Types of Data Analysis Using IBM SPSS


SPSS is a statistical program used for data analysis in various disciplines. While initially developed for sociological and psychological research, its usage has since expanded into business use as well as other fields that collect data collection.

A typical SPSS file typically comprises of a table with rows representing cases and columns representing measurements. There are two primary ways of working with SPSS: Data View and Variable View.

Statistical Analysis

SPSS can be used for a wide variety of statistical analysis. While its main use lies within social sciences such as psychology, data mining can also be conducted and predictions made regarding future trends based on past patterns. SPSS comes equipped with tools for data visualization such as histograms and scatterplots that help identify any outliers or unusual patterns within datasets; and can perform inferential statistics like t-tests and ANOVA analyses.

SPSS can be an indispensable resource for performing descriptive statistics, which summarize and describe key features of a dataset. Descriptive statistics can be used to identify any strange patterns or outliers, help select analytical techniques, design questionnaires and other data collection instruments as well as inform questionnaire design decisions.

Inferential statistics such as t-tests, ANOVA and regression can be used to examine relationships among variables in data. This form of analysis allows one to detect significant variations and draw inferences about causality (whether one factor had direct influence over another variable).

Software like Excel allows for the generation of reports and visual graphs that can help present results of an analysis and communicate findings to others. A histogram displays data values while scatterplot compares relationships between two variables.

Also, this software can produce codebooks which document variables within a dataset, particularly helpful when working with large datasets where memorizing all properties can be challenging.

SPSS utilizes its own file format as its output format; however, it can also export tab-delimited text, HTML pages, PDF documents and Excel spreadsheets for export. SPSS also integrates data from other statistical packages and databases via ODBC/SQL connections for data import and export purposes.

Organizing Data

SPSS can accommodate various data formats. It can import spreadsheets from Microsoft Excel and plain text files as well as statistical databases like MySQL or SQL. Furthermore, SPSS features various data manipulation functions – adding new variables, restructuring data structures, detecting anomalies in observations as well as numeric functions that allow for complex analyses to be created quickly and efficiently.

SPSS makes data organization simple and straightforward with two main windows designed specifically to assist. The Data View allows for data input while Variable View displays metadata associated with it – these look much like spreadsheets with numbers running vertically down each column and variable names listed horizontally along one axis. Information in Variable View automatically updates whenever changes are made to Data View spreadsheet.

Establishing information about your variables is essential when organizing data in SPSS, allowing you to add labels that make the output easier to interpret and also enable SPSS to process analyses correctly, which may prove invaluable in certain circumstances.

When sorting multiple variables at once, selecting them from the left side of the dialog box and then choosing either “Sort Ascending” or “Sort Descending.” The first option will move smaller records closer to the top while breaking any ties in each group; to save your sorted data just select the check box at the bottom.

When finished with your work, to ensure that you always work with the most up-to-date data, choose “Save file with sorted data”. This will save your sorted data into a new dataset file for further analyses.


No matter if it involves surveys, medical records or other forms of data collection, you’ll want your findings presented clearly and understandably so your colleagues and external clients can better comprehend them and make better decisions based on them. IBM SPSS Survey Reporter makes creating professional interactive reports easy – ideal for distribution to multiple parties at the same time!

SPSS not only produces tables of sums and averages, but can also generate charts to visualize trends or patterns in your data. You can use different formats such as HTML or MS Word to present the results of your analysis – all tables and charts shown by SPSS output viewer can be exported at once to make one WORD document with explanatory text and titles between each.

Regression analyses are a popular technique used to establish cause-and-effect relationships, such as adjusting scores from various schools for effects such as extra tuition or wealthier backgrounds. You can then compare the adjusted scores between schools in order to see which factors had the biggest influence on any particular child, or predict future results using this same methodology.

When you’re ready to present your results, the table display window provides two methods of presentation – a spreadsheet for showing data and an Excel-style grid for showing statistical findings. Both will automatically sync together so any adjustments made in Data View will show up immediately in Variable View sheet.

The Data View window includes tabs for displaying frequencies and descriptive statistics for your variables, with options for sorting and filtering to make the data easier to interpret. In addition, its spreadsheet offers you flexibility in choosing whether to present tables of proportional responses or mean/standard deviation data depending on which you think will best serve your audience.

Create an explanatory text file of your data or a list of variables from your dataset for those without access to SPSS. The SPSS_sav family of file formats can be opened with any text editor that supports character encoding used in its header and dictionary sections, but is nontransparent as all numeric values are stored internally with full precision preserved.

Analysis of Trends

Time series analysis involves studying data that has been recorded over an extended period. It seeks to distinguish short term fluctuations from longer term tendencies, for instance by tracking trends such as sales movements of certain products over an extended period. Positive or negative movement tendencies can be identified by looking at how each series behaves over time.

There are various approaches to analysing time series data, with linear regression analysis being one of the more powerful techniques. This analysis attempts to fit a straight line through the centre of each point on a chart and predict future values – this technique can be used for many different kinds of behavior prediction; however it must adhere to certain assumptions and pass various tests provided on Output tab within Results section in order for it to be valid.

Multiplacative models provide another option for analyzing time series data, by seeking to ‘neutralise’ any residual or cyclical fluctuation by estimating smoothing values for every point in the dataset. This technique is usually considered more reliable.

Noting the assumptions made by additive and multiplacative models should also be kept in mind when considering additive and multiplacative models, including both compound symmetric covariance structures such as SAS proc glm and SPSS glm tools, with regards to covariance structures between data points in their datasets. SPSS mixed has an advantage over these tools in accepting various covariance structures as input parameters.

Plotting data gives a quick snapshot of its covariance. A series of observations over time provides the viewer with a visual display that clearly shows their overall trends as well as any anomalies that require further investigation.

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