Statistical Package for the Social Sciences INTRODUCTION TO SPSS SPSS for Windows Version 16.0: Its first version in 1968 In 1975.

Transcription

1 Statistical Package for the Social Sciences INTRODUCTION TO SPSS SPSS for Windows Version 16.0: Its first version in 1968 In SPSS Statistics were designed

2 INTRODUCTION TO SPSS

3 Objective About the four-windows in SPSS The basics of managing data files The basic analysis in SPSS

4 Introduction: What is SPSS? Originally it is an acronym of Statistical Package for the Social Science but now it stands for Statistical Product and Service Solutions One of the most popular statistical packages which can perform data manipulation and analysis with simple instructions

5 Statistical Package for the Social Sciences INTRODUCTION TO SPSS SPSS for Windows Version 16.0: Its first version in 1968 In SPSS Statistics were designed

6 Data Classification Level of Measurement Statistics Test 1.Categorical Categories data Gender: Male (M), Female (F) Religion/Nationality 1. Nominal Level Categories data: Gender, Name of City, Political Party * There is no distance property Qualitative Analysis (Non-parametric test) 2.Rank Ranking or Ordering data Income Level: 1= Very Low Income 2= Low Income 3= Medium Income 4= High Income 5= Very High Income Education Level: 1= Primary 2= Secondary 3= Height School 4= Collage 5= University 1. Ordinal Level Ranking data/ Opinion/Attitude data: 1= Very Disagree 2= Disagree 3= Neutral 4= Agree 5= Very Agree Median Chi-square test (χ 2 test) Spearman Rank Correlation etc. Bar Chart Pie Chart

7 3. Metric Certain unit of Measurement: Income =$ 215, Expenditure=$234, Agri. Productivity=245 X kg./unit, Speed = km./hr. etc. 3. Interval Level Fixed and equal units Distances between Categories Zero point is not available Metric data: G.P.A.= 3.52 Thermometer: 40 o F, 0 o C etc. Quantitative Analysis (Parametric test), S.D. t-test (two set of data) ANOVA (F-test), (more than two groups of data) 4. Ratio Level Defined Zero point Metric data: Real Number : Age, Income,, Expenditure, Area, Product, Height, Weight, Volume etc. Pearson s Correlation Multiple Regression Factor Analysis Correlation Matrix Index Number etc. Histograms Chart

8 Open Spss Windows Click Click

9 SPSS Windows

10 The Four Windows: 1-Data editor 2-Output viewer 3-Syntax editor 4-Script window

11 First Windows: Data Editor Data Editor Spreadsheet-like system for defining, entering, editing, and displaying data. Extension of the saved file will be *.sav

12 Second Windows: Output Viewer Output Viewer Displays output and errors. Extension of the saved file will be *.spv

13 Third Windows: Syntax editor Syntax Editor Text editor for syntax composition. Extension of the saved file will be *.sps

14 Fourth Windows: Script Window Script Window Provides the opportunity to write full-blown programs, in a BASIC-like language. Text editor for syntax composition. Extension of the saved file will be.*sbs

15 The basics of managing data files

16 Opening SPSS The default window will have the data editor There are two sheets in the window: 1. Data view 2. Variable view

17 Data View window The Data View window This sheet is visible when you first open the Data Editor and this sheet contains the data Click on the tab labeled Variable View Click

18 Types of Variables What are variables you would consider in buying a second hand bike? Brand (Trek, Raleigh) Type (road, mountain, racer) Components (Shimano, no name) Age Condition (Excellent, good, poor) Price Frame size Number of gears

19 Variable View window This sheet contains information about the data set that is stored with the dataset Name The first character of the variable name must be alphabetic Variable names must be unique, and have to be less than 64 characters. Spaces are NOT allowed.

20 Variable View window: Type Type Click on the type box. The two basic types of variables that you will use are numeric and string. This column enables you to specify the type of variable.

21 Variable View window: Width Width Width allows you to determine the number of characters SPSS will allow to be entered for the variable

22 Variable View window: Decimals Decimals Number of decimals It has to be less than or equal to

23 Variable View window: Label Label You can specify the details of the variable You can write characters with spaces up to 256 characters

24 Variable View window: Values Values This is used and to suggest which numbers represent which categories when the variable represents a category

25 Defining the value labels Click the cell in the values column as shown below For the value, and the label, you can put up to 60 characters. After defining the values click add and then click OK. Click

26 Practice 1 How would you put the following information into SPSS? Name Gender Height JAUNITA SALLY DONNA SABRINA JOHN MARK 1 6 ERIC BRUCE Value = 1 represents Male and Value = 2 represents Female

27 Practice 1 (Solution Sample) Click

28 Click

29 Saving the data To save the data file you created simply click file and click save as. You can save the file in different forms by clicking Save as type. Click Click Click

30 Import Files In SPSS Three types of Data 1. SPSS Files or SPSS Data 2. Excel files or Excel Data 3. Text files or Text data

31 1.SPSS Files or SPSS Data 1-Click 2-Click 3-Click 4-Click 5-Click 6-Click

32 Resulting Windows

33 2. Opening Excel Files 1-Click 2-Click 3-Click

34 1-Click 3-Click 2-Click 4-Click 5-Click

35 1-Click 2-Click 3-Click 4-Click

36 Selection Of 1or More Then 1 Variable 1-Click 2-Click (change variable) 3-Click

37 Click

38 1-Click Open window 2-Click

39 3.Opening Text Files 1 st Step

40 2 nd Step

41 3 rd Step

42 4 th Step

43 Sorting the data Click Data and then click Sort Cases

44 Sorting the data (cont d) Double Click Name of the students. Then click ok. Click Click

45 Practice How would you sort the data by the Height of students in descending order? Answer Click data, sort cases, double click height of students, click descending, and finally click ok.

46 Transforming data Click Transform and then click Compute Variable

47 Transforming data (cont d) Example: Adding a new variable named lnheight which is the natural log of height Type in lnheight in the Target Variable box. Then type in ln(height) in the Numeric Expression box. Click OK Click

48 Transforming data (cont d) A new variable lnheight is added to the table

49 Practice Create a new variable named sqrtheight which is the square root of height. Answer

50 The basic analysis

51 The basic analysis of SPSS Frequencies This analysis produces frequency tables showing frequency counts and percentages of the values of individual variables. Descriptives This analysis shows the maximum, minimum, mean, and standard deviation of the variables Linear regression analysis Linear Regression estimates the coefficients of the linear equation

52 Frequency distributions and graphing Levels of Measurement Frequency distributions Graphing data

53 Stages in scientific investigation Obtain your data: Usually get data from a sample, taken from a population. Descriptive statistics: Statistical Information of data. Inferential statistics: Use data from a sample to reveal characteristics of the population from which the sample data were selected.

54 Levels (scales) of measurement Nominal Scale: Consists of a set of categories that have different names. Measurements on a nominal scale label and categorize observations, but do not make any quantitative distinctions between observations. Example: Eye color: blue, green, brown, hazel

55 Levels of measurement Ordinal Scale: Consists of a set of categories that are organized in an ordered sequence. Measurements on an ordinal scale rank observations in terms of size or magnitude. Example: T-shirt size: Small, Med, Lrg, XL, XXL

56 Levels of measurement Interval Scale: Consists of ordered categories where all of the categories are intervals of exactly the same size. With an interval scale, equal differences between numbers on the scale reflect equal differences in magnitude. Ratios of magnitudes are not meaningful. Example: Fahrenheit temperature scale 40º Not Twice as hot 20º

57 Levels of measurement Ratio scale: An interval scale with the additional feature of an absolute zero point. With a ratio scale, ratios of numbers DO reflect ratios of magnitude. SPSS doesn t distinguish between these, collapses them into scale measurements

58 Distributions The data that we have entered into our SPSS files form distributions. Each column of information in the data view corresponds to the scores of a variable for the individuals in our sample.

59 Levels of measurement 1. Nominal (categorical or frequency data): When numbers are used as names. e.g. street numbers, footballers' numbers. We can do with nominal data is count how often each number occurs (i.e. get frequencies of categories). 2. Ordinal When numbers are used as ranks. e.g. order of finishing in a race: the first three finishers are "1", "2" and "3", but the difference between "1" and "2" is unlikely to be the same as between "2" and "3".

60 3. Interval When measurements are made on a scale with equal intervals between points on the scale e.g. temperature on Celsius scale 4. Ratio When measurements are made on a scale with equal intervals between points on the scale, and the scale has a true zero point. e.g. height, weight, time, distance.

61 Nominal data masquerading as scale measurements SPSS uses numbers as codes for nominal data. Here 1 = male and 2 = female. These are names, not numbers

62 Frequency distributions 50 scores on a statistics exam

63 Raw (ungrouped) Frequency Distribution Score Freq Score Freq Score Freq Score Freq

64 Grouped Frequency Distributions Class interval width = 3 Score Frequency Class interval width = 5 Score Frequency

65 Raw Frequency Raw Frequency Grouped Frequency Distributions Raw Frequency of Scores (Class Interval = 3): Raw Frequency of Scores (Class Interval = 5): Score Score

66 Cumulative Frequency Distributions Score Raw Freq. (=total in each cell) Cumulative freq. (=each cell total + all preceding cell totals) ( = ) 12 ( = ) 7 ( = 5+0+2) 2 ( = 0+2) 2 ( = 2) Cumulative freq. (= cum. freq. as % of total) ( = (14/50)*100 ) 24 ( = (12/50)*100 ) 14 ( = (7/50)*100 ) 4 ( = (2/50)*100 ) 4 ( = (2/50)*100 )

67 Frequency (% total) Cumulative frequency graph Score

68 Group A: N = 50 Relative Frequency Distributions Useful for comparing groups with different totals. Group B: N = 80 Score Raw Freq. Rel. Freq. Score Raw Freq. Rel. Freq % % % % % % % % % % % % % % % % Total: % Total: % Relative frequency = (cell total/overall total) x 100

69 Raw frequency Raw Frequency and Relative Frequency Distributions Only the scale of the graph changes - not the pattern of frequencies Raw Frequencies of Scores (N = 50) Score

70 Relative frequency (%) Relative Frequencies of Scores (N = 50) Score

71 No. of accidents per year Effects of aspect ratio and scale on graph appearance (a) A graph aimed at giving an accurate impression... Frequency of accidents volvo mini porsche Type of car driven

72 No. of accidents per year (b) A tall thin graph exaggerates apparent differences... Frequency of accidents volvo mini porsche Type of car driven

73 No. of accidents per year (c) A low wide graph minimises apparent differences... Frequency of accidents volvo mini porsche Type of car driven

74 No. of accidents per year (d) Starting the scale at a value other than zero can also exaggerate apparent differences. Frequency of accidents volvo mini porsche Type of car driven

75 Mean annual insurance premium (+/- 1 S.D.) Graphing averages If plotting averages, always include a measure of how scores are spread out around the average porsche drivers Type of driver skoda drivers

76 Opening the sample data Open Employee data.sav from the SPSS Go to File, Open, and Click Data

77 Opening the sample data Go to Program Files, SPSSInc, SPSS17, and Samples folder. Open Employee Data.sav file

78 Frequencies Click Analyze, Descriptive statistics, then click Frequencies

79 Frequencies Click gender and put it into the variable box. Click Charts. Then click Bar charts and click Continue. Click Click

80 Frequencies Finally Click OK in the Frequencies box. Click

81

82 Using the Syntax editor Click Analyze, Descriptive statistics, then click Frequencies. Put Gender in the Variable(s) box. Then click Charts, Bar charts, and click Continue. Click Paste. Click

83 Using the Syntax editor Highlight the commands in the Syntax editor and then click the run icon. You can do the same thing by right clicking the highlighted area and then by clicking Run Current Click Right Click!

84 Practice Do a frequency analysis on the variable minority Create pie charts for it Do the same analysis using the syntax editor

85

86 Answer Click

87 Descriptives Click Analyze, Descriptive statistics, then click Descriptives Click Educational level and Beginning Salary, and put it into the variable box. Click Options Click

88 Descriptives The options allows you to analyze other descriptive statistics besides the mean and Std. Click variance and kurtosis Finally click Continue Click Click

89 Descriptives Finally Click OK in the Descriptives box. You will be able to see the result of the analysis.

90 Regression Analysis Click Analyze, Regression, then click Linear from the main menu.

91 Regression Analysis For example let s analyze the model salbegin 0 1edu Put Beginning Salary as Dependent and Educational Level as Independent. (SPSS 16 but SPSS 17 has a different Window) Click Click

92 Regression Analysis Clicking OK gives the result

93 Plotting the regression line Click Graphs, Legacy Dialogs, Interactive, and Scatterplot from the main menu.

94 Plotting the regression line Drag Current Salary into the vertical axis box and Beginning Salary in the horizontal axis box. Click Fit bar. Make sure the Method is regression in the Fit box. Then click OK.(SPSS 16 but SPSS 17 has a different Window) Click Set this to Regression!

95

96 Practice Find out whether or not the previous experience of workers has any affect on their beginning salary? Take the variable salbegin, and prevexp as dependent and independent variables respectively. Plot the regression line for the above analysis using the scatter plot menu.

97 Answer Click

98

99 Click on the fit tab to make sure the method is regression

100