Multiple logistic regression is like simple logistic regression, except that there are two or more predictors. The predictors can be interval variables or dummy variables, but cannot be categorical variables. If you have categorical predictors, they should be coded into one or more dummy variables. We have only one variable in our data set that is coded 0 and 1, and that is female. The first variable listed after the logistic regression command is the outcome or dependent variable, and all of the rest of the variables are predictor or independent variables listed after the keyword with.
In our example, female will be the outcome variable, and read and write will be the predictor variables. These results show that both read and write are significant predictors of female. Discriminant analysis is used when you have one or more normally distributed interval independent variables and a categorical dependent variable. It is a multivariate technique that considers the latent dimensions in the independent variables for predicting group membership in the categorical dependent variable.
For example, using the hsb2 data file , say we wish to use read , write and math scores to predict the type of program a student belongs to prog. Clearly, the SPSS output for this procedure is quite lengthy, and it is beyond the scope of this page to explain all of it. However, the main point is that two canonical variables are identified by the analysis, the first of which seems to be more related to program type than the second.
For example, using the hsb2 data file , say we wish to examine the differences in read , write and math broken down by program type prog.
The students in the different programs differ in their joint distribution of read , write and math. Multivariate multiple regression is used when you have two or more dependent variables that are to be predicted from two or more independent variables.
In our example using the hsb2 data file , we will predict write and read from female , math , science and social studies socst scores.
These results show that all of the variables in the model have a statistically significant relationship with the joint distribution of write and read. Canonical correlation is a multivariate technique used to examine the relationship between two groups of variables.
For each set of variables, it creates latent variables and looks at the relationships among the latent variables. It assumes that all variables in the model are interval and normally distributed. SPSS requires that each of the two groups of variables be separated by the keyword with. There need not be an equal number of variables in the two groups before and after the with. The output above shows the linear combinations corresponding to the first canonical correlation.
At the bottom of the output are the two canonical correlations. These results indicate that the first canonical correlation is.
The F-test in this output tests the hypothesis that the first canonical correlation is equal to zero. However, the second canonical correlation of. Factor analysis is a form of exploratory multivariate analysis that is used to either reduce the number of variables in a model or to detect relationships among variables. All variables involved in the factor analysis need to be interval and are assumed to be normally distributed. The goal of the analysis is to try to identify factors which underlie the variables.
There may be fewer factors than variables, but there may not be more factors than variables. We will include subcommands for varimax rotation and a plot of the eigenvalues. We will use a principal components extraction and will retain two factors. Using these options will make our results compatible with those from SAS and Stata and are not necessarily the options that you will want to use. Communality which is the opposite of uniqueness is the proportion of variance of the variable i.
The scree plot may be useful in determining how many factors to retain. From the component matrix table, we can see that all five of the test scores load onto the first factor, while all five tend to load not so heavily on the second factor. The purpose of rotating the factors is to get the variables to load either very high or very low on each factor.
In this example, because all of the variables loaded onto factor 1 and not on factor 2, the rotation did not aid in the interpretation. Instead, it made the results even more difficult to interpret. Click here to report an error on this page or leave a comment. Your Name required. Your Email must be a valid email for us to receive the report! How to cite this page. About the hsb data file Most of the examples in this page will use a data file called hsb2, high school and beyond. One sample t-test A one sample t-test allows us to test whether a sample mean of a normally distributed interval variable significantly differs from a hypothesized value.
One sample median test A one sample median test allows us to test whether a sample median differs significantly from a hypothesized value. Binomial test A one sample binomial test allows us to test whether the proportion of successes on a two-level categorical dependent variable significantly differs from a hypothesized value. Chi-square goodness of fit A chi-square goodness of fit test allows us to test whether the observed proportions for a categorical variable differ from hypothesized proportions.
Two independent samples t-test An independent samples t-test is used when you want to compare the means of a normally distributed interval dependent variable for two independent groups. See also SPSS Learning Module: An overview of statistical tests in SPSS Wilcoxon-Mann-Whitney test The Wilcoxon-Mann-Whitney test is a non-parametric analog to the independent samples t-test and can be used when you do not assume that the dependent variable is a normally distributed interval variable you only assume that the variable is at least ordinal.
Chi-square test A chi-square test is used when you want to see if there is a relationship between two categorical variables. One-way ANOVA A one-way analysis of variance ANOVA is used when you have a categorical independent variable with two or more categories and a normally distributed interval dependent variable and you wish to test for differences in the means of the dependent variable broken down by the levels of the independent variable.
The command for this test would be: oneway write by prog. Paired t-test A paired samples t-test is used when you have two related observations i.
Wilcoxon signed rank sum test The Wilcoxon signed rank sum test is the non-parametric version of a paired samples t-test. One-way repeated measures ANOVA You would perform a one-way repeated measures analysis of variance if you had one categorical independent variable and a normally distributed interval dependent variable that was repeated at least twice for each subject. Ordered logistic regression Ordered logistic regression is used when the dependent variable is ordered, but not continuous.
See also Annotated output for logistic regression Correlation A correlation is useful when you want to see the relationship between two or more normally distributed interval variables. Missing Data in SPSS Simple linear regression Simple linear regression allows us to look at the linear relationship between one normally distributed interval predictor and one normally distributed interval outcome variable.
Simple logistic regression Logistic regression assumes that the outcome variable is binary i. Multiple regression Multiple regression is very similar to simple regression, except that in multiple regression you have more than one predictor variable in the equation. Multiple logistic regression Multiple logistic regression is like simple logistic regression, except that there are two or more predictors.
Canonical correlation Canonical correlation is a multivariate technique used to examine the relationship between two groups of variables. F Hypoth. Univariate F-tests with 2, D. About Us. Learn nQuery Resources Webinars.
All Rights Reserved. Measurement from Gaussian Population. Binomial Two Possible Outcomes. Survival Time. Describe one group. Mean, SD. Median, interquartile range. Kaplan Meier survival curve. Compare one group to a hypothetical value. One-sample t-test. Wilcoxon test. Compare two unpaired groups.
Unpaired t test. Mann-Whitney test. Fisher's test chi-square for large samples. Compare two paired groups. Paired t test. McNemar's test. Compare three or more unmatched groups. Kruskal-Wallis test. Chi-square test. Compare three or more matched groups. Friedman test. Quantify association between two variables. Pearson correlation. Spearman correlation. Predict value from another measured variable.
Simple linear regression or Nonlinear regression. Predict value from several measured or binomial variables. Cox proportional hazard. Exact test for goodness-of-fit. Chi-square test of goodness-of-fit. G —test of goodness-of-fit. Statistical tests commonly assume that:. If your data does not meet these assumptions you might still be able to use a nonparametric statistical test , which have fewer requirements but also make weaker inferences.
A test statistic is a number calculated by a statistical test. It describes how far your observed data is from the null hypothesis of no relationship between variables or no difference among sample groups. The test statistic tells you how different two or more groups are from the overall population mean , or how different a linear slope is from the slope predicted by a null hypothesis. Different test statistics are used in different statistical tests. Statistical significance is a term used by researchers to state that it is unlikely their observations could have occurred under the null hypothesis of a statistical test.
Significance is usually denoted by a p -value , or probability value. Statistical significance is arbitrary — it depends on the threshold, or alpha value, chosen by the researcher. When the p -value falls below the chosen alpha value, then we say the result of the test is statistically significant. Quantitative variables are any variables where the data represent amounts e.
Categorical variables are any variables where the data represent groups. This includes rankings e. You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results.
Discrete and continuous variables are two types of quantitative variables :. Have a language expert improve your writing. Check your paper for plagiarism in 10 minutes. Do the check. Generate your APA citations for free! APA Citation Generator. Home Knowledge Base Statistics Statistical tests: which one should you use? Statistical tests: which one should you use? They can be used to: determine whether a predictor variable has a statistically significant relationship with an outcome variable.
Statistical tests flowchart Table of contents What does a statistical test do? Receive feedback on language, structure and layout Professional editors proofread and edit your paper by focusing on: Academic style Vague sentences Grammar Style consistency See an example.
What are the main assumptions of statistical tests? Statistical tests commonly assume that: the data are normally distributed the groups that are being compared have similar variance the data are independent If your data does not meet these assumptions you might still be able to use a nonparametric statistical test , which have fewer requirements but also make weaker inferences.
What is a test statistic? What is statistical significance?
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