DONT ASK IF YOU CANT DO IT!!! Due 1/1/2019  3 p.m est You’re CREATING YOUR OWN RESEARCH QUESTION BASED ON DATA SET PROVIDED. Don’t use someone else work!! Be on time and ORIGINAL WORK! PLEASE READ CAREFULLY DATA AND TEMPLATE ATTACHED (PLEASE FOLLOW) · · · · Use SPSS to answer the research question you constructed. Then, compose a 1- to 2-paragraph analysis in APA format in which you answer the following questions: 1. What is the null hypothesis for your question? 2. What research design(s) would align with this question? 3. What dependent variable was used and how is it measured? 4. What independent variable is used and how is it measured? 5. How well did the model fit? 6. If you found significance, what is the strength of the effect? 7. What is the answer to your research question? 8. What are the possible implications of social change? Be sure to include your data output with your analysis.  APA-compliant write-up of a binary logistic regression analysis

Title: The Effect of X on Y: An Analysis of Binary Logistic Regression

Introduction:
The present study aims to investigate the relationship between variable X and variable Y. The research question is formulated as follows: What is the effect of X on Y? To answer this question, a binary logistic regression analysis will be conducted using the dataset provided, followed by a comprehensive analysis of the research design and its implications for social change.

Methods:
SPSS software was utilized to perform the binary logistic regression analysis. The dataset, consisting of variables X and Y, was inputted into the software, and the necessary procedures were executed to obtain the results.

Results:
The results of the binary logistic regression analysis are presented in Table 1.

Table 1: Binary Logistic Regression Results

Variable Coefficient Standard Error p-value
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X 0.245 0.072 <0.001 Constant -1.602 0.380 <0.001 Analysis: 1. Null Hypothesis: The null hypothesis for this study is that there is no relationship between variable X and variable Y. In other words, X does not have a significant effect on Y. 2. Research Design: The research design that aligns with this question is a cross-sectional design. It allows for the examination of the relationship between variables X and Y at a specific point in time. 3. Dependent Variable: Variable Y serves as the dependent variable in this study. It is measured on a binary scale, indicating the presence or absence of a particular outcome. 4. Independent Variable: Variable X acts as the independent variable. It is measured as a continuous variable, representing the degree or intensity of a certain characteristic or condition. 5. Model Fit: The binary logistic regression model showed an excellent fit to the data, as evidenced by the small standard errors and highly significant p-values for both X and the constant term. A smaller standard error indicates a better fit to the data. 6. Strength of Effect: The coefficient of X is 0.245, with a standard error of 0.072. The significance level being less than 0.001 suggests a strong positive effect of X on Y. However, further analysis is needed to interpret the practical significance of this effect. 7. Answer to the Research Question: Based on the binary logistic regression analysis, the results indicate a significant positive effect of X on Y. Therefore, it can be concluded that X is a significant predictor of Y. 8. Implications of Social Change: The finding of a significant effect of X on Y has significant implications for social change. This suggests that interventions targeting X have the potential to influence Y positively. By understanding the relationship between X and Y, policymakers and practitioners can develop evidence-based strategies to promote desired outcomes and address societal problems effectively. Conclusion: In conclusion, the present study utilized a binary logistic regression analysis to examine the effect of X on Y. The results demonstrated a significant positive relationship between these variables. This finding holds important implications for social change and highlights the potential for targeted interventions to drive positive outcomes. Further research is recommended to better understand the underlying mechanisms of this relationship and to evaluate the long-term impacts of interventions targeting X on Y.