A correlation indicates the size and direction of any relationship between variables. If, however, your hypothesis involves prediction such as variables "A", "B", and "C" predict variable "D"then a regression is the statistic you will use in your analysis. If you have only one independent variable and one dependent variable, you would use a bivariate linear regression the straight line that best fits your data on a scatterplot for your analysis. Types of Regression Analysis There are several types of regression analysis -- simple, hierarchical, and stepwise -- and the one you choose will depend on the variables in your research.
It is used when we want to predict the value of a variable based on the value of another variable.
Maxwell, Kori Lloyd Hugh, "Logistic Regression Analysis to Determine the Significant Factors Associated with Substance Abuse in School-Aged Children." Thesis, Georgia State University, Linear Regression Analysis on Net Income of an Agrochemical Company in Thailand.! 10!! 3.) Stepwise Regression: It is a method that allows moves in either direction, dropping or adding variables at the various steps. It combines both forward selection and backward elimination. We perform two steps in forward selection and a backward step. Multiple Regression in Dissertation & Thesis Research For your dissertation or thesis, you might want to see if your variables are related, or correlated.
The variable we want to predict is called the dependent variable or sometimes, the outcome variable. The variable we are using to predict the other variable's value is called the independent variable or sometimes, the predictor variable.
For example, you could use linear regression to understand whether exam performance can be predicted based on revision time; whether cigarette consumption can be predicted based on smoking duration; and so forth. If you have two or more independent variables, rather than just one, you need to use multiple regression.
This "quick start" guide shows you how to carry out linear regression using SPSS Statistics, as well as interpret and report the results from this test. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for linear regression to give you a valid result.
We discuss these assumptions next.
SPSS Statistics Assumptions When you choose to analyse your data using linear regression, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using linear regression.
You need to do this because it is only appropriate to use linear regression if your data "passes" six assumptions that are required for linear regression to give you a valid result. In practice, checking for these six assumptions just adds a little bit more time to your analysis, requiring you to click a few more buttons in SPSS Statistics when performing your analysis, as well as think a little bit more about your data, but it is not a difficult task.
Before we introduce you to these six assumptions, do not be surprised if, when analysing your own data using SPSS Statistics, one or more of these assumptions is violated i. This is not uncommon when working with real-world data rather than textbook examples, which often only show you how to carry out linear regression when everything goes well!
Even when your data fails certain assumptions, there is often a solution to overcome this. Your two variables should be measured at the continuous level i.
Examples of continuous variables include revision time measured in hoursintelligence measured using IQ scoreexam performance measured from 0 toweight measured in kgand so forth. You can learn more about interval and ratio variables in our article: There needs to be a linear relationship between the two variables.
Whilst there are a number of ways to check whether a linear relationship exists between your two variables, we suggest creating a scatterplot using SPSS Statistics where you can plot the dependent variable against your independent variable and then visually inspect the scatterplot to check for linearity.
Your scatterplot may look something like one of the following: If the relationship displayed in your scatterplot is not linear, you will have to either run a non-linear regression analysis, perform a polynomial regression or "transform" your data, which you can do using SPSS Statistics.
In our enhanced guides, we show you how to: There should be no significant outliers. An outlier is an observed data point that has a dependent variable value that is very different to the value predicted by the regression equation.
As such, an outlier will be a point on a scatterplot that is vertically far away from the regression line indicating that it has a large residual, as highlighted below: The problem with outliers is that they can have a negative effect on the regression analysis e.Linear Regression Analysis using SPSS Statistics Introduction.
Linear regression is the next step up after correlation. It is used when we want to predict the value of a .
Paul D. Allison is Professor of Sociology at the University of Pennsylvania and President of Statistical Horizons LLC. He is the author of Logistic Regression Using SAS: Theory and Application, Survival Analysis Using SAS: A Practical Guide, and Fixed Effects Regression Methods for .
Statistical Regression Analysis: The Fundamentals By admin on January 5, in Data Analysis In the field of research, Regression is a generic term that is used commonly for all the methods that strive towards the quantification of the relationship between two groups of variables.
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It stars Ana Torrent, Fele Martínez and Eduardo Noriega. Maxwell, Kori Lloyd Hugh, "Logistic Regression Analysis to Determine the Significant Factors Associated with Substance Abuse in School-Aged Children." Thesis, Georgia State University, For an analysis using step-wise regression, the order in which you enter your predictor variables is a statistical decision, not a theory on which your dissertation is based.
To determine which of these regressions you should use to analyze your data, you must look to the underlying question or theory on which your dissertation or thesis is based.