Mini Research Proposal: Written Assignment Based On Thesis

Mini Research Proposalthis Written Assignment Is Based On The Work Con

This mini research proposal is developed based on the work conducted in the “Z, T, or Chi-Square Test Study” discussion forum from last week, incorporating feedback received and additional research. The proposal aims to identify a specific research question and select an appropriate statistical test—either Z-test, T-test, or Chi-Square test—to analyze the data. It provides a comprehensive plan, including the introduction with hypotheses, methods detailing participants and procedures, anticipated results with justification for the chosen statistical test, and a discussion addressing potential biases and the significance of the findings. The entire document will be formatted according to APA standards, contain approximately 1000 to 1350 words, and include a title page and references.

Paper For Above instruction

Introduction

The research question of interest for this study pertains to examining whether there is a significant difference in test scores between students who utilize a specific study technique and those who do not. Specifically, the study aims to investigate if implementing a new cognitive strategy affects students' academic performance. The appropriate statistical test for this investigation is the independent samples t-test, which compares the means of two independent groups where the variable of interest is continuous and normally distributed.

The null hypothesis (H₀) for this study states that there is no difference in mean test scores between students using the new study technique and those not using it: H₀: μ₁ = μ₂. Conversely, the alternative hypothesis (H₁) posits that there is a statistically significant difference in the mean scores: H₁: μ₁ ≠ μ₂. Using statistical notation, this can be expressed as:

• H₀: μ₁ – μ₂ = 0
• H₁: μ₁ – μ₂ ≠ 0

Potential errors in hypothesis testing include Type I errors, where the null hypothesis is incorrectly rejected (a false positive), and Type II errors, where the null hypothesis is incorrectly accepted (a false negative). The significance level (α) determines the threshold for Type I errors, typically set at 0.05.

Participants

The study will include 60 undergraduate university students enrolled in a psychology course, with an equal number of males and females to ensure demographic balance. Participants will primarily be aged between 18 and 25 years, representing typical college-aged students. Participants will be recruited through university email lists and class announcements. Selection will be via random sampling to reduce selection bias, ensuring each eligible student has an equal chance of participation. Inclusion criteria stipulate that participants are enrolled in the course and consent to participate, while exclusion criteria eliminate students with prior familiarity with the study technique being tested.

Procedures

The independent variable in this study is the use of a new study technique, categorized as two levels: use and non-use. It is a categorical, nominal variable. The dependent variable is the students’ test scores, measured on an interval scale, providing continuous data suitable for the t-test. The operational definition of the study technique involves a specific cognitive strategy introduced to participants in the experimental group through a workshop, whereas the control group receives standard study instructions. Test scores will be operationalized as the final grade percentage obtained on a standardized quiz administered after four weeks of study.

Participants will be randomly assigned to either the experimental group, which will be trained in the new technique, or the control group, which will not. The test scores will then be collected and analyzed to determine if there is a significant difference attributable to the intervention.

Results

The statistical analysis will employ an independent samples t-test due to the comparison between two independent groups and the continuous nature of the outcome measure. The test was chosen because preliminary data suggest a normal distribution of scores within groups and similar variances, satisfying the assumptions of parametric testing. The t-test will generate a p-value that indicates the probability of observing the data if the null hypothesis were true. A p-value less than 0.05 will denote statistically significant differences in mean test scores, allowing us to reject H₀ in favor of H₁.

From the results, we will obtain the t-statistic, degrees of freedom, and the mean difference between groups. These will inform whether the intervention has a meaningful effect on student performance. Additionally, effect size measures such as Cohen’s d will be calculated to evaluate the practical significance of observed differences.

Discussion

Potential biases in this study include the Hawthorne effect, where students may alter their behavior because they are aware of being studied, and selection bias, despite random sampling efforts. Assumptions underlying the t-test include normality of the distribution within groups and homogeneity of variances; violations could compromise the validity of results. Furthermore, the sample size, although sufficient, may limit generalizability to broader populations.

Conclusions drawn will be limited to the specific sample and context under study. The t-test will enable us to determine whether the observed differences are statistically significant, but it does not establish causality or practical importance alone. The practical significance of the findings could influence educational practices by identifying effective study strategies, contingent upon effect sizes and real-world applicability. Recognizing the limitations and potential biases assures cautious interpretation and highlights the need for further replication and diverse samples.

References

• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
• Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage Publications.
• Gravetter, F. J., & Wallnau, L. B. (2016). Statistics for the behavioral sciences (10th ed.). Cengage Learning.
• Keselman, H. J., et al. (1998). Statistical methods for psychological research. Routledge.
• Levine, J. M., & Hullett, C. R. (2002). Eta squared, partial eta squared, and standardized eta squared in current use. Behavior Research Methods, 34(3), 377–392.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.
• Urdan, T. C., & Zumbo, B. D. (2009). Rank-based and resampling methods for educational and social research. Routledge.
• Wilkinson, L. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54(8), 594–604.
• Zimmerman, B. J., & Schunk, D. H. (2011). Self-regulated learning and academic achievement: Theoretical perspectives. Routledge.
• Field, A., Miles, J., & Attridge, P. (2012). Discovering statistics using R. Sage Publications.