Submit A Basic Research Proposal That Calls For Use Of A

Submit A Basic Research Proposal That Calls For The Use Of A Dependent

Submit a basic research proposal that calls for the use of a dependent-samples t test or repeated-measures ANOVA. The paper should be APA formatted as a research proposal, and contain approximately words of content. Include a title page, and a reference page that includes any resources utilized. Please include the following in the research proposal: 1. Introduction (1-2 paragraphs) Present the research question of interest. Explain how the chosen statistical test applies to this research question. Provide the statistical notation and written explanations for the null and alternative hypotheses. 2. Methods (1 paragraph) Participants List how many participants will be selected. Identify who will be the participants and their major demographic characteristics (e.g., sex, age, etc.). Explain how participants will be selected for the study. 3. Procedures (1-2 paragraphs) Identify the variables in the study. Describe each variable’s scale of measurement (nominal, ordinal, interval, or ratio) and characteristics (i.e., discrete vs. continuous, qualitative vs. categorical, etc.). Provide an operational definition for each variable, explaining how the variables will be measured. 4. Results (2-3 paragraphs) Describe the statistical test that will be conducted. Be sure to include why the test was chosen and why it is appropriate for this study. Include in the discussion the necessary assumptions that should be met for the chosen test and how these will be addressed. Identify the information that will be obtained from the results of this test and what will be needed to draw conclusions regarding the hypotheses. Be sure to include a discussion of applicable critical and calculated values, p levels, confidence intervals, effect sizes, post-hoc tests, and/or tables. 5. Discussion (1 paragraph) Identify any expected biases, assumptions, or faults with the proposed study and the use of the identified statistical test. Explain what conclusions can and cannot be made for this study, and using this statistical test. Describe the practical significance or importance of the results.

Paper For Above instruction

The present study aims to investigate the effect of a specific intervention on participants' stress levels. The primary research question asks: Does the intervention significantly reduce stress levels as measured before and after the intervention? To analyze this question, a repeated-measures design will be employed, and a dependent-samples t test will be used to compare pre- and post-intervention stress scores within the same participants. This statistical test is appropriate because it assesses the mean differences between two related groups, controlling for individual variability. The hypotheses are as follows: H₀ (null hypothesis): μ_before = μ_after (there is no difference in stress levels before and after the intervention); H₁ (alternative hypothesis): μ_before ≠ μ_after (there is a significant difference in stress levels before and after the intervention). The notation reflects the comparison of population means related to the measure of stress at two time points within the same sample.

The study will recruit 30 adult participants aged between 20 and 50 years, selected via convenience sampling from university staff and students interested in stress management programs. Participants will be balanced in terms of sex distribution and will have no prior diagnosis of chronic stress disorders. Inclusion criteria include willingness to participate, no current use of psychotropic medication, and the availability to attend both assessment sessions. Demographic data such as age, sex, and education level will be collected to characterize the sample.

The variables in the study include the stress level, which will be measured using a validated self-report questionnaire—such as the Perceived Stress Scale (PSS). This variable is continuous and interval-scaled, providing numerical scores that reflect perceived stress severity. Stress levels are operationally defined as the total score obtained from the PSS, with higher scores indicating greater stress. The intervention itself is an independent variable that is manipulated within subjects but is not a variable of measurement in the traditional sense for this analysis.

The statistical analysis will involve conducting a dependent-samples t test to compare the mean stress scores before and after the intervention. This test is chosen because of its suitability for repeated-measures data, where the same participants are measured at two different time points. The assumptions of the dependent t test include the normality of the difference scores, the scale of measurement being interval or ratio, and the independence of observations within pairs. Normality will be assessed using the Shapiro-Wilk test, and if violated, a non-parametric alternative such as the Wilcoxon signed-rank test may be considered. The test will yield a t-value, degrees of freedom, and a p-value to determine statistical significance.

The critical t-value will be obtained from the t-distribution table based on the degrees of freedom (n-1). Effect sizes will be calculated using Cohen's d to understand the magnitude of the difference. Confidence intervals for the mean difference will provide estimates about the precision of the effect. If significant, post-hoc analysis is unnecessary due to only two time points; otherwise, exploratory analyses may be conducted to assess patterns. The results will inform whether the intervention has a statistically and practically meaningful impact on stress levels, supporting or refuting the null hypothesis.

In the discussion, potential biases such as self-report bias and sample selection bias will be acknowledged. Assumptions such as normality will be explicitly tested, and deviations will be addressed with appropriate statistical adjustments. It will also be discussed that causality cannot be definitively established due to the study's design limitations. The findings' practical significance will be highlighted, emphasizing the intervention’s potential for stress reduction in real-world settings. Overall, the study expects to contribute valuable insights into stress management techniques, with implications for mental health practitioners and policy makers.

References

• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.
• Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage.
• Harrell, F. E. (2015). Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis. Springer.
• Lehmann, E. L., & Romano, J. P. (2005). Testing statistical hypotheses (3rd ed.). Springer.
• Morling, B. (2017). Research methods in psychology: Evaluating a world of information. WW Norton & Company.
• Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics. Pearson.
• Vickers, A. J. (2014). Analysing controlled trials with baseline and follow-up measurements. BMJ, 348, g1051.
• Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80–83.
• Zimmerman, D. W. (1997). A note on the normality assumption in large public samples. The Journal of Experimental Education, 65(2), 165-173.