Data analysis using spss
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This one-day workshop on data analysis using SPSS has two parts. Part 1 covers entering data into SPSS, including preparing datasets, transforming data, and running descriptive statistics. Part 2 provides an overview of statistical analysis techniques and how to choose the appropriate technique for decision making, giving examples. The document introduces SPSS and its four windows: the data editor, output viewer, syntax editor, and script window. It describes how to define variables, enter and manage data files, sort cases, compute new variables, and perform basic analyses like frequencies, descriptives, and linear regression. Proper use of statistical techniques depends on the research question, variable types and definitions, and assumptions.
Data analysis using spss
- 1. Welcome to the one day workshop on DATA ANALYSIS USING SPSS
- 2. Structure of the Workshop Time Split-up • Part 1 : 45 minutes Break : 15 minutes • Part 2: 1 hour Part-1 How to enter data into SPSS? 1. How to prepare a dataset? 2. How to transform the data in SPSS? 3. How to run basic descriptive statistics? Part-2 How to analyze data? 1. An overview of the statistical analysis techniques 2. Choosing the appropriate statistical analysis technique for decision making 3. Examples to understand which statistical analysis to use based on the requirements
- 3. Introduction: What is SPSS? • Originally it is an acronym of Statistical Package for the Social Sciences but now it stands for Statistical Product and Service Solutions • One of the most popular statistical packages which can perform highly complex data manipulation and analysis with simple instructions
- 4. The Four Windows: Data editor Output viewer Syntax editor Script window
- 5. The Four Windows: Data Editor • Data Editor Spreadsheet-like system for defining, entering, editing, and displaying data. Extension of the saved file will be “sav.”
- 6. The Four Windows: Output Viewer • Output Viewer Displays output and errors. Extension of the saved file will be “spv.”
- 7. The Four Windows: Syntax editor • Syntax Editor Text editor for syntax composition. Extension of the saved file will be “sps.”
- 8. The Four 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.”
- 9. The basics of managing data files
- 10. Opening SPSS • Start → All Programs → IBM SPSS Ststistics→ IBM SPSS Statistics 19 • The default window will have the data editor • There are two sheets in the window: 1. Data view 2. Variable view
- 11. 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
- 12. 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 – Cannot start with numbers and symbols – Spaces are NOT allowed.
- 13. 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.
- 14. Variable View window: Width • Width – Width allows you to determine the number of characters SPSS will allow to be entered for the variable
- 15. Variable View window: Decimals • Decimals – Number of decimals – It has to be less than or equal to 16 3.14159265
- 16. Variable View window: Label • Label – You can specify the details of the variable – You can write characters with spaces up to 256 characters
- 17. Variable View window: Values • Values – This is used and to suggest which numbers represent which categories when the variable represents a category
- 18. 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
- 19. Missing Values
- 20. Measures Nominal categorical-M/F,Which course,P/F,ethnicity,eye colour Lowest level of measurement May be referred to as qualitative,categorical or dichotomous eg:(M/F),type of course,religion,P/F,ethnicity,eye/hair colour,marital status Ideal to use pie chart
- 21. Ordinal • Ordered • Follows a natural order • Eg: ranking grades,levels,socio-economicstatus,pain intensity,likert scale(agree/disargee) or level of satisfaction(note the positivity of the attitute/perception of research question)
- 22. Scale-2 types (median,mode cant be calculated) 1.Interval (numeric ) • Ordered • Equal distance • Zero is existing • Eg:Temp,score 2.Ratio (continous) • Highest level • Exact Value • Eg:Height,weight,BP,Time
- 23. Measure Examples of scale variables include age in years and income in thousands of dollars. Examples of ordinal variables include attitude scores representing degree of satisfaction or confidence and preference rating scores. A variable can be treated as nominal when its values represent categories with no intrinsic ranking; for example, the department of the company in which an employee works.
- 32. Practice 1 • How would you put the following information into SPSS? • Ten students ID,Gender and Scores GenderValue = 1 represents Male and Value = 2 represents Female and PassFail Value=1 represents Fail and Value=2 represents Pass
- 33. Practice 1 (Solution Sample) Click
- 34. 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
- 35. Sorting the data • Click ‘Data’ and then click Sort Cases
- 36. Sorting the data (cont’d) • Double Click ‘Name of the students.’ Then click ok. Click Click
- 37. Practice 2 • 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.
- 38. Transforming data • Click ‘Transform’ and then click ‘Compute Variable…’
- 39. 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
- 40. Transforming data (cont’d) • A new variable ‘lnheight’ is added to the table
- 41. Practice 3 • Create a new variable named “sqrtheight” which is the square root of height. • Answer
- 43. The basic analysis of SPSS that will be introduced in this class • 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
- 44. Opening the sample data • Open ‘Employee data.sav’ from the SPSS – Go to “File,” “Open,” and Click Data
- 45. Opening the sample data • Go to Program Files,” “SPSSInc,” “SPSS16,” and “Samples” folder. • Open “Employee Data.sav” file
- 46. Frequencies • Click ‘Analyze,’ ‘Descriptive statistics,’ then click ‘Frequencies’
- 47. Frequencies • Click gender and put it into the variable box. • Click ‘Charts.’ • Then click ‘Bar charts’ and click ‘Continue.’ Click Click
- 48. Frequencies • Finally Click OK in the Frequencies box. Click
- 50. 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
- 51. 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!
- 52. Practice 4 • Do a frequency analysis on the variable “minority” • Create pie charts for it • Do the same analysis using the syntax editor
- 54. Answer Click
- 55. Descriptives • Click ‘Analyze,’ ‘Descriptive statistics,’ then click ‘Descriptives…’ • Click ‘Educational level’ and ‘Beginning Salary,’ and put it into the variable box. • Click Options Click
- 56. Descriptives • The options allows you to analyze other descriptive statistics besides the mean and Std. • Click ‘variance’ and ‘kurtosis’ • Finally click ‘Continue’ Click Click
- 57. Descriptives • Finally Click OK in the Descriptives box. You will be able to see the result of the analysis.
- 58. Regression Analysis • Click ‘Analyze,’ ‘Regression,’ then click ‘Linear’ from the main menu.
- 59. Regression Analysis • For example let’s analyze the model • Put ‘Beginning Salary’ as Dependent and ‘Educational Level’ as Independent. edusalbegin 10 Click Click
- 60. Regression Analysis • Clicking OK gives the result
- 61. Plotting the regression line • Click ‘Graphs,’ ‘Legacy Dialogs,’ ‘Interactive,’ and ‘Scatterplot’ from the main menu.
- 62. 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’. Click Set this to Regression!
- 64. Practice 5 • 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.
- 65. Answer Click
- 67. Click on the “fit” tab to make sure the method is regression
- 69. CHOOSING THE APPROPRIATE STATISTICAL ANALYSIS TECHNIQUE Choose based on 1.The type of research question, research design 2. Nature of data-a)identify and define your variables b) Identify the nature of the variables 3.Draw a diagram of your design
- 70. 1.Based on Research Question • 1.Descriptive research question(Descriptive Analyses) Ex: What is the pass percentage of a group/course? It can be calculated frequency,Percentiles(eg Y/N),central tendency (mean,median,mode),standard scores(z-score)
- 71. • 2.Correlational/Predictive(Correlation and Regression) Ex1:Is there any relationship between the pass percentage and the internal marks? Ex2:Can the attendance affect the pass percentage? Correlation can be + or – based on direction.Pearson correlation develops the relationship between two variables.(0.05) Regression is an extension of correlation.
- 72. • 3.Group Differences/Cause & Effect Ex:Is the pass percentage higher in one course or department than another • Analysing differences between groups- Compare Means(One-samples T- test,Independent Samples T-Test, • Paired Samples T-test,One-way Anova)
- 73. 2.Identify & define your variables 1.Independent or dependent? 2.Number of each variables?(eg:courses) 3.Operational definition of each variable 4.Normal/expected range of score level of each variable
- 74. 3.Identify the nature of variables • 1.How many variables? • Define each variable • Range of variables • Level of measurement (Dependent of Independent) Nominal/categorical-M/F,Which course,P/F. Ordinal-grades,levels,likert scale(agree/disargee) or level of satisfaction Scale(Interval-numeric or Ration – continuous)- temp,scale,course taught this semester,paper published).these can be 0- a value
- 75. 4.Make the final decision • Determine variables • Are the variables meeting all assumptions?
- 76. Choosing the appropriate statistical analysis to use for decision making
- 77. Values to remember • ANOVA: If sig. is greater than 0.05,then the homogeneity of variance assumption is not violated • Correlation: + or -;0.05 • Reliability:Chrobach’s Alpha: .00 to 1.0 ( - ) not good (no good consistency) .00 – no consistency 1.0-perfect consistency 0.07-means 70% is reliable (so 30% is error value)
- 78. Summary on choosing Analysis If Question is based on • Description: Descriptives • Relationship: Pearson’s • Difference : t-tests • Prediction : Regression
- 79. Thank You!