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SPSS?



SPSS - Statistical Package for the
         Social Sciences
What is SPSS?

   SPSS is a comprehensive and flexible
    statistical analysis and data management
    solution.
   SPSS is a computer program used for survey
    authoring and deployment, data mining, text
    analytics, statistical analysis, and collaboration
    and deployment
Cont..
   SPSS can take data from almost any type of
    file and use them to generate tabulated
    reports, charts, and plots of distributions and
    trends, descriptive statistics, and conduct
    complex statistical analyses.
   SPSS is among the most widely used
    programs for statistical analysis in social
    science.
About
   Its is developed by Norman H. Nie and C.
    Hadlai Hull of IBM Corporation in the year
    1968. It is compatible with Windows, Linux,
    UNIX & Mac operating systems. SPSS is
    among the most widely used programs
    for statistical analysis in social science.
Used in…
   Telecommunications,
   Banking,
   Finance,
   Insurance,
   Healthcare,
   Manufacturing,
   Retail,
   Consumer packaged goods,
   Higher education,
   Government,
   and Market research.
Features of SPSS

   It is easy to learn and use.
   It includes a full range of data. management
    system and editing tools.
   It provides in-depth statistical capabilities.
   It offers complete plotting, reporting and
    presentation features.
Getting data into SPSS
   Creating new SPSS data files
   Opening existing SPSS system files
   Importing data from an ASCII file
   Importing data from other file formats
Entering Data
DATA EDITOR
   The data editor offers a simple and efficient
    spreadsheet like facility for entering data and
    browsing the working data file.
   This window displays the content of the data
    file.
   One can create new data files or modify
    existing ones.
   One can have only one data file open at a
    time.
Cont..
   This editor provides two views of the data,
DATA VIEW
       Displays the actual data values or defined
    value labels.
VARIABLE VIEW
       Displays variable definition information,
    including defined variable and value labels,
    data type, etc..,
Editing Data
PIVOT TABLE EDITOR


     Output can be modified in many ways with
  is editor, and can create multidimensional
  tables.
Ex:
            We can edit text, swap data in rows
  and columns
Cont..
TEXT OUTPUT EDITOR
      Text output not displayed in pivot tables
    can be modified with the text output editor.

CHART EDITOR
     High-resolution charts and plots can be
    modified in chart windows.
Saving Data
   We need to save it and give it a name. The
    default extension name for saving files is ‘.sav’
    .
   Ex. SSPS.sav
   Also we can able to retrieving already saved
    file
Variables

   Variable is a user defined name of Particular
    type of data to hold information (such as
    income or gender or temperature or dosage).
    Array of variable is a collection values of
    similar data types.
Variables types
 1.   Numeric
 2.   Comma
 3.   Dot
 4.   Scientific notation
 5.   Date
 6.   Custom currency
 7.   String
Rules for Variable Names
    Names must begin with a letter.
    Names must not end with a period.
    Names must be no longer than eight
    characters.
    Names cannot contain blanks or special
    characters.
    Names must be unique.
    Names are not case sensitive. It doesn’t matter
    if you call your variable CLIENT, client, or
    CliENt. It’s all client to SPSS.
BASIC STEPS IN DATA
ANALYSIS
   Get Your Data Into SPSS:
        We can open a previously saved SPSS
    data file, read a spreadsheet, database ,or
    text data file, or enter directly in the data
    editor.
   Select a Procedure:
        Select a procedure from the menus to
    calculate statistics or to create a chart.
Cont..
   Select The Variable For The Analysis:
        Variables in the data file are displayed in a
    dialog box for the procedure.

   Run The Procedure:
       Results are displayed in the viewer.
STATISTICAL PROCEDURES
 After entering the data set in data editor or
  reading an ASCII data file, we are now ready
  to analyze it.
The Procedures Available are
 Reports

 Descriptive Statistics

 Custom Tables

 Compare means

 General Linear model (GLM)
   Correlate
   Regression
   Loglinear
   Classify
   Data Reduction
   Scale
   Non parametric tests
   Time Series
   Survival
   Multiple response.
REPORTS
    Report is a textual work made with the
 specific intention of relaying information or
 recounting certain events in a
 widely presentable form
DESCRIPTIVE STATISTICS
   This provides techniques for summarizing
 Data with statistics, charts, and reports.
Cont..
CUSTOM TABLES
       It provides attractive, flexible, displays of
    frequency counts, percentages and other
    statistics.
COMPARE MEANS
        This provides techniques for testing
    differences among two or more means on their
    values for other variable.
Cont..
GENERAL LINEAR MODEL(GLM)
         This provides technique for testing
    univariate and multivariate analysis-of-
    variance models including repeated
    measures.
CORRELATE
   This provides measures of association for two
    or more Variable measured at the interval
    level.
Cont..
REGRESSION
   This provides a variety of regression
    techniques , including Linear, logistic,
    nonlinear, weighted, and two-stage least-
    squares regression.
LOGLINEAR
   This provides general and hierarchical log-
    linear analsis and logit analysis.
Cont..
CLASSIFY
   This provides cluster and discriminant analysis
DATA REDUCTION
   This provides factor analysis, correspondence
    analysis, and optional scaling.
Cont..
SCALE
   This provides reliability analysis and
    multidimensional scaling.
NON PARAMETRIC TESTS
   This provides non-parametric tests for one
    sample, or for two and paired or Independent
    sample.
Cont..
TIME SERIES
       Provides exponential smoothing, autocorrelated
  regression, ARIMA, X11 ARIMA, seasonal decomposition,
  spectral analysis, and related techniques.

SURVIVAL
       This provides techniques for analyzing the time for some
  terminal event to occur, including Kaplan-Meier analysis and
  Cox regression.

MULTIPLE RESPONSE:
     This provides facilities to define and analyze multiple-
 response .
GRAPHS
BAR
       Generate a simple , clustered , or stacked
    bar chart of the data.
LINE
       Generate a simple or multiple line chart of
    the data.
AREA
       Generate a simple or stacked area chart of
    the data.
Cont..
PIE
      Generates a simple pie chart or a
    composite bar chart from the data.
BOXPLOT
   Generates box plot showing the median,
    outline, and extreme cases of individual
    variables.
Cont..
PARETO
 Generates Pareto charts, bar charts with a line
 superimposed showing the cumulative sum.

CONTROL
 Produces the most commonly-used process-
 control charts.
Cont..
NORMAL P-P PLOTS
      The cumulative proportions of a variable's
 distribution against the cumulative proportions of the
 normal distribution.

NORMAL Q-Q PLOTS
     The quantiles of a variable's distribution against
 the quantiles of the normal distribution.

SEQUENCE
      Produces a plot of one or more variables by order
 in the file, suitable for examining time-series data.
TIME SERIES: AUTOCORRELATIONS
     Calculates and plots the autocorrelation
 function (ACF) and partial autocorrelation
 function of one or more series to any specified
 number of lags, displaying the Box-Ljung
 statistic at each lag to test the overall
 hypothesis that the ACF is zero at all lags.
TIME SERIES: CROSS-CORRELATIONS
    Calculates and plots the cross-correlation
 function of two or more series for positive,
 negative, and zero lags.
TIME SERIES: SPECTRAL
     Calculates and plots univariate or bivariate
 periodograms and spectral density functions,
 which express variation in a time series as the
 sum of a series of sinusoidal components. It
 can optionally save various components of the
 frequency analysis as new series.
Advantages
    SPSS offers a user friendliness that most
    packages are only now catching up to. It is
    popular, and though that is certainly not a
    reason for choosing a statistical package,
    many data sets are easily loaded into it and
    other programs can easily import SPSS files.
Disadvantages
   For academic use SPSS lags notably behind
    SAS, R and even perhaps others that are on the
    more mathematical rather than statistical side for
    modern data analysis.
   Its menu offerings are typically the most basic of
    an analysis and sometimes lacking even then,
    and it makes doing an inappropriate analysis very
    easy.
   It is expensive, sometimes ridiculously so, and
    even when you do buy you're really only leasing,
    and its license is definitely not user friendly.
   There are often compatibility issues with prior
Thank You. . .

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Spss

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  • 2. SPSS? SPSS - Statistical Package for the Social Sciences
  • 3. What is SPSS?  SPSS is a comprehensive and flexible statistical analysis and data management solution.  SPSS is a computer program used for survey authoring and deployment, data mining, text analytics, statistical analysis, and collaboration and deployment
  • 4. Cont..  SPSS can take data from almost any type of file and use them to generate tabulated reports, charts, and plots of distributions and trends, descriptive statistics, and conduct complex statistical analyses.  SPSS is among the most widely used programs for statistical analysis in social science.
  • 5. About  Its is developed by Norman H. Nie and C. Hadlai Hull of IBM Corporation in the year 1968. It is compatible with Windows, Linux, UNIX & Mac operating systems. SPSS is among the most widely used programs for statistical analysis in social science.
  • 6. Used in…  Telecommunications,  Banking,  Finance,  Insurance,  Healthcare,  Manufacturing,  Retail,  Consumer packaged goods,  Higher education,  Government,  and Market research.
  • 7. Features of SPSS  It is easy to learn and use.  It includes a full range of data. management system and editing tools.  It provides in-depth statistical capabilities.  It offers complete plotting, reporting and presentation features.
  • 8. Getting data into SPSS  Creating new SPSS data files  Opening existing SPSS system files  Importing data from an ASCII file  Importing data from other file formats
  • 9. Entering Data DATA EDITOR  The data editor offers a simple and efficient spreadsheet like facility for entering data and browsing the working data file.  This window displays the content of the data file.  One can create new data files or modify existing ones.  One can have only one data file open at a time.
  • 10. Cont..  This editor provides two views of the data, DATA VIEW  Displays the actual data values or defined value labels. VARIABLE VIEW  Displays variable definition information, including defined variable and value labels, data type, etc..,
  • 11. Editing Data PIVOT TABLE EDITOR  Output can be modified in many ways with is editor, and can create multidimensional tables. Ex:  We can edit text, swap data in rows and columns
  • 12. Cont.. TEXT OUTPUT EDITOR  Text output not displayed in pivot tables can be modified with the text output editor. CHART EDITOR  High-resolution charts and plots can be modified in chart windows.
  • 13. Saving Data  We need to save it and give it a name. The default extension name for saving files is ‘.sav’ .  Ex. SSPS.sav  Also we can able to retrieving already saved file
  • 14. Variables  Variable is a user defined name of Particular type of data to hold information (such as income or gender or temperature or dosage). Array of variable is a collection values of similar data types.
  • 15. Variables types 1. Numeric 2. Comma 3. Dot 4. Scientific notation 5. Date 6. Custom currency 7. String
  • 16. Rules for Variable Names  Names must begin with a letter.  Names must not end with a period.  Names must be no longer than eight characters.  Names cannot contain blanks or special characters.  Names must be unique.  Names are not case sensitive. It doesn’t matter if you call your variable CLIENT, client, or CliENt. It’s all client to SPSS.
  • 17. BASIC STEPS IN DATA ANALYSIS  Get Your Data Into SPSS: We can open a previously saved SPSS data file, read a spreadsheet, database ,or text data file, or enter directly in the data editor.  Select a Procedure: Select a procedure from the menus to calculate statistics or to create a chart.
  • 18. Cont..  Select The Variable For The Analysis: Variables in the data file are displayed in a dialog box for the procedure.  Run The Procedure: Results are displayed in the viewer.
  • 19. STATISTICAL PROCEDURES  After entering the data set in data editor or reading an ASCII data file, we are now ready to analyze it. The Procedures Available are  Reports  Descriptive Statistics  Custom Tables  Compare means  General Linear model (GLM)
  • 20. Correlate  Regression  Loglinear  Classify  Data Reduction  Scale  Non parametric tests  Time Series  Survival  Multiple response.
  • 21. REPORTS Report is a textual work made with the specific intention of relaying information or recounting certain events in a widely presentable form DESCRIPTIVE STATISTICS This provides techniques for summarizing Data with statistics, charts, and reports.
  • 22. Cont.. CUSTOM TABLES  It provides attractive, flexible, displays of frequency counts, percentages and other statistics. COMPARE MEANS  This provides techniques for testing differences among two or more means on their values for other variable.
  • 23. Cont.. GENERAL LINEAR MODEL(GLM)  This provides technique for testing univariate and multivariate analysis-of- variance models including repeated measures. CORRELATE  This provides measures of association for two or more Variable measured at the interval level.
  • 24. Cont.. REGRESSION  This provides a variety of regression techniques , including Linear, logistic, nonlinear, weighted, and two-stage least- squares regression. LOGLINEAR  This provides general and hierarchical log- linear analsis and logit analysis.
  • 25. Cont.. CLASSIFY  This provides cluster and discriminant analysis DATA REDUCTION  This provides factor analysis, correspondence analysis, and optional scaling.
  • 26. Cont.. SCALE  This provides reliability analysis and multidimensional scaling. NON PARAMETRIC TESTS  This provides non-parametric tests for one sample, or for two and paired or Independent sample.
  • 27. Cont.. TIME SERIES Provides exponential smoothing, autocorrelated regression, ARIMA, X11 ARIMA, seasonal decomposition, spectral analysis, and related techniques. SURVIVAL This provides techniques for analyzing the time for some terminal event to occur, including Kaplan-Meier analysis and Cox regression. MULTIPLE RESPONSE: This provides facilities to define and analyze multiple- response .
  • 28. GRAPHS BAR  Generate a simple , clustered , or stacked bar chart of the data. LINE  Generate a simple or multiple line chart of the data. AREA  Generate a simple or stacked area chart of the data.
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  • 32. Cont.. PIE  Generates a simple pie chart or a composite bar chart from the data. BOXPLOT  Generates box plot showing the median, outline, and extreme cases of individual variables.
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  • 35. Cont.. PARETO Generates Pareto charts, bar charts with a line superimposed showing the cumulative sum. CONTROL Produces the most commonly-used process- control charts.
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  • 38. Cont.. NORMAL P-P PLOTS The cumulative proportions of a variable's distribution against the cumulative proportions of the normal distribution. NORMAL Q-Q PLOTS The quantiles of a variable's distribution against the quantiles of the normal distribution. SEQUENCE Produces a plot of one or more variables by order in the file, suitable for examining time-series data.
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  • 42. TIME SERIES: AUTOCORRELATIONS Calculates and plots the autocorrelation function (ACF) and partial autocorrelation function of one or more series to any specified number of lags, displaying the Box-Ljung statistic at each lag to test the overall hypothesis that the ACF is zero at all lags.
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  • 44. TIME SERIES: CROSS-CORRELATIONS Calculates and plots the cross-correlation function of two or more series for positive, negative, and zero lags.
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  • 46. TIME SERIES: SPECTRAL Calculates and plots univariate or bivariate periodograms and spectral density functions, which express variation in a time series as the sum of a series of sinusoidal components. It can optionally save various components of the frequency analysis as new series.
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  • 48. Advantages  SPSS offers a user friendliness that most packages are only now catching up to. It is popular, and though that is certainly not a reason for choosing a statistical package, many data sets are easily loaded into it and other programs can easily import SPSS files.
  • 49. Disadvantages  For academic use SPSS lags notably behind SAS, R and even perhaps others that are on the more mathematical rather than statistical side for modern data analysis.  Its menu offerings are typically the most basic of an analysis and sometimes lacking even then, and it makes doing an inappropriate analysis very easy.  It is expensive, sometimes ridiculously so, and even when you do buy you're really only leasing, and its license is definitely not user friendly.  There are often compatibility issues with prior