Data Analytics Using SPSSqtanalytics.in/wp-content/uploads/2019/12/Learning-SPSS.pdf ·...

Data Analytics Using SPSS

Prabhat Mittal

University of Delhi

Introduction Getting Started with SPSS Data Analysis Tools

Table of contents I

1 IntroductionAbout Data AnalyticsAbout SPSSMajor Achievements of SPSSProducts and ServicesCharacteristics of SPSSHelp FacilityWhy SPSS?

2 Getting Started with SPSSIntroductionEntering DataExercise: Inserting Primary DataExercise: Inserting Secondary Data

3 Data Analysis ToolsStatistical Procedures in SPSS

Prabhat Mittal Data Analytics Using SPSS 2 / 33


IntroductionData Analytics



IntroductionAbout SPSS

SPSS originally stood for ”Statistical Package for the Social Sci-ences,” is a software package for statistical analysis. Long producedby SPSS Inc, it was acquired by IBM in 2009 and officially namedIBM SPSS statistics.

Originally, it was focused on analyzing data about people opinion,attitude and behavior, now is popular in other fields as well, includ-ing the health sciences and marketing.

Presently, SPSS ranked second in advanced and predictive analyticsindustry after SAS, its market share (16.1 %) is only about half ofSAS (30.5 %).(International data Corporation IDC, 2016).

Office Information: World HeadquartersIBM CorporationArmonk, New York, U.S.



Major Achievements of SPSSStages of SPSS Development

1 1968- Norman H. Nie (social scientist), C. Hadlai Hull (MBA graduatefrom Stanford) & Dale H. Bent (Doctoral candidate of OR), developedSPSS for local consumption for converting social science raw data todecision making.

2 1969- Nie joined University of Chicago & considered SPSS an intellectualproperty and Hull joined at the same University

3 1975- SPSS incorporated and two founders Nie & Hull became the com-pany executives

4 In Mid 1980s- Introduced first mainframe statistical package for PC

5 1992- SPSS was the first to market with a statistical product on PC

6 1996- Company shipped the first Windows version



Major Achievements of SPSSStages of SPSS Development

1 1997-2002- The transition to the enterprise (acquisition of Quantine, amarket research application software & ISL, data mining software)

2 2003- Added business intelligence technology of IBM e-server OnlineAnalytical Processing (OLAP) technology

3 2004- Predictive analytics is successfully established (advanced analytics& decision optimization)

4 July 2009- IBM (NYSE: IBM) and SPSS Inc. (Nasdaq: SPSS) enteredinto a definitive merger agreement for IBM to acquire SPSS, a publicly-held company headquartered in Chicago, in an all cash transaction ata price of $50 per share, resulting in a total cash consideration in themerger of approximately $1.2 billion.



IntroductionProducts and Services



IntroductionIBM SPSS Product free trials



IntroductionCharacteristics of SPSS

SPSS is a highly flexible and integrated software environment thatcan be used in virtually any setting to access, manipulate, manage,

store, analyze and report on data. It provides in-depth statisticalcapabilities, plotting, reporting and presentation features



IntroductionCharacteristics of SPSS

a graphical user interface for administering SPSS tasks

is among the most widely used programs for statisticalanalysis in social science

the flexibility to run on all major operating environmentssuch as Windows, UNIX, and z/OS macOS

the access to virtually any data source such asSPSS/PC+ (.sav), Systat(.syd,.sys), Portable (.por),excel (.xls, .xlsx, .xlsm), Lotus (.w), Sylk (.slk), dbase(.dbf), SAS (.sas), STATA (.dta), and Text (.txt) files



IntroductionHelp Facility

The help facility can be accessed from a Web browser athttps://www.ibm.com/analytics/us/en/technology/spss/

The Help facility can be opened within your SPSS windowfrom Help SPSS help and Tutorials



IntroductionWhy SPSS?

SPSS, SAS or STATA: which is the best package?

Each package has its own unique style and its own strengthsand weaknesses

Switching from one analysis package to another depending onthe nature of your problem. For example, if you wereperforming analyses using mixed models you might chooseSAS, but if you were doing logistic regression you mightchoose Stata, and if you were doing analysis of variance youmight choose SPSS.

If you are frequently performing statistical analyses, we wouldstrongly urge you to consider making each one of thesepackages part of your toolkit for data analysis.




SPSS, SAS or STATA: General Use?

SPSS :SPSS has a ”point and click” interface that allows youto use pull down menus to select commands that you wish toperform. SPSS also supports a ”syntax” language

SAS :Power users like because of programmability alsodifficult to learn. To perform data analyses and manipulationyou must write a program. Errors are difficult to locate

STATA :It uses one line commands which can be entered onecommand at a time easy for beginners or can be entered manyat a time in a Stata program. The errors are easy to locate




SPSS, SAS or STATA: Data Management?

SPSS : SPSS resembles Excel that allows you to enter your data and at-tributes of your data (missing values, value labels, etc.). SPSS primarilyedits one data file at a time and is not very strong for tasks with multipledata files at once. There is no limit to the number of variables or casesallowed in your SPSS data files - you are only limited only by your diskspace.SAS : It allows you to manipulate your data in just about any way possible.SAS includes proc sql that allows you to perform sql queries on your SASdata files. However, SAS can work with many data files at once. SAS canhandle enormous data files up to 32,768 variables and the number of recordsis generally limited to the size of your hard disk.STATA :While the data management capabilities of Stata may not be quiteas extensive as those of SAS. However, Stata primarily works with one datafile at a time so tasks that involve working with multiple files at once canbe cumbersome. You can now have up to 32,768 variables in a Stata datafile but probably would not want to analyze a data file that exceeds the sizeof your computers memory.Prabhat Mittal Data Analytics Using SPSS 17 / 33



SPSS, SAS or STATA: Statistical Analysis?

SPSS :The greatest strengths of SPSS are in the area of analysis of vari-ance and multivariate analysis (e.g., manova, factor analysis, discriminantanalysis) and has added some capabilities for analyzing mixed models. Thegreatest weakness of SPSS are probably in the absence of robust methods

SAS :SAS can performs most general statistical analyses. The greateststrengths are in mixed model and multivariate analysis, while it is probablyweakest in ordinal and multinomial logistic regression and robust methods.While there is some support for the analysis of survey data, it is quite limitedas compared to Stata and SPSS.

STATA :The greatest strengths of Stata are probably in regression andcoefficient diagnostics, robust methods. Stata also excels in the area ofsurvey data analysis offering the ability to analyze surveys. The greatestweaknesses is in the area of analysis of variance and discriminant functionanalysis.




SPSS, SAS or STATA: Graphics?

SPSS :SPSS has a very simple point and click interface for creating graphsand once you create graphs they can be extensively customized via its pointand click interface. The graphs are very high quality and can be pasted intoother documents (e.g., Word documents or Powerpoint).

SAS :SAS may have the most powerful graphic tools among all of thepackages via SAS/Graph. However, SAS/Graph is also very technical andtricky to learn. The graphs are created largely using syntax language

STATA :Stata graphics can be created using Stata commands or using apoint and click interface. Unlike SPSS, the graphs cannot be edited using agraph editor. The syntax of the graph commands is the easiest of the threepackages and is also the most powerful.



IntroductionSAS, SPSS, R Vs. Python



Getting Started with SPSS ProgramBasic Steps in Analysis



Getting Started with SPSSSPSS Window

Click on SPSS Statistics 26.0 Select Type in data OK



Getting Started with SPSSEntering Data

Data Editor offers a simple and efficient spreadsheet like facility forentering data and browsing the working data file. It provides two

views of the data:

Data View-displays the actual data values or defined valuelabels

Variable View- displays variable information, includingdefined variables and value labels, data type, etc..,

Output Editor Window

Text Editor

Chart Editor



Getting Started with SPSSSaving data

We need to save it and give it a name. The default extensionname for saving files is ’.sav’

Ex. ABC.sav

Also we can able to retrieving already saved file



Entering DataDefine Variables

Variable is a user defined name of particular type of data to holdinformation (such as income or gender or temperature or dosage)

Numeric

Comma

Dot

Scientific notation

Date

Custom Currency

String



Getting Started with SPSSInput the following data into SPSS



Getting Started with SPSSDefine the variables in variable view



Getting Started with SPSSEnter the data in data view



Getting Started with SPSSInput the following data into SPSS



Data Analysis ToolsBasic Descriptive Statistics



Data Analysis ToolsStatistical Procedures in SPSS

The Procedures available are:

Reports: Textual work made with the specific intention ofrelaying information or recounting certain events in a widelypresentable form

Descriptive Statistics: Frequency, Descriptives, Explore,Crosstabs

Custom Tables: provides attractive, flexible, displays offrequency counts, percentages and other statistics

Compare means: testing differences among two or moremeans on their values for other variable

General Linear model (GLM):Testing Univariate andmultivariate analysis-of-variance models including repeatedmeasures



Data Analysis ToolsStatistical Procedures in SPSS

The SPSS Procedures available are:

Correlate: Measures association for two or more variables

Regression: linear, logistic, nonlinear, weighted, Two stageleast squares

Loglinear: General and hierarchical log-linear analysis andlogit.

Classify: Cluster and discriminant analysis

Data Reduction: Factor analysis, correspondence analysisand optional scaling

Scale: Reliability analysis and multidimensional scaling

Non parametric tests: one, two paired or independentsamples

Time Series: Autocorrelations, Cross-correlations, Spectral

Survival: Kaplan-Meier analysis and Cox Regression


Begin Analyzing Your Data

URL: http://people.du.ac.in/p̃mittal/

Data Analytics Using SPSSqtanalytics.in/wp-content/uploads/2019/12/Learning-SPSS.pdf ·...

Documents

Transcript of Data Analytics Using SPSSqtanalytics.in/wp-content/uploads/2019/12/Learning-SPSS.pdf ·...