Outline

Introduction to SPSSMr. Magdi Morsi StatisticianDepartment of Research and Studies, MOHmagdimom@hotmail.com1SPSS Workshop AgendaLayout of the ProgramAdding Data to SPSSPreparing Data for AnalysisCreating New VariablesDescriptive Statistics Compare MeansLayout of the SPSS ProgramSPSS Program WindowsMenus and ToolbarsSPSS Program WindowsSPSS Program WindowsData EditorData ViewVariable ViewOutput ViewerSyntax EditorFile TypesData: filename.savOutput: filename.spvCommands: filename.spsMenus and Toolbars

Adding Data to SPSSData Entry in the SPSS Data EditorImport from ExcelOther Ways to Load DataData Entry in the SPSS Data Editor

Import from ExcelFile Menu:OpenDataIn Open Data box, enter: C:\OMSB\Data_Excel.xlsClick OK for defaults

Preparing Data for AnalysisVariable FormatsVariable LabelsValue LabelsMissing Values

Creating New VariablesCollapsing Variables Using RecodeComputing Variables

Recoding VariablesRecoding renumbers or collapses the values of a variableTransform menuRecode into different variablesHighlight variable(s) and move over with arrowFill in a Name and Label for the new variableClick Old and New Values

Recoding VariablesSpecify the Old Valuee.g., lowest through 29, 30 through 39, etc.Specify a New Valuee.g., 1 (for an A), 2(for a B), etc.Click on the Add buttonRepeat until all old and new values are specifiedOld values can be defined as single values, ranges or missing valuesAdd value and variable labels, etc.

Computing New VariablesCreate new variables using equations or functionsTransform menuCompute VariableEnter a Target Variable Name e.g. BMIBuild a Numeric ExpressionE.g. wt/(ht/100)*(ht/100)Click OK

11Descriptive Data AnalysisFREQUENCIES

DESCRIPTIVES

CROSSTABS

ExploreThe FREQUENCIES ProcedureFREQUENCIES creates tables with counts of cases for each value of the variableAnalyze Menu:Descriptive StatisticsFrequenciesHighlight variables to create tables, click the arrow to add to variable list, then click OKStatistics, Chart and Format options are available

FREQUENCIES OutputCommand syntaxSummary statisticsVariable values and corresponding labelsFrequency counts for each valuePercentagesRaw percentValid percentsCumulative percents

The DESCRIPTIVES ProcedureDESCRIPTIVES creates tables with summaries of values for variablesAnalyze Menu:Descriptive StatisticsDescriptivesHighlight variables to create tables, click the arrow to add to variable list, then click OKOptions are available to choose different statistics

DESCRIPTIVES OutputCommand syntaxVariable name and labelNumber of casesStatistics:MinimumMaximumMeanStandard Deviation

The CROSSTABS ProcedureCROSSTABS displays the intersection of values of two or more variablesAnalyze Menu:Descriptive StatisticsCrosstabsHighlight variables to create tables, click the arrow to add to Row, Column or Layer variable lists, then click OKStatistics, Cells and Format options are available

Crosstabs OutputTable titleColumn variablesRow variablesCell counts (# of cases)Column percents (% of cases in column)Statistics

Graphical RepresentationSimple Bar ChartSimple bar chart is a pictorial from one variable frequency table. It is drown for categorized (nominal/ordinal) variables.Analyze Menu:GraphsBar Simple.. DefineHighlight variable and transfer it to, Category Axis then click OK

19

Simple Bar Chart, cont.2. Multiple bar( or Clustered bar)chartAnalyze Menu:GraphsBar cluster.. DefineHighlight variable and transfer it to, Category Axis then Define Clusters by then click OK

3. Pie chartIt is another pictorial presentation of the distribution of a categorized variables.Analyze Menu:GraphsPie Summaries for groups of cases Define Slices by boxHighlight variable and transfer it to, Category Axis then click OK

4. HistogramHistogram is a graphical representation of continuous frequency distribution (scale variable) which gives idea about the symmetry of the distribution.Analyze Menu:GraphsHistogram Simple.. DefineHighlight variable and transfer it to, Variables then click OK

5. Box plots (Whisker plots)Boxplots are away of summarizing a distribution of scores of group of individuals.The box in boxplot shows the median score as a line and the first quartile (25th percentile) and third quartile( 75th percentile) of the score distribution as a the lower and upper parts of the box.Boxplots are preferred when the study variable does not follow normal distribution pattern.GraphsBoxplot Simple.. Define

7. Line Graph (Trend line) Simple line graph represents (ordinal) ungrouped frequency distribution. For drawing a line graph, the ordinal variable is taken along X-axis, the frequencies/measurements are taken along Y-axis and the corresponding plotted points are joined by straight lines to show the trend/pattern with respect to the ordinal variable.GraphsScatter/Dot.. Overlay Scatter Define

Test of NormalityThe assumption of normality is prerequisite for many inferential statistical techniques.We can explore this assumption Graphically by:- Histogram- Stem-and-leaf plot- BoxplotExploreExplore The Explore Descriptive table provides summary statistics for continuous, numeric variables.Analyze Menu:Descriptive StatisticsExploreHighlight variables to create tables, click the arrow to add to Dependent variable lists, then click OKStatistics, plots and options are available

Explore outputMean95% Confidence Interval for Mean5% Trimmed MeanMedianVarianceStd. DeviationMinimumMaximumRangeInterquartile RangeSkewnessKurtosis

28Explore plotsStem-and-Leaf PlotBoxplotHistogram

Test of significantChi-square test for Association - used to test the significance of association between two categorized variables.

Analyze Menu:Descriptive StatisticsCrosstabsHighlight variables to create tables, click the arrow to add to Dependent & Independent variable s, from statistics select chi square , then click OK

If you can classify two variables as Dependent & Independent, transfer Independent in Column box & Dependent in Row box.Assumptions of Chi-square test:Item or entity contribute s to only one cell. You cannot use chi-square on repeated measures design. The expected frequencies should be greater than 5.

2. T-tests (compare means)A t-test is used to determine whether a set or sets of scores arefrom the same population. Three main types of t-test may be applied :One sample.Independent groups.Repeated measures.Assumption testingAssumption testing:The generic assumption underlying all types of t-test are:Scale measurementRandom samplingNormalityAnalyze Menu:compare means

one sample T test

Analyze Menu:Compare meanOne sample t testPut test valueHighlight variables to create tables, click the arrow to add

Independent sample T testAnalyze Menu:Compare meanIndependent sample T testHighlight variables to create tables, click the arrow to add Select Grouping variable, then define Groups

Paired sample T test