Teknik Analisis Data Metode Kuantitatif Lengkap SPSS #spss #kuantitatif #skripsi #ujinormalitas

Introduction

This tutorial provides a comprehensive guide to conducting quantitative data analysis using SPSS, based on the techniques presented by Uda Mamat. It covers essential statistical tests such as validity and reliability tests, classical assumption tests, and hypothesis testing. This guide is valuable for students and researchers looking to analyze data effectively for their theses or research projects.

Step 1: Conduct Validity Testing

Validity testing ensures that your measurement tools accurately measure what they intend to.

1. Prepare your data: Import your dataset into SPSS.
2. Use the following methods for validity testing:
   • Content Validity: Consult experts in your field to evaluate whether your items adequately cover the relevant content.
   • Construct Validity: Perform factor analysis to determine if the items group together as expected.
3. Analyze results: Review the output for correlation coefficients and factor loadings to confirm validity.

Step 2: Perform Reliability Testing

Reliability testing assesses the consistency of your measurement tools.

1. Open your dataset in SPSS.
2. Run Cronbach’s Alpha:
   • Go to Analyze > Scale > Reliability Analysis.
   • Select the items you want to test and move them to the “Items” box.
   • Click Statistics and check “Scale if item deleted”.
   • Click OK to generate the report.
3. Interpret results: A Cronbach’s Alpha value above 0.70 generally indicates acceptable reliability.

Step 3: Conduct Classical Assumption Testing

This step verifies the assumptions of linear regression.

Step 3a: Test for Normality

1. Use the Shapiro-Wilk test:
   • Go to Analyze > Descriptive Statistics > Explore.
   • Move your dependent variable to the "Dependent List".
   • Click Plots, select “Normality plots with tests”, and click Continue.
   • Click OK.
2. Review output: Check the p-value; a value below 0.05 indicates that the data is not normally distributed.

Step 3b: Test for Heteroskedasticity

1. Visual inspection: Create a scatter plot of the residuals.
   • Go to Graphs > Chart Builder.
   • Choose a scatter plot and plot residuals against predicted values.
2. Check for patterns: Look for any funnel shapes in the scatter plot, indicating heteroskedasticity.

Step 3c: Test for Multicollinearity

1. Run a correlation matrix:
   • Go to Analyze > Correlate > Bivariate.
   • Select independent variables and review the correlation coefficients.
2. Check VIF:
   • In your regression analysis, observe the Variance Inflation Factor (VIF) values.
   • A VIF above 10 suggests multicollinearity issues.

Step 4: Perform Hypothesis Testing

This step tests your research hypotheses.

Step 4a: Conduct Determination Coefficient Test

1. Run a regression analysis:
   • Go to Analyze > Regression > Linear.
   • Set your dependent variable and independent variables.
   • Check the output for the R-squared value to determine the explanatory power of your model.

Step 4b: Conduct Multiple Linear Regression

1. Follow the same procedure as above for regression analysis.
2. Interpret coefficients: Examine the regression coefficients for significance and effect size.

Step 4c: Conduct F-test

1. Use the output from your regression analysis:
   • Find the F-statistic and its significance level.
2. Interpret results: A significant F-test indicates that at least one predictor variable is statistically significant.

Step 4d: Conduct T-test

1. Examine the coefficients table from your regression output:
   • Look for the t-statistics and their significance levels for each independent variable.
2. Determine significance: A t-test value with a p-value below 0.05 shows a significant relationship.

Conclusion

This tutorial outlined key steps for conducting quantitative data analysis using SPSS, including validity and reliability testing, classical assumption tests, and hypothesis testing. By following these steps, you can ensure that your analysis is robust and reliable. As a next step, consider applying these techniques to your own data and interpreting the results to draw meaningful conclusions. For further learning, refer to the provided SPSS module link for additional resources.