Help to Run and Report Multiple Regression in SPSS

Multiple regression analysis is used when a researcher wants to predict a dependent variable's value based on the independent variables' values. It is a statistical technique used to evaluate the relationship between a single dependent variable and two or more independent variables.

Being an extension of simple linear regression, multiple regression analysis aims at using known values of predictor variables to predict the outcome variable value. Multiple regression, also, known as multiple linear regression allows researchers to analyze more than one independent variable. Our company provides data analysis services using multiple regression to help researchers, scholars, and students determine the outcome variable, given a set of the predictor variable(s) in a data set.

Before conducting regression analysis using SPSS statistics, it is fundamental to first understand the regression equations and the various assumption test that the data set must pass. This article contains an enhanced multiple-regression guide to use when conducting multiple linear regression analysis in SPSS.

Benefits of SPSS Multiple Regression Analysis in Research

Some benefits of multiple regression analysis in SPSS include:

• In a regression model, there is no dependency on one variable; the multiple independent variables increase the reliability of a research report.
• It is possible to evaluate whether dependent variables depend on the predictor variables to understand the relationship between more than two variables in a data set.
• One can interpret regression equations and the impact of each independent variable on the dependent variable.
• The researcher can study more possible formulated hypotheses with multiple regressions.

Assumptions for Multiple Linear Regression Analysis Using SPSS

Before choosing to conduct multiple regression using SPSS, one must be sure that the data in question meets the assumption tests for multiple regression analysis. Whether the data set meets the assumptions tests determine the validity and reliability of the multiple regression analysis results. The assumptions include:

• The scale of measurement for the dependent variable is continuous.
• There are two or more independent variables that are either continuous or categorical.
• The observations/residuals are independent, which can be determined using the Durbin-Watson statistic in SPSS.
• There is a linear relationship between the dependent variable and each of the independent variables and between the target/dependent variable and all the independent variables conjointly. If the variables display non-linear relationships, one can transform the data or use non-linear regression analysis.
• The variances should remain similar along the best-fit regression line.
• There must not be multicollinearity in the data set.
• There are no significant outliers, highly influential points, or points of high leverage in the data.
• The residuals are approximately normally distributed, which can be detected using a histogram, a normal p-p plot, and a normal Q-Q plot.

In our help with multiple linear regression in SPSS, we assist clients to:

• Understand the variables in their data sets and the regression equation.
• Conduct and interpret the Durbin-Watson statistic to determine the independence of observations.
• Know how to create scatterplots and partial regression plots, interpret the results, and advise on what to do if there are no linear relationships between the variables.
• Understand the factors to consider to correctly interpret regression equations and analysis results, and read the multiple regression model summary table correctly.
• Use SPSS statistics to perform collinearity diagnostics and interpret the regression coefficients and tolerance values.

In case the data does not meet the assumption tests, we can help find possible solutions to continue with the multiple regression analysis in SPSS. Our experts have extensive experience providing professional help to run and report regression analysis results. We can help interpret multiple regression results at any time a client may be in need.

How to Analyze Data Using Multiple Regression in SPSS When no Assumption is Violated

When none of the eight assumptions tests is violated, data analysis using multiple regression in SPSS follows the steps below.

1. On the main menu, click Analyze>Regression>Linear.
2. On the linear regression dialogue box, transfer the dependent variable into the dependent box and the independent variables into the independent(s) box.
3. Click on the statistics box for the Linear Regression: statistics box to appear.
4. Select confidence intervals in the regression coefficient area and leave the level% option at 95.
5. Click on the continue button to return to the regression analysis dialogue box.
6. Click on the ok button to generate the multiple regression output.

Our experts are proficient and committed to providing professional data analysis services using multiple regression in SPSS to ensure the client receives valid results that are statistically significant. Contact us at any time, 24/7 for help with multiple linear regression analysis in SPSS for your research project, dissertation, thesis, and any other relevant assignment.

How to Interpret and Report Multiple Regression Analysis Output

For multiple linear regression analysis, the SPSS software generates several output tables. Knowing which tables are relevant for understanding the results if the data set complies with all the assumptions is fundamental.

The tables help in determining whether the multiple regression model fits the data. We help interpret multiple regression analysis results in case one is not able to read and interpret the output. The most relevant tables in understanding multiple regression analysis in SPSS results include:

• The model summary table

The table shows the R, R squared, adjusted R square, and the standard error. These values can be used to determine if a regression model fits the given data. In the table, the R column represents the multiple correlation coefficient, a single measure of the quality of the prediction of the dependent variable.

The R square column represents the coefficient of determination: the proportion of variance in the dependent variable the independent variables can account for. To correctly report data, one must be in a position to interpret the adjusted R square value.

• The ANOVA table

The F-ratio in the ANOVA table is used to determine whether the overall regression model is the best fit for the data set. The corresponding p-value and the confidence intervals tell whether there is statistical significance in the independent variables' prediction of the dependent variable.

• Estimated model coefficients table

In the estimated model coefficients table, unstandardized coefficients indicate the extent of variation in the dependent variable with an independent variable when all other independent variables are constant.

This guide helps understand how to run and report multiple regression in SPSS. In case of any difficulties reporting multiple regression results, contact us for the most reliable professional assistance by experienced SPSS experts.