Research Design Questions Suppose You Are A Researcher Wh

Research Design Questionssuppose You Are A Researcher Wh

Suppose you are a researcher who wants to evaluate which type of course-delivery format (online, blended, or face-to-face) leads to the best performance in a psychological statistics class. In a 2-3 page paper, identify the following: What is your research question? (Please remember to focus your study on the evaluation of the various types of course delivery for statistics courses.) What is your hypothesis (both null and alternate)? Is this a qualitative or quantitative design (based on type of variable collected) and why? Explain if your study would be classified as a descriptive, correlational or experimental design. What would be an example of a variable for this study of course delivery formats that could be measured on a nominal scale? Ordinal scale? Interval scale? Ratio scale? Once you have collected your data on the effectiveness of the various types of course delivery for a statistics course, would you use inferential or descriptive statistics and why? Create a sample frequency distribution for one of the variables. Choose either a simple or grouped frequency distribution and explain your choice. Write a 2–3-pages in Word format. Apply APA standards to citation of sources.

Paper For Above instruction

The evaluation of course delivery formats—online, blended, or face-to-face—in terms of student performance in a psychological statistics class presents a vital area of research within educational psychology and instructional design. The primary research question guiding this study is: "Which course delivery format (online, blended, or face-to-face) results in the highest student performance in a psychological statistics course?" The aim is to discern the impact of different instructional modalities on student achievement, facilitating evidence-based decisions in curriculum development and instructional strategies.

Formulating hypotheses involves establishing expectations about the outcomes. The null hypothesis (H0) posits that there is no significant difference in student performance across the three course delivery formats. Conversely, the alternative hypothesis (H1) suggests that at least one format leads to significantly different performance levels; specifically, it may be hypothesized that face-to-face instruction yields higher performance due to direct student-instructor interaction, although this is subject to empirical testing.

The research design employed in this study aligns with quantitative methodology, primarily because the variables of interest—student performance scores—are numerical and measurable. Quantitative research facilitates statistical analysis, enabling the comparison of performance metrics across different delivery modes. This design allows for objective measurement and analysis of the outcomes, adhering to the scientific rigor necessary for reliability.

The study can be classified as an experimental design, assuming that students are randomly assigned to different course formats to control for confounding variables. This approach ensures that observed differences in performance can be causally attributed to the course delivery method rather than extraneous factors. Although observational studies could also be conducted, an experimental framework with random assignment offers stronger internal validity.

Variables measured in this research include the performance scores on assessments. A variable such as "type of course delivery" is categorical and can be measured on a nominal scale—categorizing students into groups based on whether they participated in online, blended, or face-to-face classes. Performance scores themselves are typically measured on an interval or ratio scale, as they involve numerical data reflecting student achievement.

Other variables could include students’ self-reported satisfaction or engagement, which might be measured on an ordinal scale, such as a Likert-type rating system, indicating levels of agreement or satisfaction. This scaling captures the degree of perception or attitude but does not quantify it in absolute terms, making it ordinal.

Upon data collection, the analysis would likely involve inferential statistics to determine whether differences in performance scores are statistically significant. Inferential statistics—such as ANOVA (Analysis of Variance)—would allow comparison across the three groups to generalize findings beyond the sample. Descriptive statistics, like means and standard deviations, would provide an overview of the data but are insufficient alone for hypothesis testing. Inference allows conclusions about the broader student population.

A sample frequency distribution for the variable "performance level" categorized as "High," "Medium," or "Low" can be constructed for illustrative purposes. Suppose out of 60 students, 15 scored high, 25 scored medium, and 20 scored low. A grouped frequency distribution would group these scores into intervals or categories, making it easier to observe the distribution pattern and identify trends.

Choosing a grouped frequency distribution over a simple one hinges on the data's nature. Given that student scores can vary widely and are more meaningful when categorized into performance bands, a grouped distribution offers a clearer representation of the data. It simplifies interpretation by consolidating data points into meaningful categories, facilitating easier analysis of overall performance trends within each instructional format.

In conclusion, this study aims to scientifically evaluate the impact of different course delivery modes on student performance in a psychological statistics course. Employing a quantitative, experimental design with appropriate statistical analyses ensures robust and valid findings. These results can inform best practices for instructional delivery, ultimately improving educational outcomes in higher education settings.

References

• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.
• Gall, M. D., Gall, J. P., & Borg, W. R. (2007). Educational research: An introduction (8th ed.). Pearson.
• Leedy, P. D., & Ormrod, J. E. (2010). Practical research: Planning and design (9th ed.). Pearson.
• McMillan, J. H. (2012). Classroom assessment: Principles and practice for effective standards-based instruction. Pearson.
• Maxwell, J. A. (2013). Qualitative research design: An interactive approach. Sage.
• Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage.
• Hinkle, D. E., Wiersma, W., & Jurs, S. G. (2003). Applied statistics for the behavioral sciences (5th ed.). Houghton Mifflin.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.
• Fisher, R. A. (1925). Statistical methods for research workers. Oliver and Boyd.
• Irwin, K., & Irwin, R. (2021). Research methods for education and psychology. Routledge.