting attorney/lawyer.

In criminal trials, there are four possible outcomes that comprise two correct outcomes and two incorrect outcomes. The two correct decisions are:

• The defendant did not commit a crime, and the jury determines the correct verdict of not guilty.
• The defendant did commit a crime, and the jury determines the correct verdict of guilty.

The two incorrect decisions are:

• The defendant is not guilty of a crime, but the jury determines an incorrect verdict of guilty. Statisticians refer to this error as a Type I error. Sometimes it is referred to as a false positive.
• The defendant is guilty of a crime, but the jury determines an incorrect verdict of not guilty. Statisticians refer to this error as a Type II error. Sometimes it is referred to as a false negative.

Consider the following table:

Table I: In a courtroom case, if a defendant is found not guilty, it does not necessarily mean innocence; rather, it means that there is not enough evidence to support the verdict that the defendant is guilty.

 

		Reality
		Not Guilty	Guilty
Verdict	Not Guilty	✓	Type II error (β)
	Guilty	Type I error (α)	✓

 

Table II: With the courtroom case above, the jury is trying to determine if the evidence presented corresponds to the guilt or innocence of the defendant. With inferential statistics, a researcher is trying to determine from the evidence whether or not a meaningful correlation exists between a dependent variable and an independent variable. To put it plainly, does a change in the independent variable correspond to a change in the dependent variable. If a relationship/difference is rejected, it does not mean that there is no relationship/difference; rather, it means that there is not enough evidence to support the decision that there is a relationship/difference PSYC 6800: Applied Psychology Research Methods.

 

		Reality
		Do Not Reject	Reject
Decision	Do Not Reject	✓	Type II error (β)
	Reject	Type I error (α)	✓

 

Inferential statistics involves testing one or more hypotheses that stem from the research question(s) that attempt to establish whether a relationship/difference exists among the variable in the data. These hypotheses can test either for relationships or differences.

For this Discussion you will analyze types of data analyses that can be conducted using the General Linear Model.

To Prepare:

• Review and complete the General Linear Model Worksheet, identifying the most appropriate analysis for each worksheet item (i.e., correlation analysis, independent samples t test, or descriptive statistics).
• Select two scenarios from the worksheet that can best be analyzed using correlation analysis.
• Select two scenarios from the worksheet that can best be analyzed using independent samples t test.
• Identify a phenomenon in your field of study or discipline and consider how it could be studied using two types of analyses.

By Day 4

Post two examples of research scenarios that are best studied using correlation analysis and two examples of research scenarios that are best studied using independent samples t test. Explain how these two types of analyses could be applied to study a phenomenon in your field or discipline.

By Day 6

Respond to one of your colleagues’ posts from a different field or discipline and suggest how descriptive statistics could contribute to understanding the phenomenon.

Be sure to support your postings and responses with specific references to the Learning Resources. Use proper APA format and citations.

Return to this Discussion in a few days to read the responses to your initial posting. Note what you have learned and/or any insights you have gained as a result of your colleagues’ comments.

Submission and Grading Information

Grading Criteria

To access your rubric:

Week 8 Discussion Rubric

Post by Day 4 and Respond by Day 6

To participate in this Discussion:

Week 8 Discussion

---

Final Project: Connecting Real-World Data Through Quantitative Analysis

Refer to your Final Project Overview and continue working on Parts 4 and 5 of your Final Project Worksheet.

Parts 4 and 5 of your Final Project Worksheet will be used to complete your Final Project narrative/report due by Day 5 of Week 11.

---

Week in Review

This week, you explored the General Linear Model regarding data. You also continued working on your Final Project.

Next week, you will apply Excel to correlate data. You will also develop a visual demonstration of the correlation results. In addition, you continue working on your Final Project Worksheet.

Next Week

To go to the next week:

Week 9

Week 9: Inferential — Part 2

In a criminal trial, evidence can be either direct or circumstantial. Direct evidence stems from one or more eyewitnesses, who, via one or more of their five senses, have observed or experienced something relative to the crime in question or its circumstances, which leads to a factual conclusion. Conversely, circumstantial evidence represents evidence (e.g., physical or forensic evidence) from which an inference can be drawn to reach a conclusion regarding the underlying crime.  The difference between direct evidence and circumstantial evidence is shown via the following example:

Direct evidence: Witness A testifies that she saw the defendant stealing her statistics textbook from her dorm room.

Circumstantial evidence: Witness A testifies that the defendant, who was taking the same statistics course as her, was the only person who had a key to her dorm room, which was always locked when unoccupied, and wherein no sign of forced entry to her dorm room was found by the investigating officerPSYC 6800: Applied Psychology Research Methods.

Inferential statistical techniques provide circumstantial evidence that a relationship or difference exists in the population. The most commonly used statistical technique that always provides circumstantial evidence is the correlation coefficient. The correlation coefficient provides a measure of the statistical relationship between two variables. The correlation coefficient can indicate the magnitude and direction of the relationship between one independent variable and one dependent variable. However, a strong correlation does not imply a causal relationship exists between the two variables (i.e., correlation does not imply causation).

For this week, you will use Excel to practice correlating data. You will also create a scatterplot of the correlation results. Additionally, you will continue working on your Final Project.

Learning Objectives

Students will:

• Conduct correlation analysis
• Interpret the results of a correlation analysis

---

Learning Resources

Required Readings

Johnson, R. B., & Christensen, L. B. (2020). Educational research: Quantitative, qualitative, and mixed approaches (7th ed.). Thousand Oaks, CA: Sage.

• Chapter 18, “Descriptive Statistics” (pp. 473–503) (review)
• Chapter 19, “Inferential Statistics” (pp. 504–535) (review)

Salkind, N. J. (2016). Excel statistics: A quick guide (3rd ed.). Sage.

• Excel Quickguide 53, “The Correlation Tool” (pp. 119–120)

Onwuegbuzie, A. J., & Daniel, L. G. (2002). Uses and misuses of the correlation coefficient. Research in the Schools, 9(1), 73–90.

Leech, N. L., Daniel, L. G., & Onwuegbuzie, A. J. (2007d). Pearson’s product moment correlation coefficient. In N.J. Salkind (Ed.), Encyclopedia of Measurement and Statistics (pp. 750–755). Thousand Oaks, CA: Sage. doi:10.4135/9781412952644.n338

Siegle, D. (2015). R Critical Value Table. University of Connecticut. https://researchbasics.education.uconn.edu/r_critical_value_table/#

Document: Final Project Overview (PDF)

Document: Working With Datasets Job Aid (PDF)

Required Media

Walden University (Producer). (2015). Correlation and regression [Video file]. Baltimore, MD: Author.

Note:  The approximate length of this media piece is 9 minutes.

Accessible player 
–Downloads–
Download Video w/CC
Download Audio
Download Transcript

Walden University (Producer). (2019). Correlation analysis [Video file]. Baltimore, MD: Author.

Note: The approximate length of this media piece is 27 minutes.

Accessible player 
–Downloads–
Download Video w/CC
Download Audio
Download Transcript

Excel Practice Datasets

Quick Guide Data Sets

SAGE Publications (2020). Q53.Correlation.xlsx. https://study.sagepub.com/node/22833/student-resources/quick-guide-data-sets

Note: In order to accessthis data set, select the link that reads, “Click here to download all files” and select the files from the extracted file folder.

Check Your Understanding Data Sets

SAGE Publications (2020). QS53a.xlsx. https://study.sagepub.com/node/22833/student-resources/check-your-understanding-data-sets

SAGE Publications (2020). QS53b.xlsx. https://study.sagepub.com/node/22833/student-resources/check-your-understanding-data-sets

Note: In order to accessthese data sets, select the link that reads, “Click here to download all files” and select the two files from the extracted file folder.

---

Discussion: Correlation: Relating Two Variables

Classic economic theory states that as the price of something goes up, the demand for it goes down. For example, a new Ferrari can cost over $250,000. Not a lot of people can pay that price, and that is one reason why you do not see many people driving the new Ferraris. Classic economic theory also states that raising the minimum wage would increase unemployment because employers would not be able to pay the higher cost of wages. However, statistical analysis has shown that raising the minimum wage does not actually correlate with increased unemployment. Card and Krueger (1993) compared unemployment rates in New Jersey and Pennsylvania when New Jersey raised its minimum wage in 1992 from $4.25/hour to $5.05/hour and the minimum wage in Pennsylvania remained the same. These states are neighbors and shared enough traits in common to make the comparison appropriate. The researchers did not find that the increase in the minimum wage had any correlation with a negative impact on job growth. Using correlation to determine the relationship among two variables can be applied to many fields to explore how to effect positive social change PSYC 6800: Applied Psychology Research Methods.

Reference:                                                                                                                                                                                                                                                                                        Card, D., & Krueger, A. B. (1993). Minimum wages and employment: A case study of the fast food industry in New Jersey and Pennsylvania (No. w4509). Cambridge, MA: National Bureau of Economic Research. 

For this Discussion, you will expand the political affiliation dataset you created in Week 7 (i.e., US Demographic Information_PA).

To Prepare:

• Review the Learning Resources Salkind course text and the document Working With Datasets Job Aid for information about how to complete the tasks identified in the To Prepare and Post activities.
• Practice generating correlations using the Quick Guide Data Set “Q53 CORRELATION.xlsx” and the Check Your Understanding Data Sets “QS53a” and “QS53b”.
• Based on the political affiliation dataset from Week 7, add an additional column (variable) to your dataset and name it “Population Size.” This variable will be the most current population of each state.
• Search the Internet for a reliable source and locate the population size for each state. Input the most current population size for each state designated in your dataset.
• Select a year you wish to study and determine the correlation coefficient between population size and income for the year you chose.
• Using the R Critical Value Table, determine if the correlation coefficient you calculated is indicative of a significant relationship at the p < .05 and p < .01 alpha levels.

By Day 4 (Post First)

Post the correlation coefficient between population size and income in the United States for one chosen year (i.e., Pearson Correlation). Report your source(s) of information for your dataset. Next, based on the p value (for Sig. 2-tailed) report  whether there is a statistically significant relationship between population size and income in the United States for the chosen year. Explain what the results of your analysis mean. Next, create and post a scatterplot of your correlation and provide a brief explanation of your scatterplot.

By Day 6

Respond to one of your colleagues’ posts and explain what you found interesting or surprising about the correlation PSYC 6800: Applied Psychology Research Methods.

Be sure to support your postings and responses with specific references to the Learning Resources. Use proper APA format and citations.

Note: For this Discussion, you are required to complete your initial post before you will be able to view and respond to your colleagues’ postings. Begin by clicking on the “Post to Discussion Question” link and then select “Create Thread” to complete your initial post. Remember, once you click on Submit, you cannot delete or edit your own posts, and you cannot post anonymously. Please check your post carefully before clicking on Submit!

Return to this Discussion in a few days to read the responses to your initial posting. Note what you have learned and/or any insights that you have gained as a result of your colleagues’ comments.

Submission and Grading Information

Grading Criteria

To access your rubric:

Week 9 Discussion Rubric

Post by Day 4 and Respond by Day 6

To participate in this Discussion:

Week 9 Discussion

---

Final Project: Connecting Real-World Data Through Quantitative Analysis

Refer to your Final Project Overview and continue working on Parts 4 and 5 of your Final Project Worksheet.

Parts 4 and 5 of your Final Project Worksheet will be used to complete your Final Project narrative/report due by Day 5 of Week 11.

---

Week in Review

This week, you used Excel to correlate data. You also developed a visual display (i.e., scatterplot) of the correlation results. You also continued working on your Final Project.

Next week, you will finish Module 2 by using Excel to compare independent samples of data. You will also take a quiz over the material in Module 2. Finally, you will keep working on your Final Project Worksheet, due in Week 11.

Next Week

To go to the next week:

Week 10

Week 10: Inferential — Part 3

Learning Objectives

Learning Resources

Required Readings

Johnson, R. B., & Christensen, L. B. (2020). Educational research: Quantitative, qualitative, and mixed approaches (7th ed.). Thousand Oaks, CA: Sage.

• Chapter 18, “Descriptive Statistics” (review)

Salkind, N. J. (2016). Excel statistics: A quick guide (3rd ed.). Sage.

• Excel Quickguide 49, “t-Test: Two-Sample Assuming Equal Variances”

Document: Final Project Overview (PDF)

Document: Working With Datasets Job Aid (PDF)

Note: This document provides step-by-step instructions about how to access and use the program to conduct statistical analyses, as well as how to post visual displays of those analyses.

Required Media

Walden University (Producer). (2019f). Independent t test analysis [Video file]. Baltimore, MD: Author.

Note: The approximate length of this media piece is 33 minutes.

Accessible player 
–Downloads–
Download Video w/CC
Download Audio
Download Transcript

Walden University (Producer). (2015d). One-sample and two-sample t-tests [Video file]. Baltimore, MD: Author.

Note: The approximate length of this media piece is 8 minutes.

Accessible player 
–Downloads–
Download Video w/CC
Download Audio
Download Transcript

Excel Practice Datasets

Quick Guide Data Sets

SAGE Publications (2020). Q49.Ttest – Equal.xlsx. https://study.sagepub.com/node/22833/student-resources/quick-guide-data-sets

Note: In order to access this data set, select the link that reads, “Click here to download all files” and select the files from the extracted file folder PSYC 6800: Applied Psychology Research Methods.

Check Your Understanding Data Sets

SAGE Publications (2020). QS49a.xlsx. https://study.sagepub.com/node/22833/student-resources/check-your-understanding-data-sets

SAGE Publications (2020). QS49b.xlsx. https://study.sagepub.com/node/22833/student-resources/check-your-understanding-data-sets

Note: In order to access these data sets, select the link that reads, “Click here to download all files” and select the two files from the extracted file folder.

Discussion: Comparing Two Groups: Independent Sample t Tests

By Day 4 (Post First)

By Day 6

Submission and Grading Information

Grading Criteria

To access your rubric:

Week 10 Discussion Rubric

Post by Day 4 and Respond by Day 6

To participate in this Discussion:

Week 10 Discussion

Module 2 Quiz

By Day 7

Submission Information

Submit Your Quiz by Day 7

To submit your Quiz:

Module 2 Quiz

Final Project: Connecting Real-World Data Through Quantitative Analysis

Looking Ahead

Week in Review

Next Module

To go to the next module:

Module 3

Week 11: Interpretation

Learning Objectives

Learning Resources

Required Readings

Note: The “Expanding the Framework of Internal and External Validity in Quantitative Research” article was previously read in Week 5 PSYC 6800: Applied Psychology Research Methods.

Onwuegbuzie, A. J., & Daniel, L. G. (2003, February 12). Typology of analytical and interpretational errors in quantitative and qualitative educational research. Current Issues in Education, 6(2). Retrieved from https://cie.asu.edu/ojs/index.php/cieatasu/article/view/1609/651 [Seminal]

Onwuegbuzie, A. J., & Levin, J. R. (2005). Strategies for aggregating the statistical nonsignificant outcomes of a single study. Research in the Schools, 12(1), 10–19. [Seminal]

Credit Line: Onwuegbuzie, A. J.; Levin, J. R. (2005). Strategies for Aggregating the Statistical Nonsignificant Outcomes of a Single Study. Research in the Schools, 12(1), 10-19. Used with permission of Onwuegbuzie, A. J.

Walden University. (n.d.-b). Walden templates: General templates. Retrieved from https://academicguides.waldenu.edu/writingcenter/templates/general

Note: From the Walden Writing Center’s website page, Walden Templates: General Templates, use the Graduate Courses APA Course Paper Template for your Final project narrative/report. Also, from the same website page, go to the Course Paper Template Videos section and view the video tutorial, The Course Paper Template: Downloading and Using the Template.

Optional Resources

Leech, N. L., & Onwuegbuzie, A. J. (2004). A proposed fourth measure of significance: The role of economic significance in educational research. Evaluation and Research in Education, 18(3), 179–198. doi:10.1080/09500790408668317

Guides for Creating Tables

Onwuegbuzie, A. J. (2017).  Most common formal grammatical errors committed by authors. Journal of Educational Issues, 3, 109–140. Retrieved from https://files.eric.ed.gov/fulltext/EJ1142983.pdf

Frels, R. K., Onwuegbuzie, A. J., & Slate, J. R. (2010). Editorial: A step-by-step guide for creating tables. Research in the Schools, 17(2), xxxviii–lix. Retrieved from http://msera.org/docs/RITS_17_2_Tables.pdf PSYC 6800: Applied Psychology Research Methods.

 

Final Project: Connecting Real-World Data Through Quantitative Analysis

By Day 5

Submission and Grading Information

Grading Criteria

To access your rubric:

Week 11 Final Project Rubric

Check Your Assignment Draft for Authenticity

To check your Project draft for authenticity:

Submit your Week 11 Project draft and review the originality report.

Submit Your Assignment by Day 5

To submit your Project:

Week 11 Final Project

Week in Review