Posts filed under '3b. Heavy statistics with SPSS'
We get a lot of questions about regression analysis. We have dug into this and decided to write a post about it, so we can help everyone with this.
You do a regression when you assume that a variable is influencing another one, like in the following example: We assume that cars that run on Diesel have higher costs.
To test this assumption, we run a Linear Regression in SPSS. Take the following steps:
- Define your dependent and independent variable. In our example Fuel is the indepent variable and Costs is the dependent one.
- Click Analyze
- Go to Regression and click Linear
- Click “Fuel” into the Independent variable field, and “Costs” into the Dependent variable field.
The output exists of:
1 Model Summary, in which you can find the relation between the variables.
R stands for the correlation and gives us the relation between the dependent and the independent variables. The correlation between Fuel and Costs is ,839.
R Square is the proportion of variance in the dependent variable (Costs) which can be predicted from the independent variable (Fuel). This value indicates that 70% of the variance in costs can be predicted from the variable fuel. The Adjusted R-square tries to give an even better calculation for the whole population.
2 ANOVA, which holds data about the significance of the regressionmodel.
The value under Sig. holds the significance value of the regression. In most cases this should be under 0.05. In our example this is 0.00, better it cannot get!
3 Coefficients, gives information about the first line of regression.
Conclusion would be that this regression analysis is significant and that 70% of the variance in costs can be predicted from the variable fuel.
Please find below the SPSS file we used to create this example. Just one note, the information in the SPSS file is not based on anything. Even more, it’s just random data. Please don’t sue us.
Linear Regression Example Cars
August 21st, 2006
Many visitors of ourÂ blog are searching for information about the one sample t-test.
You perform a one-sample t-test when you want to determine if the mean value of a target variable is different from a hypothesized value.
To perform a one-sample t-test in SPSS. Choose Analyze>Compare Means>One-sample t-test.
Move the variable of interest to the Test variable(s) box. Change the test value to the hypothesized value. Click the OK button.
The output from this analysis will contain the following sections.
One-Sample Statistics. Provides the sample size, mean, standard deviation, and standard error of the mean for the target variable.
One-Sample Test. Provides the results of a t-test comparing the mean of the targetvariable to the hypothesized value.
A significant test statistic indicates that the sample mean differs from the hypothesized value. This section also contains the upper and lower bounds for a 95% confidence interval around the sample mean.
Do you have an question about the one-sample t-test, submit your question here.
April 26th, 2006
Ivy sent us an e-mail about investigating the interaction effect of independent variables. MANOVA (multivariate analysis of variance) is a statistical procedure that allows you to determine if a set of categorical predictor variables can explain the variability in a set of continuous response variables.
In SPSS you can perform a MANOVA as follows:
- Choose Analyze -> General Linear Model -> Multivariate.
- Move the DVs (dependent variables) you want to examine to the Dependent Variables box.
- Move any categorical IVs (independent variables) to the Fixed Factor(s) box.
- Move any continuous IVs to the Covariate(s) box.
- Click OK and there you have your output.
If you have any more questions about MANOVA or ANOVA, submit your questions!
April 25th, 2006
Zakya asked us another question. After getting his Excel data into SPSS, he wanted to find possible correlations between a couple of variables. Zakya, please find below an explanation on finding correlations using the Pearson correlation analysis.
The Pearson correlation analysis test can be used to find correlations between responses of nominal variables.
A correlation analysis is performed to quantify the strength of association between two numeric variables. In the following task we will perform Pearson correlation analysis. The variables used in the analysis are chicken, car, house, and job.
Select Analyze>Correlate>Bivariate. This opens the Bivariate Correlations dialog box. The numeric variables in your data file appear on the source list on the left side of the screen.
Select chicken, car, house, and job from the list and click the arrow box. The variables will be pasted into the selection box. The options Pearson and Two-tailed are selected by default.
A symmetric matrix with Pearson correlation as given below will be displayed on the screen. Along with Pearson r, the number of cases and probability values are also displayed
This is the main matrix of the Pearson’s output. Variables have been arranged in a matrix such that where their columns/rows intersect there are numbers that tell about the statistical interaction between the variables. Three pieces of information are provided in each cell — the Pearson correlation, the significance, and number of cases. When a variable interacts with itself, the correlation will obviously be 1.00. No significance is given in these cases.
Notice that the .775 has asterisks by it. As is indicated at the bottom of the output this is how SPSS indicates significant interactions for you. Notice the significance is under 0.05 (.041).
April 11th, 2006
This week Nouredine sent us the following question:
“I would like to receive some information and instructions on how i can
conduct an analyse on SPSS with an output that is a priority matrix (
relevance vs score).”
A priority matrix, also called “Quadrant analysis” or “Importance-Performance Analysis (IPA)”, is a comparison chart which shows the importance ranking and the mean satisfaction of a number of elements. In SPSS there is no graph that has the same name, but you can get this kind of matrix by creating a Scatter plot. In our previous post your find a link to a video instruction on how to make a scatter plot.
April 5th, 2006