SPSS is a statistical software package that is used to analyze the data (either in quantitative or qualitative form.) and it also helps to interpret the findings. SPSS stands for Statistical Packages for Social Science.
Introduction to SPSS Statistics
In 2009, SPSS was acquired by IBM. Now, the versions of SPSS are being named “IBM SPSS Statistics”, version 27.
SPSS software is used by insurance, banking, telecom, retail, consumer package Goods, market research, health research, survey companies, government (election, population, plan), education system and students researchers, finance, etc. to analyze data. SPSS is capable of analyzing a large amount of data and creating tables and graphs.
SPSS software is used for statistical tests because sometimes it is hard to deal with a large amount of data and perform different mathematical and statistical equations by hand. So, it is helpful for us, it also helps us to interpret the results, check normality, testing of hypotheses, computation of different averages, plot simple to complex graphs, and so on. SPSS offers a wide range of statistical methods. Some examples are:
1) Helps to define and show missing values in the data
2) Compute Descriptive Statistics such as Frequency Distribution
Analyze > Descriptive statistics > Frequency > statistics
On entered data, and for selected variables, one may get appropriate and required measures such as mean, sum, mode, percentiles, quartiles, variance, range, and other measures of dispersion, skewness, kurtosis, etc.
Statistical Techniques in SPSS
Descriptive Statistics
Different Statistics can be performed such as Cross Tabulation, Frequency, Descriptive, Explore, and Descriptive Ratio Statistics. All these options contain relevant statistical measures such as measures of central tendency, measures of dispersion, measures of position, measures for identification of shape of distribution, etc.
Inferential Statistics
Inferential statistics from basic to advanced can also be performed in SPSS Software.
Estimation: Confidence Interval (lower and upper limits) and point estimation (single value).
Hypothesis Testing:
Differences Between Groups: Independent Sample t-test, Paired Sample t-test, One-Way ANOVA, Two-Way ANOVA, Chi-Squared Test for Homogeneity, etc.
Correlation Association: Pearson’s Correlation, Spearman Correlation, Chi-Squared Test of Association, Fisher Exact Test of Independence, Odd Ratio, Relative Risk.
Regression Model and Prediction: Linear Regression models, such as Simple and Multiple Regression, Step-Wise Regression, Logistic Regression, Poisson Regression, etc.
Complex Sample and Testing: Compute Statistics and Standard Error by Complex Sample Design, Visualizes and Explores Complex Categorization, Imputes Missing Values through Statistical Algorithms.
Graphs and Data Visualizations: Line, Chart, Histogram, Bar Chart, Pie Chart, Scatter Plot, Box Plot, Area Chart, Q-Q Plot, Simple 3D Bar Chart, Population Pyramid, Frequency Polygon.
So we say that the SPSS software plays a significant role in the process of analyzing and interpreting the data with the help of statistical features and methods.
For different SPSS Software Tutorials, see the following links:
- https://en.wikipedia.org/wiki/SPSS
- https://liu.brooklyn.libguides.com/SPSS
- https://libguides.library.kent.edu/SPSS/GettingStarted
- https://www.spss-tutorials.com/basics/
- https://www.statisticshowto.com/probability-and-statistics/spss-tutorial-beginners/
