دليل مبسط لبرنامج SPSS

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SPSS

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14252005 _|aII ae|a|IIj 6_IaI ka|II p_Is qwa a,ae @ ea _a|aII ge|ayI... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,1 .U >..l2,aJ,I 2: 2,aIJ ,t 2qwqaI3 - 7 1 - 3352 - 67: _,InsI oIaIqI a, y, 8 - 11888910111111: _,InsI oIaIqI _qans aIsaI y, 12 - 1612131313141516 : oIsax 2qaIay 17 - 33... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,21 - 172 - 193 - 214 - 225 - 246 - 247 - 288 - 309 - 3110 - 3133: wI 2qaIqI 34 - 393435373939 - 40ax,I 41 - 441 412 423 42 - 44 ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,3 2,aJ,I :SPSS )Statistical package for social sciences (" ". ) ( . SPSS . SPSS . Datafiles . Output files . SPSS)( . ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,4 . : :1 . .2 . SPSS.3 . .4 . . x : 2wqaI ,t 2,aIJ1 . :Data Editor Menus VariablesCases .: ::) ( ).( .( : Data View: File: . Edit: View :. Data :.Transform : . ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,5Analyze: .. Graphs::..... . Utilities : . Window :SPSS SPSS. Help :(internet Home Page ) SPSS . ) 1 ( ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,6 ( : Variable View: ::Name -@ # - $- .- .- ) ! : * (Type & Width : .Labels:256. missingvalue: : . ) 2 (... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,72 . Output: File :.Edit: . View :.Insert: . Format :. Statistics :. Utilities : . Window :SPSSSPSS . Help :( internet Home Page ) SPSS , . ) 3 (... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,8qI,s :1 :.2 :. ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,9 IqaIa : oIaIqI a, y, _,InsI Data Editor SPSS. : ) ( . . 1 2= 4 = 3 = 2 = 1 ( missing )) .( . "" Variables " " Cases . .SPSS. . ).....) ,)1 2 (... 1 2 . : 1 - 34 Enter 1 0000 Var 2 -... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,1022 Enter23 Enter24 Enter3 - : 23Missing (.) ) 4 ( :) ( :Sex, Subject, Attitude1, Attitude2, etc. : 1 - 00001 VarDataDefine Variable 2 - 00001 Var Age 3 - Ok.Age ) ( . ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,11 : SPSS.. . numeric string . 1 -2 - m 3 -4 - Data... Define variable 5 - ( variable name)Gender6 - TypeString7 - LabelsStudent Gender( Variable Label ) . 8 - f(Value )Female ( Value label)Add 9 - mmale Add"Female " =f" Male " =m 10 - OK 11 - mf ).(12 - .13 - View Value Label. ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,12 :File Savesave . Missing Values : . ) 5 ( : 1 : ) (. 2 : ) (.. ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,13IaIIa: _qans aIsaI y, _,InsI oIaIqIOutput and Modifying Data :Data Output. . . Data fileOutput file . : file open . open data . open1 2 ) 6 (... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,14 : SPSS : 1 - Analyze 2 - ).(3 - 4 -5 - OK)OK . : SPSS.Output . : SPSS UtilitiesVariables. ) 7 ( ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,15 : SPSS RecodingComputing . Recoding : : -1 - TransformRecode 2 - Into same variables 3 - ) (4 - ) Old and new values(5 - ( Old value) 6 - (New value) (System-Missing ) 7 - Add 8 -9 - Continue 10 - OK 11 - Range 12 - RangeLowest through 13 - ..: 1 ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,16 2 3) 8 ( : SPSS ) (.: -1 - TransformCompute 2 - Target variable ( ) 3 - ) ( Numeric Expression: 4 - ( (5 - + * / .... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,176 - .7 - OK. 1 2) 9 ( Functions : SPSS70 ) 9 ( . :1 : ) (. ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,18In: 2qaIay oIsax Statistics (Analyze)SPSS . SPSS .. Report: . Summarizing Data: . : 1 - Frequencies : . Summarystatistics histogram . Frequencies :

SPSSemployee data.savAnalyze frequenciessalary ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,19chartcharts frequencieshistogram. statistics salary . chart bar rangmidpoints.frequencies . 1 2) 10 ( ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,20 ) 11 ( Salary 2 - Descriptive : : , . DescriptiveSPSSemployeedata.savAnalyze Descriptivesalary: ) 12 ( DescriptiveCurrent Salary135000.0125000.0115000.0105000.095000.085000.075000.065000.055000.045000.035000.025000.015000.0Current SalaryFrequency140120100806040200Std. Dev = 17075.66Mean = 34419.6N = 474.00... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,21Descriptive Statistics474 $15,750 $135,000 $34,419.57 $17,075.661474Current SalaryValid N (listwise)N Minimum Maximum Mean Std. Deviation ) 13 ( descriptive3 - Explorer :.jobcat categories statisticsExplorersalary dependent listjobcatfactor list descriptivestatisticsstem_and_leafplot ) ( job category jobcategoriesmedian (th 75 th 25interquartile rang0 * .: ) 14 ( Explore... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,22Case Processing Summary363 100.0% 0 .0% 363 100.0%27 100.0% 0 .0% 27 100.0%84 100.0% 0 .0% 84 100.0%Employment CategoryClericalCustodialManagerCurrent SalaryN Percent N Percent N PercentValid Missing TotalCasesDescriptives$27,838.54 $397.217$27,057.40$28,619.68$27,290.50$26,550.0057274548$7,567.995$15,750$80,000$64,250$8,400.001.905 .1287.977 .255$30,938.89 $406.958$30,102.37$31,775.40$31,007.72$30,750.004471602.6$2,114.616$24,300$35,250$10,950$1,200.00-.368 .4483.652 .872$63,977.80 $1,990.668$60,018.44$67,937.16$62,728.31$60,500.00332871850$18,244.776$34,410$135,000$100,590$20,475.001.181 .2632.107 .520MeanLower BoundUpper Bound95% ConfidenceInterval for Mean5% Trimmed MeanMedianVarianceStd. DeviationMinimumMaximumRangeInterquartile RangeSkewnessKurtosisMeanLower BoundUpper Bound95% ConfidenceInterval for Mean5% Trimmed MeanMedianVarianceStd. DeviationMinimumMaximumRangeInterquartile RangeSkewnessKurtosisMeanLower BoundUpper Bound95% ConfidenceInterval for Mean5% Trimmed MeanMedianVarianceStd. DeviationMinimumMaximumRangeInterquartile RangeSkewnessKurtosisEmployment CategoryClericalCustodialManagerCurrent SalaryStatistic Std. Error1... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,2384 27 363 N =Employment CategoryManager Custodial ClericalCurrent Salary160000140000120000100000800006000040000200000343 183229386126206 281303 291146310 55217447234 80161722722182 3 ) 15 ( Explore 4 - Cross tabs : . SPSSemployee data.sav: id . , genderm=male,f=female Minority= 1 = 0Educ=12 =16 ....Jobcat 3=manager , 2=custodial ,1=clerical SalbeqinJob time. Prevexp. :jobcatogory,gender,minority corsstabulation ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,24Case Processing Summary474 100.0% 0 .0% 474 100.0%Employment Category* Gender * MinorityClassificationN Percent N Percent N PercentValid Missing TotalCasesEmployment Category * Gender * Minority Classification CrosstabulationCount166 110 27614 1410 70 80176 194 37040 47 8713 134 440 64 104ClericalCustodialManagerEmploymentCategoryTotalClericalCustodialManagerEmploymentCategoryTotalMinority ClassificationNoYesFemale MaleGenderTotal jobcatogorygender minority countexpected count . ) 16 ( corsstabulation ) 17 ( corsstabulation... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,255 . :List of Cases SPSS) (.6 . ComparingSPSS: :Means . . 1 - : Paired-Sample T Test . .) ( . : beginning salary , current salary (T) Paired-Sample T Test Compare meananalyze . beginningsalary,currentsalarypaired variable Ok . : ) 18 ( paired sample T test... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,26Paired Samples Statistics$34,419.57 474 $17,075.661 $784.311$17,016.09 474 $7,870.638 $361.510Current SalaryBeginning SalaryPair1Mean N Std. DeviationStd. ErrorMeanPaired Samples Test$17,403.48 $10,814.620 $496.732 $16,427.41 $18,379.56 35.036 473 .000Current Salary -Beginning SalaryPair1Mean Std. DeviationStd. ErrorMean Lower Upper95% ConfidenceInterval of theDifferencePaired Differencest df Sig. (2-tailed) ) 19 ( Paired sample T test 2 - : Independent-Samples T Test .... . Gender(T) . : Independent-SamplesTTestComparemeans Analyze .Test variablesalary group variablegender 12 DefinegroupsOk .: ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,27Group Statistics258 $41,441.78 $19,499.214 $1,213.968216 $26,031.92 $7,558.021 $514.258Gender12Current SalaryN Mean Std. DeviationStd. ErrorMeanIndependent Samples Test119.669 .000 10.945 472 .000 $15,409.86 $1,407.906 $12,643.322 *********11.688 344.262 .000 $15,409.86 $1,318.400 $12,816.728 *********Equal variancesassumedEqual variancesnot assumedCurrent SalaryF Sig.Levene's Test forEquality of Variancest df Sig. (2-tailed)MeanDifferenceStd. ErrorDifference Lower Upper95% ConfidenceInterval of theDifferencet-test for Equality of Means 1 2

) 20 ( Independent-Samples T Test... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,28ANOVACurrent Salary8.94E+10 2 4.472E+10 434.481 .0004.85E+10 471 102925714.51.38E+11 473Between GroupsWithin GroupsTotalSum ofSquares df Mean Square F Sig.3 - : One-Sample . . 4 - : One-Way Anova )(. . :Jobcat .One-Way Anova Comparemeans Analyze . dependentlist salary FactorJobcat Ok .: ) 21 ( One-Way Anova ) 22 ( One-Way Anova ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,29Correlations1 .880**. .000474 474.880** 1.000 .474 474Pearson CorrelationSig. (2-tailed)NPearson CorrelationSig. (2-tailed)NCurrent SalaryBeginning SalaryCurrent SalaryBeginningSalaryCorrelation is significant at the 0.01 level (2-tailed).**. 7 - Correlate.1 . Bivariate Correlations : . . : Bivariate Correlate analyzebeginningsalary,currentsalaryVariable Ok .: ) 23( Bivariate Correlations ) 23 ( Bivariate Correlations ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,302 . Correlations Partial : . ) ( . jobtime &prevexp ,(controlling ) . : analyzecorrelate Partial. Salbegin salary. job timeprevexp control variableOk . . ) 24 (_ P A R T I A L C O R R E L A T I O N C O E F F I C I E N T S Controlling for..JOBTIME PREVEXP SALARY SALBEGIN SALARY1.0000.8947 (0)(470) P= . P= .000 SALBEGIN .8947 1.0000 (470)(0) P= .000P= . (Coefficient / (D.F.) / 2-tailed Significance) ) 25 (... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,318 - Regression. 1 - Linear Regression )() () (education ,. :analyze Regression linear Regressionsalary salbegin, jobtime, prevexp Oksignificancecolumn . ) 26 ( ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,32Model Summary.897a.804 .803 $7,586.187Model1R R SquareAdjustedR SquareStd. Error ofthe EstimatePredictors: (Constant), Previous Experience (months),Months since Hire, Beginning Salarya. ANOVAb1.11E+11 3 3.696E+10 642.151 .000a2.70E+10 470 57550239.511.38E+11 473RegressionResidualTotalModel1Sum ofSquares df Mean Square F Sig.Predictors: (Constant), Previous Experience (months), Months since Hire,Beginning Salarya. Dependent Variable: Current Salaryb. Coefficientsa-10266.6 2959.838 -3.469 .0011.927 .044 .888 43.435 .000173.203 34.677 .102 4.995 .000-22.509 3.339 -.138 -6.742 .000(Constant)Beginning SalaryMonths since HirePrevious Experience(months)Model1B Std. ErrorUnstandardizedCoefficientsBetaStandardizedCoefficientst Sig.Dependent Variable: Current Salarya. ) 27 (9 - Data Reduction Factor Analysis : Factors . .40 5. 10 - Nonparametric Tests analyze .... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,331 - Chi-Square .) ( gender) 50 % 50 %( . :analyzeNonparametric TestsChi-Squaregender2 test variable Expected valuesAllcategories equal categories. .: ) 28 ( GENDER2258 237.0 21.0216 237.0 -21.04741.002.00TotalObserved N Expected N Residual Test Statistics3.7221.054Chi-SquareadfAsymp. Sig.GENDER20 cells (.0%) have expected frequencies less than5. The minimum expected cell frequency is 237.0.a. ) 29 (... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,34 2 - Binomial: . - K-S. U- K-S. - Kruskal-Wallis. Wilcoxon singed-rank. Friedman,Kindall`sW,and Cochern`s Q. : 1 : .. ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,35 Iw,Ia : 2qaIqI wI aazJ Creating and Modifying ChartsSPSS . : 1 - GraphsBar 2 -Summaries for groups of cases 3 - Define 4 - Other summary function 5 - Variable 6 - Category Axis 7 - OK :BarGraph DefineCategoryAxisGender Ok .: 1 ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,36GenderMale FemaleCount270260250240230220210 2 ) 30 ( ) 31 ( Bar )( : 1 - GraphsBar 2 - Summaries of a separate variable 3 - Define 4 - . ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,37 Graph Bar Summaries of a separate variable Definesalary salbegin Ok: 1 2) 32 ( ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,38Beginning Salary Current SalaryMean40000300002000010000 ) 33 ( Creating a clustered Bar Chart : . : 1 - GraphsBar 2 - Clustered 3 - Summaries for groups of cases 4 - Define 5 - : Category Axis 6 - : Define Clusters by 7 - OK :Graph Barclustered summaries for groups cases Define categoryaxisgender jobcat Ok: ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,39GenderMale FemaleCount3002001000Employment CategoryClericalCustodialManager 1 2) 34 ( ) 35 ( ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,40 : : 1 - EditCopy 2 -3 - EditPast special 4 - PictureBitmap . : 1 : . 2 : . . 1 : ) ( .2 ::) ( ) (. 4 : . ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,415 : ) ( ) ( . 6 : ( (. ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,42 ) 1 ( Statistical Functions CFVAR(numexpr,numexpr[,...]) ) (. LAG(variable) . MAX(value,value[,...]) . MIN(value,value[,...]) . MEAN(numexpr,numexpr[,...]) ] . SD(numexpr,numexpr[,...])] . SUM(numexpr,numexpr[,...]) . VARIANCE(numexpr,numexpr[,...]) ] . ... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,43) 2 ( Missing Value Functions NMISS(variable[,...]) . MISSING(variable) :. SYSMIS(numvar) :. VALUE(variable) . ) 3 ( Arithmetic Functions ABS(numexpr) . ARSIN(numexpr) 1 + 1 . ARTAN(numexpr) . COS(radians) . EXP(numexpr)e) e ( LN(numexpr) e .... ; ..., _.. ..SPSS .,:s ..; ....:: .,.: ....,44 LG10(numexpr) 10 . MOD(numexpr,modulus) 5 22.5 0.5. RND(numexpr) 5 . SIN(radians). SQRT(numexpr) . TRUNC(numexpr) . .|: _,,:| _: |..., ]|. J| ]|., .,|.| J|, ., ,|.,

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