# Your Research Design After reviewing the Module 4 Assignment, discuss the following: Indicate your research design (i.e., correlational, experimental, quasi

After reviewing the Module 4 Assignment, discuss the following:

Indicate your research design (i.e., correlational, experimental, quasi-experimental, or causal-comparative, etc.)
State the descriptive (i.e., mean, median, mode, standard deviation, range, percentages, etc.)
State the inferential statistics you will be using to analyze your data given your variables and design.

Edu503week5discussionandpeerresponses.docx

DiscussionModule 5 Discussion

After reviewing the  Module 4 Assignment , discuss the following:

· Indicate your research design (i.e., correlational, experimental, quasi-experimental, or causal-comparative, etc.)

· State the descriptive (i.e., mean, median, mode, standard deviation, range, percentages, etc.)

· State the inferential statistics you will be using to analyze your data given your variables and design.

Research Evaluation: Part 2

Read and watch the lecture resources & materials below early in the week to help you respond to the discussion questions and to complete your assignment(s).

· Beins, B.C. (2021).

· Chapter 5—Communicating Statistics

· Chapter 6—The Results

· Chapter 7—The Discussion Section

Peer responses

Research Design

Given that the study aims to establish the nature of the relationship between the strategies used by educational leaders and the effective use of digital technologies in STEM education, the research design adopted for this study is correlational. This design is chosen because it will enable the author to determine the relationships between various leadership practices and the impact of digital tool implementation within STEM curricula. Thus, through the analysis of regularities and interrelations, the study seeks to define which approach is most effective in increasing the digital component and addressing the issue of educational effectiveness.

Descriptive Statistics

Furthermore, descriptive statistics will be used to analyze the data regarding the success of integrating digital tools. These statistics will include measures such as average, first quartile, second quartile, third quartile, mid-range, and standard error. The mean will represent the average achievement in different educational contexts while the median and mode will show the usual success levels. The standard deviation will give the dispersion of success rates which will further explain the extent of the integration of digital tools in different schools.

Inferential Statistics

Descriptive statistics and, in particular, the Pearson correlation coefficient will be used to compare the correlations between the strategies of educational leaders and the success of digital tool integration. This is because the Pearson correlation coefficient will quantify the degree of relationship between leadership practices and integration success. This analysis will help ascertain if some of the strategies presented are significantly related to higher outcomes in STEM education digitalization, and provide important recommendation evidence to the educational leaders.

Research Design

The research design of this study is correlational, intending to investigate the association between involvement in extracurricular activities and their effects on both academic performance and social skills among high school students in metropolitan locations. This approach enables us to determine and measure the degree of correlation between these two variables without changing any variables or demonstrating causality (Creswell, 2018).

Descriptive Statistics

Descriptive statistics will be used to provide a concise overview of the data collected from the sample. These statistics will aid in comprehending the fundamental characteristics of the data obtained from participants, encompassing:  Purpose: To calculate the mean of students’ GPAs and scores on social skills examinations. This will provide a deeper understanding of the participants’ comprehensive academic achievement and social skill proficiency.  Median: This is a statistical metric that helps determine the middle point in the distribution of students’ GPAs and social skills scores. It is a more reliable measure of central tendency since it is less influenced by extreme values.  Mode: This analysis aims to identify the GPA scores and social skills evaluations that occur most often, hence emphasizing the prevalent performance levels among students.  Standard Deviation: A statistical metric used to quantify the spread or variability in academic achievement and social skills, demonstrating the extent of deviation from the average.  Range: To evaluate the extent of variation between the best and lowest scores in both academic achievement and social skills.  Percentages are used to provide a concise summary of categorical data, such as the percentage of students involved in various extracurricular activities including sports, arts, and clubs.  The descriptive statistics will provide a thorough summary of the sample’s features and early observations about the dataset (Trochim & Donnelly, 2016).

Inferential Statistics

The data will be analyzed using inferential statistics to make inferences about the relationships between the variables, given the correlational design. The inferential statistics used will encompass:  The Pearson Correlation Coefficient (r) will be used to quantify the magnitude and direction of the linear association between engagement in extracurricular activities and academic achievement, as well as social skills. The Pearson correlation coefficient will give a measurable indication of the strength of the relationship between these variables within the sample (Fredricks & Eccles, 2010).  Multiple regression analysis will be used to examine the influence of various independent variables, such as different kinds of extracurricular activities and demographic characteristics, on dependent variables such as academic achievement and social skills. Utilizing multiple regression enables the management of possible confounding factors and facilitates the identification of the distinct impact of extracurricular engagement on student outcomes (Lamborn et al., 2017).  An Analysis of Variance (ANOVA) will be performed to see if there are statistically significant disparities in academic performance and social skills among students depending on their involvement in different types of extracurricular activities, such as sports or arts. This test aims to compare the means of several groups to determine whether participating in certain activities results in improved outcomes (Eccles & Barber, 2003).  T-tests: Independent sample t-tests will be used to compare the means of academic achievement and social skills between students who engage in extracurricular activities and those who do not. This test will provide insights into whether engaging in extracurricular activities has a substantial impact on these outcomes.  The research utilizes inferential statistics to derive significant conclusions on the associations between extracurricular activities, academic achievement, and social skills. These findings may have implications for educational policy and practice in urban schools (Smith et al., 2019).

References

Creswell, J. W. (2018).  Research design: Qualitative, quantitative, and mixed methods approach (5th ed.). SAGE Publications.

Eccles, J. S., & Barber, B. L. (2003). Student council, volunteering, basketball, or marching band: What kind of extracurricular involvement matters?  Journal of Adolescent Research, 14(1), 10-43. https://doi.org/10.1177/0743558499141003

Fredricks, J. A., & Eccles, J. S. (2010). Participation in extracurricular activities in the middle school years: Are there developmental benefits for African American and European American youth?  Journal of Youth and Adolescence, 39(9), 1101-1114. https://doi.org/10.1007/s10964-010-9554-0

Lamborn, S. D., Brown, B. B., Mounts, N. S., & Steinberg, L. (2017). Putting school in perspective: The influence of family, peers, extracurricular participation, and part-time work on academic engagement.  Journal of Adolescent Research, 7(1), 10-32. https://doi.org/10.1177/0743554892721002

Smith, J. A., Flowers, P., & Larkin, M. (2019).  Interpretative phenomenological analysis: Theory, method and research. SAGE.

Trochim, W. M., & Donnelly, J. P. (2016).  Research methods knowledge base. Atomic Dog Publishing.

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