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Developed by SPSS Inc. and acquired by IBM in 2009, the Statistical Package for the Social Sciences is a software package used in statistical analysis of data. Originally developed for the social sciences field, SPSS became popular and used in other fields such as health and sciences, marketing, market research and data mining.
In SPSS you begin by defining a set of variables and then you enter data for the variables to create a number of cases. Each variable is defined as containing a specific kind of number. After you enter your data into SPSS your cases are all defined by value stored in the variables and you can run an analysis. SPSS software works its best to keep you from running into the ditch. To foul things up, you have to work at figuring out a way of doing something wrong.
SPSS can be used in various other operating systems; windows platform is popular among others. SPSS is a window based program that can be used to perform data entry and analysis and to create tables and graphs. Apart from these, SPSS is capable to handle huge amounts of data and can perform all the analysis covered in the text and much more.
Simply data that comes as a file is raw data. The data could be from online depositories or from CDROMS. A normal pre installed notepad cannot read the raw data well and it is wise to not to try open large file in notepad. In such scenario SPSS software can be helpful in reading such raw data from excel, notepad, R. Stata and databases.
Univariate analysis is used to only look at one variable to see if anything is going with that variable. Univariate analysis looks at basic, descriptive, measures of central tendency: mean, median, mode, Measures of dispersion, range, standard deviation and variance.
Transforming the data and the conditional computes: the if command lesson cover topics such as: computing new variables, redefining or reorganization of existing data, Filtering the data, Weighing cases, sorting, Replacing the missing values and using subsets of variables.
SPSS software allows it user to draw a sample or sub sample of desire form a data set. The sample drawn out can be targeted sample or random sample. Targeted sample selection is used when an researcher is not interested in all of the cases of the data file. And thus wants to draw a sample out particular sample from the data of his/her desire. Similarly when a researcher need just an ordinary sample from the data file, random data selection is in use.
This lesson will be introducing SPSS numerical and Graphical Summaries of data Histogram, Box-plots, Bar-charts, Scatter Plots, Stem and leaf etc. And also helps in producing such histograms and bar-charts based on the samples of the data.
Chi-squared test is any statistical hypothesis test in which the sampling distribution of test is a chi-square distribution when the null hypothesis is true. It is test is used to determine whether there is a significant difference between the expected frequencies and the observed frequencies in one or more categories. Chi-squared test are often constructed from a sum of squared errors or through the sample variance.
There are various examples of Chi-squared test. Some of them are: Pearson’s Chi-squared test, Yates’s Correction for Community etc.
Two way analyses is an extension of one way variance (ANOVA) that examines the influence of two different categorical independent variables on one continuous dependent variable. It not only accesses the main effect of each independent variable but also sees if there is any interaction between them.
This lesson cover the topics like simple linear regression:The case of one explanatory variable, types of relationships, Scatter plots: a type of plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data, Linear regression: an approach for modeling the relationships between a scalar dependent variable Y and one more explatory variables denoted X and lines of best fit.
This lesson will be guiding students through different concepts of about correlation and multiple regressions: a multivariate statistical technique for examining the linear correlation between two or more independent variables and a single dependent variable and Enter/step wise method.
Developed by statistian David Cox, Logistic regression is a regression model where the dependent variable is categorical. Similarly logistic regression hardly makes key assumptions of linear ad general linear models. Apart from these concepts lesson 12 deals with strength of relationships, binary logistic regression which requires the dependent variable to be ordinal and multi-nominal logistic.
Factor analysis assumptions, Factors involved in factor analysis, procedure of factor analysis are what this lesson is all about. Here, factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors.