Writing Question

Post ID: 3515737

Overview

FOR SELF REFLECTIVE ESSAY AND SIGNATURE ESSAY

At the end of the Program, students will be able to:

Recognize how to apply the scholar-practitioner model to the development of evidence-based solutions when presented with a problem in professional practice [Scholar-practitioner Perspective].
Integrate relevant local evidence along with theories and principles from the applied psychology literature in service of evidence-based applications that advance professional practice [Translational Research Competence].
Select and apply appropriate research methods to develop and validate evidence-based solutions in professional practice [Investigative Competence].
Select the best media and communication methods to support issue-selling and implementation of evidence-based solutions [Professional Communication Skills].
Access and evaluate relevant information resources in service of translational research and evidence-based practice [Information Literacy].
Implement evidence-based solutions that demonstrate cultural responsiveness and ethical awareness [Professional Competence].
Use best organizational practices including collaboration and group processes to help groups attain success in implementing evidence-based solutions [Organizational Awareness].
Practice reflectively employing a progressive problem-solving process comprised of plan, act, observeand reflect (Reflective Practice).

Course Description: Building on knowledge and skills gained in Quantitative Research Methods I, the course will address the framing of research questions, selection of appropriate research methods and designs, selection of appropriate statistics for data analysis, principles of analysis, interpretation of findings, and the presentation of results. Students will learn how the techniques covered in the class apply to the translational and evaluation phases of the scholar-practitioner model.

Module 1

In this first module, we will focus on properly framing research questions. A well-framed research question is crucial because it guides our sampling strategies, defines the types of data we need to collect, and ultimately determines the statistical tests we will use to answer these questions.

Open.ED@PSU. (n.d.). Hypothesis testing. Pennsylvania State University – Eberly College of Science. https://online.stat.psu.edu/statprogram/reviews/statistical-concepts/hypothesis-testing

Najmi, A., Sadasivam, B., & Ray, A. (2021). How to choose and interpret a statistical test? An update for budding researchers. Journal of Family Medicine and Primary Care, 10(8), 2763–2767. https://doi.org/10.4103/jfmpc.jfmpc_433_21

Optional Reading:

Hypothesis Testing. (n.d.). Laerd Statistics. https://statistics.laerd.com/statistical-guides/hypothesis-testing.php

Hypothesis Testing Part 2. (n.d.). Laerd Statistics. https://statistics.laerd.com/statistical-guides/hypothesis-testing-2.php *Part 2

Hypothesis Testing Part 3. (n.d.). Laerd Statistics. https://statistics.laerd.com/statistical-guides/hypothesis-testing-3.php *Part 3

Module 2

In this unit, we will focus on the concept of “non-parametric statistical techniques.” Non-parametric statistics is not a single test but an umbrella term for various tests, including chi-square, McNemar test, Mann-Whitney U, Kruskal-Wallis H, and others. In parametric tests of significance, our goal is to estimate at least one parameter from our sample population, which requires meeting certain assumptions. One of the most critical assumptions is that our sample is normally distributed within the population for which we plan to generalize our findings. If this assumption cannot be met, we must use non-parametric statistical methods. For this reason, non-parametric tests are sometimes called “distribution-free” methods. They are particularly useful when researchers are dealing with small sample sizes or significant distortions, such as skewed data. While there are certain advantages to using non-parametric techniques, one major limitation is their inability to address multivariate questions. However, do not worry—we will cover that later in this course!

Required Reading:

Hoskin, T. (n.d.). Parametric and nonparametric: Demystifying the terms [PDF]. The Mayo Clinic. https://www.mayo.edu/research/documents/parametric-and-nonparametric-demystifying-the-terms/doc-20408960

Devore J.L., Berk K.N., & Carlton M.A. (2021). Nonparametric methods. Modern Mathematical Statistics with Applications. Springer Texts in Statistics. https://link.springer.com/chapter/10.1007/978-3-030-55156-8_14

Prof. Essa. (2021, March 1). Parametric vs. nonparametric statistical tests [Video]. YouTube. https://www.youtube.com/watch?v=biXY84hDX5M (3:05)

Prof. Essa. (2021, May 26). Chi-square test intro [Video]. YouTube. https://www.youtube.com/watch?v=GG7dFnvbMKI (3:57)

Optional Reading:

Laerd Statistics. (n.d.). Chi-square goodness-of-fit test in SPSS statistics. https://statistics.laerd.com/spss-tutorials/chi-square-goodness-of-fit-test-in-spss-statistics.php

Grambow, S. (2013). Parametric & Nonparametric Tests: The Rest of the Story [Video file]. Retrieved from : https://www.youtube.com/watch?v=frGwZJdOa74 (5:33)

Module 3

In this module, we will explore the t-test, a fundamental statistical tool used in research to compare the mean scores of two groups. Often referred to as the “Student’s t-test,” this test is widely utilized to determine whether there is a statistically significant difference between the means of two independent samples. The t-test is particularly valuable in studies where researchers need to compare two groups to understand the impact of a specific variable or treatment. The t-test can be applied in various contexts, from comparing the performance of two groups in an educational setting to examining the effectiveness of a new medication versus a placebo. However, it is important to recognize that the t-test is appropriate only when certain assumptions are met. These assumptions include the normal distribution of data, the homogeneity of variances, and the independence of observations. If these conditions are not satisfied, the t-test results may be misleading, leading to incorrect conclusions. In this module’s background readings, you will delve deeper into the situations where t-tests are appropriate and learn about the critical assumptions that underpin the t-test. Understanding these foundational concepts will help you correctly apply the t-test in your research and ensure that your findings are reliable and valid.

Required Reading:

What Are The Differences Between One-tailed And Two-tailed Tests? (2022). UCLA: Advanced Research Computing. https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faq-what-are-the-differences-between-one-tailed-and-two-tailed-tests/

Prof. Essa. (2021, September 13). Independent samples T test SPSS [Video]. YouTube. https://www.youtube.com/watch?v=E4IHy1vy0xk (5:10)

Prof. Essa. (2021, May 26). Chi-square test intro [Video]. YouTube. https://www.youtube.com/watch?v=GG7dFnvbMKI (3:57)

Bakshi, S. (2021, July 13). Independent samples t-test in SPSS | t-test in SPSS (Part 3) | SPSS for beginners [Video]. YouTube. https://www.youtube.com/watch?v=RwoFrc1gfNY (9:47)

Cote, L.R. & Gordan, R.G. (n.d.). Chapter 8: Introduction to T-tests. Introduction to Statistics in the Psychological Sciences. https://umsystem.pressbooks.pub/isps/chapter/chapter-8/

Optional Materials

Laerd Statistics. (n.d.). Dependent T-test using SPSS statistics. https://statistics.laerd.com/spss-tutorials/dependent-t-test-using-spss-statistics.php

UCLA: Advanced Research Computing. (n.d.). SPSS annotated output T-test. https://stats.oarc.ucla.edu/spss/output/t-test/

Module 4

In research, it’s common to compare several different groups on a particular measure to understand how they differ. While a t-test is a valuable tool for comparing the means of two groups, it becomes problematic when applied repeatedly to more than two groups. Running multiple t-tests for 3, 4, 5, or more groups increases the likelihood of a Type-I error, which is the incorrect rejection of a true null hypothesis. In other words, the more t-tests you run, the greater the chance of finding a significant difference purely by chance rather than because of a true effect.

Researchers use more sophisticated techniques like ANOVA (Analysis of Variance) and MANOVA (Multivariate Analysis of Variance) when comparing the means across multiple groups to avoid this issue. ANOVA is used when there is one dependent variable and one or more independent variables. It helps determine whether there are any statistically significant differences between the means of three or more groups. On the other hand, MANOVA extends this analysis to scenarios where there are multiple dependent variables. A MANOVA allows researchers to assess whether group differences exist across multiple measures simultaneously.

Understanding when and how to apply these techniques is crucial for accurately interpreting your data and avoiding the pitfalls associated with multiple comparisons. To delve deeper into these powerful statistical methods and their applications, please refer to this module’s background reading.

Required Reading:

Open.ED@PSU. (2022). Multivariate Analysis of Variance (MANOVA). Pennsylvania State University – Eberly College of Science. https://online.stat.psu.edu/stat505/lesson/8

Geert van den Berg, R. (2022). SPSS One-Way ANOVA with Post Hoc Tests Tutorial. SPSS Tutorials. https://www.spss-tutorials.com/spss-one-way-anova-with-post-hoc-tests-example/

Cote, L.R. & Gordan, R.G. (n.d.). Chapter 11: Analysis of variance. Introduction to Statistics in the Psychological Sciences. https://umsystem.pressbooks.pub/isps/chapter/chapter-11/

Kent State University Libraries. (2021). SPSS tutorials: One-way ANOVA. https://libguides.library.kent.edu/spss/onewayanova

Optional Materials:

Prof. Essa. (2021, September 20). How to run a one-way ANOVA in SPSS [Video]. YouTube. https://www.youtube.com/watch?v=P_SU1BssitE (5:28)

Crowson, M. (2020, June 19). One-way ANOVA and Tukey's post hoc tests via two routes in SPSS [Video]. YouTube. https://www.youtube.com/watch?v=dUK4eoBj0ms (12:51)

Laerd Statistics. (n.d.). One-way ANOVA in SPSS. https://statistics.laerd.com/spss-tutorials/one-way-anova-using-spss-statistics.php

Module 5

Correlational techniques are fundamental tools used to explore and analyze the relationship between variables. In exploratory studies, correlation is particularly valuable for determining whether a relationship exists among the variables being studied, offering insights that can guide further research. These techniques are most often applied in studies that aim to test hypotheses, helping researchers identify patterns and associations between variables without making assumptions about cause and effect. One crucial point to remember is that correlation does not imply causation. While a correlation can show that two variables are related, it does not indicate that one variable causes the other to change. This distinction is essential in research, as it prevents the misinterpretation of results and encourages more rigorous analysis to uncover underlying causes. To gain a deeper understanding of this important technique and how it can be applied in research, please proceed to this module’s background reading.

Objectives

Required Reading:

Trochim, W. (2022). Correlation. Conjoint.ly. http://www.socialresearchmethods.net/kb/statcorr.php

Geert van den Berg, R. (2022). SPSS correlation test – Simple tutorial. SPSS Tutorials. https://www.spss-tutorials.com/spss-correlation-test/

Aldrich, J. O. (2023). Chapter 23: Simple Linear Regression, step-by-step SPSS® tutorial videos. Sage. https://study.sagepub.com/aldrich3e/student-resources/step-by-step-spss%C2%AE-tutorial-videos

UCLA: Advanced Research Computing. (2022). Correlation | SPSS annotated output. https://stats.oarc.ucla.edu/spss/output/correlation/

Cote, L.R. & Gordan, R.G. (n.d.). Chapter 12: Correlations. Introduction to Statistics in the Psychological Sciences. https://umsystem.pressbooks.pub/isps/chapter/chapter-12/

Module 6

In contrast to correlation, which identifies the relationship between two variables, linear regression goes further by using that relationship to make predictions. While correlation helps us understand whether and how two variables are related, linear regression allows us to predict one variable’s value based on another’s value. This makes linear regression a powerful tool for making predictions and understanding underlying patterns in psychological research. Linear regression works by fitting a line to the data points that best represents the relationship between the independent variable (the predictor) and the dependent variable (the outcome). The equation of this line, often referred to as the regression equation, is then used to predict the dependent variable’s score based on any given value of the independent variable. For instance, in a study examining the relationship between stress levels and coping strategies, linear regression could be used to predict an individual’s stress level based on the frequency or type of coping strategies they use. The strength and direction of the relationship are quantified by the slope of the regression line. However, it is important to remember that, like correlation, regression analysis does not imply causation; it merely indicates the extent to which one variable can predict another based on their established relationship. To gain a deeper understanding of this powerful predictive technique, including how to interpret the results and apply them in your psychological research, please proceed to this module’s background reading.

Required Reading:

Cote, L.R. & Gordan, R.G. (n.d.). Chapter 13: Linear regressions. Introduction to Statistics in the Psychological Sciences. https://umsystem.pressbooks.pub/isps/chapter/chapter-13/

UCLA: Advanced Research Computing. (2022). Introduction to regression with SPSS Lesson 3: SPSS regression with categorical predictors. https://stats.oarc.ucla.edu/spss/seminars/introduction-to-regression-with-spss/introreg-lesson3/

Kent State University Libraries. (2021). SPSS tutorials: Pearson correlation. https://libguides.library.kent.edu/SPSS/PearsonCorr

Optional Reading:

Princeton University. (2007). Introduction to regression. Retrieved from http://dss.princeton.edu/online_help/analysis/regression_intro.htm

ConnectCubed (2014) What is R-Squared [Video file]. Retrieved from https://www.youtube.com/watch?v=lZtyxpHsBlg (5:27)

Quantitative Specialists (2014). Pearson r Correlation in SPSS [Video file]. Retrieved from https://www.youtube.com/watch?v=IIlyI7bsvIQ (4:35)

Module 7

One of the most crucial sections of a research paper is the “Results” section. When reading research studies, you’ll notice that authors begin by presenting key information and then build a case for why their study is necessary and valuable. In the “Results” section, the researcher reports the scientific basis for the conclusions they will draw. As you can imagine, this makes the results section a vital part of the research report. The Results section is typically filled with statistics and the outcomes of statistical tests that the researcher used to answer their research questions or test their hypotheses. Unfortunately, this section is often “glossed over” by readers who may not fully understand how to interpret the presented statistics. This is regrettable because the ultimate goal of research is to translate new knowledge into practice—a topic we will discuss in our final module.

Required Reading:

UCLA: Advanced Research Computing. (2022). Statistical writing. https://stats.oarc.ucla.edu/other/mult-pkg/seminars/statistical-writing/

USC Libraries. (2022, February 15). Organizing your Social Sciences research paper. https://libguides.usc.edu/writingguide/results

Wendorf, C. A. (n.d.). Reports – APA style: summary of parametric statistic reporting. Sourcebook. https://cwendorf.github.io/Sourcebook/Reports/apa-style/summary.html Archival link: https://web.archive.org/web/20230519214329/https://cwendorf.github.io/Sourcebook/Reports/apa-style/summary.html

Wendorf, C. A. (n.d.). Reports – APA style: examples of results in tables. Sourcebook. https://cwendorf.github.io/Sourcebook/Reports/apa-style/tables.html Archival link: https://web.archive.org/web/20230519214329/https://cwendorf.github.io/Sourcebook/Reports/apa-style/tables.html

Module 8

In this final module, we focus on the very essence of the “Scholar-Practitioner”—the ability to translate research findings into practical applications. Before the advent of practice doctorates, there was a clear divide between researchers and academicians, who focused on theoretical research, and practitioners in the field, who applied their knowledge in real-world settings. This gap often resulted in a disconnect where research findings suggested one course of action while practitioners in the field were doing something entirely different. When this “divide” was pointed out, the common response was that researchers were “far removed from practice,” often described as being in an “ivory tower” with little understanding of the day-to-day realities faced by practitioners. To bridge this gap, the Scholar-Practitioner model was developed. A Scholar-Practitioner is someone who is not only well-versed in research but also in the practical application of that research within their discipline. This individual plays a crucial role in answering practical questions by linking research to real-world practice issues from a solution-oriented perspective. The Scholar-Practitioner approach ensures that research is not just theoretical but is directly applicable to solving problems in the field. This concept will be discussed in greater detail in this module’s background reading.

Objectives

Examine essential features of an appropriately framed research question
Identify situations in which non-parametric statistical methods should be used
Explore the use and limitations of the t-test
Discern between the use of ANOVA and MANOVA
Apply principles of correlation and regression appropriately
Discuss the use of advanced statistical techniques and their applicability to research methods
Demonstrate the accurate interpretation of statistical test results
Postulate ways of translating research findings into practice

Required Reading:

Connor, L., Dean, J., McNett, M., Tydings, D. M., Shrout, A., Gorsuch, P. F., Hole, A., Moore, L., Brown, R., Melnyk, B. M., & Gallagher-Ford, L. (2023). Evidence-based practice improves patient outcomes and healthcare system return on investment: Findings from a scoping review. Worldviews on Evidence-Based Nursing, 20(1), 6-15. https://doi.org/10.1111/wvn.12621

Borsboom, D., van der Maas, H. L. J., Dalege, J., Kievit, R. A., & Haig, B. D. (2021). Theory Construction Methodology: A practical framework for building theories in Psychology. Perspectives on Psychological Science, 16(4), 756-766. https://doi.org/10.1177/1745691620969647

Optional Materials

Richardson, D. S., Bledsoe, R. S., & Cortez, Z. (2020). Mindset, motivation, and teaching practice: Psychology applied to understanding teaching and learning in STEM disciplines. CBE life sciences education, 19(3), ar46. https://doi.org/10.1187/cbe.19-11-0238