SPSS and its usage 2073/06/07 06/12. Dr. Bijay Lal Pradhan Dr Bijay Lal Pradhan

Transcription

1 SPSS and its usage 2073/06/07 06/12 Dr. Bijay Lal Pradhan

2 Ground Rule Mobile Penalty System Involvement

3 Object of session I Define Statistics and SPSS Install SPSS 20 and crack Open and exit SPSS Importing and exporting data Different format of files

4 What is Statistics? Singular form: The process of collection, organization, presentation, analysis and interpretation of number facts. Plural form: Aggregate of facts which has different characteristics. Comparable Numerous factors effects Numerically expressed Systematically collected Purposefully collected Accurate reasonably

5 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 highly complex data organization, presentation and analysis with simple instructions.

6 The Three Windows: Data editor Output viewer Syntax editor

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

8 The Three Windows: Output Viewer Output Viewer Displays output and errors. Extension of the saved file will be spv.

9 The Three Windows: Syntax editor Syntax Editor Text editor for syntax composition. Extension of the saved file will be sps.

10 The basics of managing software.

11 Installation of SPSS 20.0 You have software SPSS 20.0 in your computer There are two folders namely setup and crack Open setup folder and double click on application file setup. Follow the instruction and install SPSS in your computer. Don t go for licensing process. Copy "lservrc" from crack folder and paste it into the installed directory (C:\Programme\ IBM\SPSS\Statistics\20)

12 Opening Screen From start button click on IBM SPSS Statistics 20

13 Obtain the data Open your saved file with SPSS data1.sav

14 Variable descriptions Drop down menus Variable View Action buttons

15 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.

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

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

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

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

20 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

21 Measure scale?? Nominal Ordinal Scale

22 Nominal Gender Caste Marital status

23 Ordinal? First Second Third..

24 Scale Scale

25 Scales of Measure Scale Basic Characteristics Nominal Numbers identify & classify objects Ordinal Scale Nos. indicate the relative positions of objects but not the magnitude of differences between them Zero point is fixed, ratios of scale values can be compared Examples Examples Social Security nos., numbering of football players Quality rankings, rankings of teams in a tournament Length, weight Brand nos., store types Preference rankings, market position, social class Age, sales, income, costs Permissible Statistics Descriptive Inferential Percentages, mode Percentile, median quartile deviation Arithmatic, Geometric harmonic mean range MD SD Chi-square, binomial test Rank-order correlation, Friedman ANOVA Z test, t-test, ANOVA test all other tests

26 Data Editor Action buttons

27 SPSS output viewer Drop down menus Action buttons Navigation window

28 SPSS Viewer export results

29 Syntax Editor Drop down menus Action buttons Navigation window

30 Export

31 Import

32 Import

33 Data management with SPSS

34 Practice 1 Construct the following variables in the variable view on the basis of following information A study was conducted to know the attitude of a bank s customer towards the bank. The question asked to the customer was: Do you feel safe in your transactions with the bank? The respondents were to answer the question on a seven-point scale (1 = Strongly Disagree, 7 = Strongly Agree). There were other variables mentioned below on which data was collected. Strongly disagree 1 No difference 4 Little agree 5 Moderately disagree 2 Moderately agree 6 Little disagree 3 Strongly agree 7

35 Other variable 1. Sex of the respondent Male - M Female - F 2. Marital status Married - M Single - S 3. Income of the respondent (in rupees) 4. Age of the respondent (in years) 5. Educational background of the respondent Below higher secondary - 1 Higher secondary - 2 Graduation - 3 Post graduation - 4

36 Click

37 Entering Data Copy paste can be done to copy it from word to SPSS. First copy paste in to MS Excel and then to SPSS. Save the data in Excel and import to SPSS Or save in CSV format then to SPSS

38 Variable/Case in and out Entering new variable Deleting the existing variable Entering new case Deleting the existing cases

39 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

40 Sorting the data Click Data and then click Sort Cases

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

42 Transforming data Click Transform and then click Compute Variable

43 Transforming data (cont d) Example: Adding a new variable named corrected_ci which is corrected confidence interval Type in corrected_ci in the Target Variable box. Then type in 8-CI in the Numeric Expression box. Click OK Click

44 Transforming data (cont d) In the same way find the log(income) Type in ln_income in the Target Variable box. Then type in lnincome in the Numeric Expression box. Click OK In the similar manner Create a new variable named sqrtage which is the square root of age.

45 Visual Binning Visual Binning is the process of arranging data in a suitable class. So that we can tabulate data and can be drawn conclusion from the scale type of data.

46 The basic analysis using SPSS

47 Scopes & Limitations of Data Analysis Collection Organization Presentation Analysis Reporting If the data were collected from a random sample drawn from a well-defined population in such a way that every unit in the population has a known non-zero probability of being included in the sample, then the information derived from such sample can be generalized to the population (inferential statistics). If the data were collected from a non-random sample, then the information derived from sample cannot be generalized (descriptive statistics). If data and variables are not properly organized in a computer, then computer software fail to provide meaningful results.

48 Condensation of Data Summarizing data in tables and graphs (stem and leaf display, line graph, bar graph, pie chart and Histogram, measure of central tendency and measure of dispersion. 1. small tables (frequency tables) 2. graphs or diagrams (histogram, bar graph, pie chart etc.) 3. summary statistics (percentage, mean, standard deviation etc.)

49 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. Graphical Presentation Pie chart, Bar chart, Histogram, Area chart, Line chart, Scatter plot Descriptive Statistics This analysis shows the maximum, minimum, mean, and standard deviation of the variables

50 Descriptive & Inferential Statistics Statistics Descriptive Inferential Estimation Hypothesis Testing Tabular Graphical Numerical Point Interval Parametric Non-Parametric The methods of inferential statistics are applicable when results are obtained from a random. Uncertainty always remains while generalizing results from a sample to a population. The degree of uncertainty is measured in terms of probability in inferential statistics.

51 Univariate Data Analysis Analysis of data of a single variable at a time is univariate analysis. The suitable univariate data analysis methods by scale of variables are listed below Nominal or Ordinal What type of data? Scale data 1. Prepare frequency table 2. Compute mode 3. Compute median (ordinal) 4. Draw graphs Bar diagram Pie-chart 5. Chi-square test 1. Prepare frequency table (discrete) 2. Compute mean. Median and mode 3. Compute positional statistics 4. Compute SD, range etc. 5. Draw graphs. Steam-and-leaf plot (discrete). Box-Whisker plot. Histogram (continuous). Bar diagram (discrete). 6. Z, t, F & 2 tests 7. Transform into categorical.

52 Bivariate Data Analysis Analysis of data of two variables at a time. The kinds of data analysis are listed below. Nominal Ordinal 1. Prepare two-way frequency tables 2. Compute row or column percentages 3. Draw charts and diagrams 4. Test hypotheses (chi-square test of independence) 1. Prepare two-way frequency tables Scale 2. Draw Scatter diagram 3. Test hypotheses (chi-square, z, t, F tests) 4. Carry out correlation & simple regression analysis

53 Frequency Distribution Frequency distribution of a nominal/ordinal data

54 Frequency Distribution

55 Frequency table of scale data

56 Frequency distribution

57 Stem Leaf Display Income of the Respondent Stem-and-Leaf Plot Frequency Stem & Leaf Stem width: Each leaf: 1 case(s)

58 Diagrammatic Presentation Bar diagram Line graphs Pie diagram Scatter diagram Histogram

59 Descriptive measures Measure of Central Tendency Mean Arithmetic, Geometric, Harmonic Median Mode Measure of dispersion range QD SD

60 Mean value for ungrouped data

61 Next method for mean

62 Mean Value for Grouped Data Height Mid value frequency Go to: Data>weight cases Select weight cases by frequency and select ok Then find out mean using mid value as variable

63 Changing Report Row-Column

64 Skewness - Kurtosis Use compare mean and find out skewness and kurtosis of the data

65 Bivariate Data Analysis Analysis of data of two variables at a time. The kinds of data analysis are listed below. Nominal Ordinal 1. Prepare two-way frequency tables 2. Compute row or column percentages 3. Draw charts and diagrams 4. Test hypotheses (chi-square test of independence) 1. Prepare two-way frequency tables Scale 2. Draw Scatter diagram 3. Test hypotheses (chi-square, z, t, F tests) 4. Carry out correlation & simple regression analysis

66 Estimation Point Estimation Interval estimation Confidence Interval (Analyse>descriptive statistics>explore>estimation)

67 Fundamental of Hypothesis Testing There two types of statistical inferences, Estimation and Hypothesis Testing Hypothesis Testing: A hypothesis is a claim (assumption) about one or more population parameters. Average price of a lunch in hetauda is μ = Rs 200 The population mean monthly cell phone bill of this city is: μ = Rs 125 The average number of TV sets in Homes is equal to three; μ = 2

68 It Is always about a population parameter, not about a sample statistic Sample evidence is used to assess the probability that the claim about the population parameter is true A. It starts with Null Hypothesis, H 0 H:μ 0 3 and X=2.79 We begin with the assumption that H 0 is true and any difference between the sample statistic and true population parameter is due to chance and not a real (systematic) difference. Always contains =, or sign May or may not be rejected

69 B. Next we state the Alternative Hypothesis, H 1 Is the opposite of the null hypothesis e.g., The average number of TV sets in homes is not equal to 2 ( H 1 : μ 2 ) Never contains the =, or sign May or may not be proven Is generally the hypothesis that the researcher is trying to prove. Evidence is always examined with respect to H 1, never with respect to H 0. We never accept H 0, we either reject or not reject it

70 A. Rejection Region Method: Divide the distribution into rejection and non-rejection regions Defines the unlikely values of the sample statistic if the null hypothesis is true, the critical value(s) Defines rejection region of the sampling distribution Rejection region(s) is designated by, (level of significance) Typical values are.01,.05, or.10 is selected by the researcher at the beginning provides the critical value(s) of the test

71 Rejection Region or Critical Value Approach: Level of significance = H 0 : μ = 12 H 1 : μ 12 a/2 Non-rejection region a /2 Represents critical value H 0 : μ 12 H 1 : μ > 12 H 0 : μ 12 H 1 : μ < 12 Two-tail test Upper-tail test Lower-tail test a a Rejection region is shaded

72 P-Value Approach P-value=Max. Probability of (Type I Error), calculated from the sample. Given the sample information what is the size of blue are? H 0 : μ = 12 H 1 : μ 12 Two-tail test 0 H 0 : μ 12 H 1 : μ > 12 Upper-tail test 0 H 0 : μ 12 H 1 : μ < 12 0

73 Type I and II Errors: The size of, the rejection region, affects the risk of making different types of incorrect decisions. Type I Error Rejecting a true null hypothesis when it should NOT be rejected Considered a serious type of error The probability of Type I Error is It is also called level of significance of the test Type II Error Fail to reject a false null hypothesis that should have been rejected The probability of Type II Error is β

74 Two types of decision errors: Type I error = erroneous rejection of true H 0 Type II error = erroneous retention of false H 0 Truth Decision H 0 true H 0 false Retain H 0 Correct retention Type II error Reject H 0 Type I error Correct rejection α probability of a Type I error β Probability of a Type II error

75 P-Value approach to Hypothesis Testing: That is to say that P-value is the smallest value of for which H 0 can be rejected based on the sample information Convert Sample Statistic (e.g., sample mean) to Test Statistic (e.g., Z statistic ) Obtain the p-value from a table or computer Compare the p-value with If p-value <, reject H 0 If p-value, do not reject H 0

76 P-value (Observed Significance Level) P-value - Measure of the strength of evidence the sample data provides against the null hypothesis: P(Evidence This strong or stronger against H 0 H 0 is true) P val : p P( Z zobs)

77 Test of Hypothesis for the Mean σ known The test statistic is: σ Unknown The test statistic is: Z X μ σ n t n-1 X μ S n

78 Steps to Hypothesis Testing 1. State the H 0 and H 1 clearly 2. Identify the test statistic (two-tail, one-tail, and type of test to be used) 3. Depending on the type of risk you are willing to take, specify the level of significance, 4. Find the decision rule, critical values, and rejection regions. If CV<actual value (sample statistic) <+CV, then do not reject the H 0 5. Collect the data and do the calculation for the actual values of the test statistic from the sample

79 Steps to Hypothesis testing, continued Make statistical decision Do not Reject H 0 Reject H 0 Conclude H 0 may be true Conclude H 1 is true (There is sufficient evidence of H1) Make management/business/admi nistrative decision

80 When do we use a two-tail test? when do we use a one-tail test? The answer depends on the question you are trying to answer. A two-tail is used when the researcher has no idea which direction the study will go, interested in both direction. (example: testing a new technique, a new product, a new theory and we don t know the direction) A new machine is producing 12 fluid once can of soft drink. The quality control manager is concern with cans containing too much or too little. Then, the test is a two-tailed test. That is the two rejection regions in tails is most likely (higher probability) to provide evidence of H 1. H H 1 0 : 12 oz : 12 oz 12

81 One-tail test is used when the researcher is interested in the direction. Example: The soft-drink company puts a label on cans claiming they contain 12 oz. A consumer advocate desires to test this statement. She would assume that each can contains at least 12 oz and tries to find evidence to the contrary. That is, she examines the evidence for less than 12 0z. What tail of the distribution is the most logical (higher probability) to find that evidence? The only way to reject the claim is to get evidence of less than 12 oz, left tail. H H 1 0 : 12 oz : 12 oz

82 Type of Hypothesis What to test Significance of means Single mean test Double mean test Dependent pairs Independent pairs More than two mean test

83 Correlation and Regression

84 How do we measure association between two variables? 1. For ordinal and nominal variable Odds Ratio (OR) Chi square test of independence of attributes 2. For scale variables Correlation Coefficient R Coefficient of Determination (R-Square)

85 Example A researcher believes that there is a linear relationship between BMI (Kg/m 2 ) of pregnant mothers and the birth-weight (BW in Kg) of their newborn The following data set provide information on 15 pregnant mothers who were contacted for this study

86 BMI (Kg/m 2 ) Birth-weight (Kg)

87 Scatter Diagram Scatter diagram is a graphical method to display the relationship between two variables Scatter diagram plots pairs of bivariate observations (x, y) on the X-Y plane Y is called the dependent variable X is called an independent variable

88 Scatter diagram of BMI and Birthweight

89 Is there a linear relationship between BMI and BW? Scatter diagrams are important for initial exploration of the relationship between two quantitative variables In the above example, we may wish to summarize this relationship by a straight line drawn through the scatter of points

90 Simple Linear Regression Although we could fit a line "by eye" e.g. using a transparent ruler, this would be a subjective approach and therefore unsatisfactory. An objective, and therefore better, way of determining the position of a straight line is to use the method of least squares. Using this method, we choose a line such that the sum of squares of vertical distances of all points from the line is minimized.

91 Least-squares or regression line These vertical distances, i.e., the distance between y values and their corresponding estimated values on the line are called residuals The line which fits the best is called the regression line or, sometimes, the leastsquares line The line always passes through the point defined by the mean of Y and the mean of X

92 Linear Regression Model The method of least-squares is available in most of the statistical packages (and also on some calculators) and is usually referred to as linear regression Y is also known as an outcome variable X is also called as a predictor

93 Estimated Regression Line y ˆ = ˆ + ˆ x = x ˆ is. called. y int ercept ˆ is. called. the. slope

94 Application of Regression Line This equation allows you to estimate BW of other newborns when the BMI is given. e.g., for a mother who has BMI=40, i.e. X = 40 we predict BW to be y ˆ = ˆ ˆ + x = (40) 3.096

95 Correlation Coefficient, R R is a measure of strength of the linear association between two variables, x and y. Most statistical packages and some hand calculators can calculate R For the data in our Example R=0.94 R has some unique characteristics

96 Correlation Coefficient, R R takes values between -1 and +1 R=0 represents no linear relationship between the two variables R>0 implies a direct linear relationship R<0 implies an inverse linear relationship The closer R comes to either +1 or -1, the stronger is the linear relationship

97 Coefficient of Determination R 2 is another important measure of linear association between x and y (0 R 2 1) R 2 measures the proportion of the total variation in y which is explained by x For example r 2 = , indicates that 87.51% of the variation in BW is explained by the independent variable x (BMI).

98 Difference between Correlation and Regression Correlation Coefficient, R, measures the strength of bivariate association The regression line is a prediction equation that estimates the values of y for any given x

99 Limitations of the correlation coefficient Though R measures how closely the two variables approximate a straight line, it does not validly measures the strength of nonlinear relationship When the sample size, n, is small we also have to be careful with the reliability of the correlation Outliers could have a marked effect on R Causal Linear Relationship

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

101 Regression Analysis For example let s analyze the model salbegin 0 1edu Put Beginning Salary as Dependent and Educational Level as Independent. Click Click

102 Regression Analysis Clicking OK gives the result

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

104 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!

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106 Is the model significant? r 2 is the proportion of the variance in y that is explained by our regression model SE is also another measure check significance through complicated F-statistic: F (dfŷ,df er ) = s ŷ 2 rearranging =...= r2 (n - 2) 2 1 r 2 s er 2 And we should know the significance of reg. coeff. t = byx S.E. If all these satisfies than we can say model is

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108 For further Questions: