Logistic regression is a form of predictive analysis performed when the dependent variable is binary (dichotomous). SPSS is one of the programs used to conduct logistic regression. With this tool, data analysts can identify and explain the relationship between dependent variables and ordinal, nominal, ratio-level, or interval independent variables.
Survival analysis is a statistical technique used to study the duration a given event takes to occur. Using SPSS, researchers can employ survival analysis to investigate events or answer questions such as what percentage of a population will survive past a given time, what rate this percentage will die at, what circumstances increase or decrease the chances of survival, etc. The ability to perform survival analysis has seen SPSS grow in popularity in various disciplines including healthcare, sociology, and engineering.
Cluster & factor analysis
Cluster and factor analyses are some of the most important statistical techniques performed using SPSS. The sole purpose of cluster analysis is to deal with, or rather, address heterogeneity in a data set, while that of factor analysis is to relate variables to each other and describe correlation in data sets. Basically, cluster analysis is used to categorize data and factor analysis is used to simplify it.
Multivariate Analysis of Variance or simply MANOVA is an analysis of variance with multiple dependent variables. It differs from ANOVA in that the latter measures only one dependent variable. In SPSS, like in most statistical data analysis tools, MANOVA is used to calculate the difference between multiple vectors of means. It extends the capabilities of ANOVA by analyzing multiple dependent variables simultaneously.
A repeated-measures ANOVA is used to compare multiple groups of means where the variables being observed are the same in every group. It resembles the one-way ANOVA, only that the repeated measures ANOVA deals with independent groups while the former deals with related groups. SPSS enables researchers to compare means across multiple variables that have been put together after repeated observations.
Charts & pivot tables
A chart is simply a graphical representation of data in which information is displayed in the form of symbols such as slices, bars, lines, etc. A pivot table is a technique of data processing used to summarize, reorganize, sort, count, or group data stored in a table. Both charts and pivot tables are used in data analysis to make understanding information much easier. SPSS adds titles, footnotes, and captions to charts and pivot tables to make the display even more appealing and data much easier to comprehend.
Nearest neighbor analysis
The Nearest Neighbor Analysis (NNA) is the measure of the distribution or spread of a population over a given geographical area. It offers a numerical value that identifies and explains the extent to which points in population are spaced. Researchers use SPSS to perform NAA to find out whether the frequency with which a population is spatially observed is comparable with other geographical areas. It provides a numerical value for clustering a geographical phenomenon, enabling the value to be compared with other areas more accurately and effectively.
Discriminant analysis is a statistical method used to explore and analyze research data when the dependent (criterion) variable is categorical and the independent (predictor) variable is an interval. SPSS is used to create discriminant functions that are linear combinations of the independent variables enabling proper and accurate discrimination between the various categories of the dependent variable. It helps researchers to study the differences between predictor variable groups and examine the accuracy of the classification of these groups.
Decision trees are charts or diagrams that data analysts use to illustrate a statistical probability or determine a course of action. Every branch on the tree represents a possible reaction, decision, or outcome, with the furthest branches representing the results. Decision trees in SPSS enable researchers to discover groups of data, identify relationships between them, and forecast future events.
Generalized linear models
Generalized linear models are flexible statistical models consisting of continuous (categorical) independent variables and normally distributed dependent variables. They incorporate three components; a random component, a link function, and a systematic component. Generalized linear models in SPSS allow users to use syntax and dialog boxes to specify linear models and display the results in charts and pivot tables so they can easily edit and modify the output.
Linear mixed models
Linear Mixed Models (LMM) are a special type of simple linear models that allow data analysts to implement both random and fixed effects. They are especially used when data is not independent, a condition that sometimes arises from hierarchical structures. The LMM procedure in SPSS allows users to fit linear mixed models to data and information sampled from a normal distribution. This procedure fits models more generally than procedures performed in generalized linear models and includes all the models contained in the variance component procedure.
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