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A specified set of commands in SPSS enables you to read simple and complex data in various formats. The Getting Data commands will make it possible for a user to read data in SAS datasets, various database tables, several spreadsheets like Excel, and others.
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| Command Name | Function |
| Get Data | This command reads all types of Text and Excel data files as well as Database Tables |
| Import | Reading all portable data files that are created using the Export command is possible with Import |
| File Type | File Type is the command that defines nested, grouped, and mixed data structures |
| Input Program | It reads all complex data files while also generating case data |
| Dataset Name | The Dataset Name command is responsible for having more than one dataset open in SPSS simultaneously |
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There is a dedicated group of SPSS commands responsible for Saving and Exporting various types of Data. They enable you to save data in Excel Spreadsheet, IBM SPSS Statistics, Database Table, and other supported formats.
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- XSave: Makes it possible to save data in the IBM SPSS Statistics format minus a separate data pass
- Save Data Collection: It saves both data and metadata files in the IBM SPSS Statistics and Data Collection MDD formats, respectively.
- Save Translate: This command has numerous functions, including saving data in Various spreadsheet formats, dBase, CSV, et cetera; and creating new database tables
- Export: This is the command responsible for Saving data in any portable format
- Write: It saves data in fixed format text
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Data Transformations SPSS commands help you perform such tasks as collapsing categories and creating new variables, among others. The list of commands in this group is a long one, and the good news is that we have experts who understand all of them, so you don't need to fret over any homework about Data Transformations.
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- Autorecode
This is the command that recodes string and numeric variable values to the consecutive integers.
- Create
It comes up with new series as a function of an already existing series.
- Rank
The Rank command comes up with new variables that contain normal scores, ranks, and Savage, and other related scores.
- RMV
It replaces all missing values with estimates that are computed by various methods.
- String
This is the command that declares all the new string variables.
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Data values aren't the only attributes in an IBM SPSS Statistics data file. There are several metadata variables like display formats, value labels, special missing-value codes, and measurement levels, and others. SPSS Data Definition commands are a group of commands that help create, modify, and remove database elements on the platform.
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- We have experts who can code in the Datafile Attribute command, which is responsible for creating user-defined variables.
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- Variable Width is the command that specifies the width of the column in the Data Editor.
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File Information commands enable you to incorporate descriptive information into a data file. They're also responsible for displaying both file and data attributes for any IBM SPSS Statistics data file or an active dataset. There aren't so many commands under this category.
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- Add Documents: The command creates a text of any length in the active dataset
- Compare Datasets: It compares contents of the active dataset to those of another dataset in the current session or an external one in the IBM SPSS Statistics.
- Drop Documents: This command deletes all the text created by Add Documents or Documents commands.
- Sysfile Info: It displays complete information for l IBM SPSS Statistics data file variables.
- Display: Displays all information obtained from the active dataset's dictionary.
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The Time Series group of commands in IBM SPSS implement various time series functionalities on the platform. Some of the most common functionalities include determining chart types, forecasting, and the activities performed in implementing decision tree functionalities.
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- ACF:-The ACF command has two functions; displaying and plotting the sample autocorrelation function of one or several time series.
- PACF :-PACF will display and plot all the sample partial autocorrelation functions of many times series
- Fit :-Fit displays several descriptive statistics calculated from the residual series. These statistics are used to evaluate the goodness of fit of various models.
- Predict :-Predict identifies and specifies the observations that signify the beginning and end of the period of forecasting.
- Verify :-This one generates a report on the condition of the most recent Date, Predict Specifications, and Use.
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