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How to Interpret the Output of Correlation Analysis | Hypothesis Testing, Sign, Size, and Direction
Correlation analysis is an associative test commonly used by many researchers to understand the relationship between variables. Before discussing how to interpret the results of correlation analysis, it is essential to understand the basic theory of correlation analysis.
How to Choose Regression, Correlation, or Difference Test for Variable Association Analysis
Selecting the appropriate analysis method will prevent errors in concluding research results. There are various methods of data analysis that researchers can choose from. The selection of data analysis methods depends on the research objectives and the characteristics of the collected data.
How to Determine Correlation Analysis for Nonparametric Variables
Correlation analysis aims to determine the relationship between variables. Partial correlation analysis is the analysis most often used by researchers.
How to Distinguish Cross-Section Data, Time Series Data, and Panel Data
Based on the collection method, data can be divided into cross-section, time series, and panel data. A good understanding of the differences between the three types and how to collect the three types of data will lead to the right choice of analysis.
Things to consider if none of the variables has a significant effect (null hypothesis accepted)
For researchers, obtaining statistically significant results is the desired outcome. In a research proposal, researchers write the background and research problem. Futhermore, based on the research problem, the research objectives are formulated.
How to Interpret Negative Coefficients of Linear Regression Output
Regression analysis is commonly used by researchers to analyze influence of independent variables on a dependent variable. But what if the estimated regression coefficient turns out to be negative and its influence is significant? In this article, we will explore and review the based on a case study where the estimation coefficient is negative and how to interpret it in an academic context.
Interpreting the estimation coefficients of dummy variables in linear regression analysis
Linear regression analysis using the Ordinary Least Squares (OLS) method is the most commonly used technique for examining the influence of one variable on another. There are certain assumptions that need to be met when employing linear regression. Assumption testing is necessary to ensure consistent and unbiased estimation results.
Differences in the use of paired sample t-test and independent sample t-test
The t-test is commonly used to test the mean difference between two samples. The t-test can be performed on paired samples as well as independent samples. When should we use the paired sample t-test and when should we use the independent sample t-test?