Business Research Methods. Introduction to Data Analysis

Transcription

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2 Business Research Methods Introduction to Data Analysis

3 Data Analysis Process

4 STAGES OF DATA ANALYSIS EDITING CODING DATA ENTRY ERROR CHECKING AND VERIFICATION DATA ANALYSIS

5 Introduction Preparation of Data Editing, Handling Blank responses, Coding, Categorization and Data Entry These activities ensure accuracy of the data and its conversion from raw form to reduced data Exploring, Displaying and Examining data Breaking down, inspecting and rearranging data to start the search for meaningful descriptions, patterns and relationship.

6 Editing The Process Of Checking And Adjusting The Data For Omissions For Legibility For Consistency And Readying Them For Coding And Storage

7 Editing FIELD EDITING IN-HOUSE EDITING

8 Reasons for Editing Accurate Consistent Arranged for simplification Criteria Uniformly entered Complete

9 Birth Year Recorded By Interviewer 1873? 1973 MORE LIKELY

10 Coding Involves assigning numbers or other symbols to answers so the responses can be grouped into a limited number of classes or categories. Example: M for Male and F for Female 1 for Male and 2 for Female Numeric vs Alphanumeric Numeric versus Alphanumeric Open ended questions Check accuracy by using 10% of responses

11 Coding Rules Exhaustive Appropriate to the research problem Categories should be Mutually exclusive Derived from one classification principle

12 Appropriateness Let s say your population is students at institutions of higher learning What is you age group? years years years Above 45 years

13 Exhaustiveness What is your race? Malay Chinese Indians Others

14 Mutual Exclusivity What is your occupation type? Professional Managerial Sales Crafts Operatives Unemployed Clerical Housewife Others

15 Single Dimension What is your occupation type? Professional Crafts Managerial Sales Clerical Housewife Operatives Unemployed Others

16 Coding Open-ended Responses

17 Coding Open Ended Questions

18 Handling Blank Responses How do we take care of missing responses? If > 25% missing, throw out the questionnaire Other ways of handling Use the midpoint of the scale Ignore (system missing) Mean of those responding Mean of the respondent Random number

19 Code Book Identifies each variable Provides a variable s description Identifies each code name and position on storage medium

20 Sample SPSS Codebook

21 Data Entry Keyboarding Database Programs Digital/ Barcodes Optical Recognition Voice recognition

22 Data Transformation Weights Assigning numbers to responses on a pre-determined rule Respecification of the Variable Transforming existing data to form new variables or items Recode Compute

23 Scale Transformation Reason for Transformation to improve interpretation and compatibility with other data sets to enhance symmetry and stabilize spread improve linear relationship between the variables (Standardized score) X z i - s X

24 Characteristics of Distributions

25 Summarizing Distributions with Shape

26 Parameter & Statistics Variable Population Sample Mean µ X Proportion p Variance 2 s 2 Standard deviation s Size N n Standard error of the mean x S x

27 Statistical Testing Procedures State null hypothesis Interpret the test Stages Choose statistical test Obtain critical test value Compute difference value Select level of significance

28 Hypotheses Null H0: = 50 mpg H0: < 50 mpg H0: > 50 mpg Alternate HA: 50 mpg HA: > 50 mpg HA: < 50 mpg

29 Accept/Reject

30 Accept/Reject

31 How to Select a Test Two-Sample Tests k-sample Tests Measurement Scale One-Sample Case Related Samples Independent Samples Related Samples Independent Samples Nominal Binomial McNemar Fisher exact test Cochran Q x 2 for k samples x 2 one-sample test x 2 two-samples test Ordinal Kolmogorov-Smirnov one-sample test Runs test Sign test Wilcoxon matched-pairs test Median test Mann-Whitney U Kolmogorov- Smirnov Wald-Wolfowitz Friedman twoway ANOVA Median extension Kruskal-Wallis one-way ANOVA Interval and Ratio t-test t-test for paired samples t-test Repeatedmeasures ANOVA One-way ANOVA Z test Z test n-way ANOVA

32 Research Model 5 items Attitude 5 items 3 items 4 items Subjective norm 4 items Perceived Behavioral Control Intention to Share Information Actual Sharing of Information

33 Reliability - Command

34 Reliability Question: How reliable are our instruments? Reliability Statistics Cronbach's Alpha N of Items Item-T otal Statistics Att1 Att2 Att3 Att4 Att5 Scale Mean if Item Deleted Scale Variance if Corrected Item-T otal Cronbach's Alpha if Item Item Deleted Correlation Deleted

35 Reliability Reliability Statistics Cronbach's Alpha N of Items Item-T otal Statistics Sn1 Sn2 Sn3 Sn4 Scale Mean if Item Deleted Scale Variance if Corrected Item-T otal Cronbach's Alpha if Item Item Deleted Correlation Deleted

36 Reliability Reliability Statistics Cronbach's Alpha N of Items Item-Total Statistics Pbc1 Pbc2 Pbc3 Pbc4 Scale Mean if Item Deleted Scale Variance if Corrected Item-Total Cronbach's Alpha if Item Item Deleted Correlation Deleted

37 Reliability Reliability Statistics Cronbach's Alpha N of Items Item-Total Statistics Intent1 Intent2 Intent3 Intent4 Intent5 Scale Mean if Item Deleted Scale Variance if Corrected Item-Total Cronbach's Alpha if Item Item Deleted Correlation Deleted

38 Table in Report Variable N of Item Item Alpha Deleted Attitude SN Pbcontrol Intention Actual

39 Example - Recoding Perceived Enjoyment PE1 PE2 PE3 PE4 PE5 The actual process of using Instant Messenger is pleasant I have fun using Instant Messenger Using Instant Messenger bores me Using Instant Messenger provides me with a lot of enjoyment I enjoy using Instant Messenger

40 Recoding

41 Recoding

42 Data before Transformation

43 Data after Transformation

44 Frequencies - Command

45 Frequencies Question: 1. Is our sample representative? 2. Data entry error Valid Male Female Total Gender Cumulative Frequency Percent Valid Percent Percent Current Position Valid Technician Engineer Sr Engineer Manager Above manager Total Cumulative Frequency Percent Valid Percent Percent

46 Table in Report Gender Male Female Position Technician Engineer Sr Engineer Manager Above manager Frequency Percentage

47 Descriptives - Command

48 Descriptives Age Years working in the organization Total years of working experience Attitude subjective Pbcontrol Intention Actual Valid N (listwise) Question: Descriptive Statistics N Minimum Maximum Mean Std. Skewness Kurtosis Statistic Statistic Statistic Statistic Deviation Statistic Statistic Std. Error Statistic Std. Error Is there variation in our data? 2. What is the level of the phenomenon we are measuring?

49 Table in Report Attitude Subjective Norm Behavioral Control Intention Actual Mean Std. Deviation

50 Chi Square Test - Command

51 Crosstabulation Question: Is level of sharing dependent on gender? Gender * Intention Level Cr osstabulation Gender Total Male Female Count % within Gender % within Intention Level % of Total Count % within Gender % within Intention Level % of Total Count % within Gender % within Intention Level % of Total Intention Level Low High Total % 23.6% 100.0% 70.5% 94.4% 75.0% 57.3% 17.7% 75.0% % 4.2% 100.0% 29.5% 5.6% 25.0% 24.0% 1.0% 25.0% % 18.8% 100.0% 100.0% 100.0% 100.0% 81.3% 18.8% 100.0% Pearson Chi-Square Continuity Correction a Likelihood Ratio Fisher's Exact T est Linear-by-Linear Association N of Valid Cases Chi-Square Tests Asymp. Sig. Value df (2-sided) b a. Computed only for a 2x2 table Exact Sig. (2-sided) Exact Sig. (1-sided) b. 0 cells (.0%) have expected count less than 5. The minimum expected count is

52 T-test - Command

53 t-test (2 Independent) Question: Does intention to share vary by gender? Intention Gender Male Female Group Statistics Std. Std. Error N Mean Deviation Mean Independent Samples Test Intention Equal variances assumed Equal variances not assumed Levene's Test for Equality of Variances F Sig. t df Sig. (2-tailed) t-test for Equality of Means Mean Difference 95% Confidence Interval of the Std. Error Difference Difference Lower Upper

54 Paired t-test - Command

55 t-test (2 Dependent) Question: Are there differences between intention to share and actual sharing behavior? Pair 1 Intention Actual Paired Samples Statistics Std. Std. Error Mean N Deviation Mean Paired Samples Correlations Pair 1 Intention & Actual N Correlation Sig Paired Samples Test Pair 1 Intention - Actual Paired Differences 95% Conf idence Interval of the Std. Std. Error Dif ference Mean Deviation Mean Lower Upper t df Sig. (2-tailed)

56 One Way ANOVA - Command

57 One way ANOVA (k independent) Question: Does intention vary by position? ANOVA Intention Between Groups Within Groups Total Sum of Squares df Mean Square F Sig Duncan a,b Current Po sition Engine er Manag er Te chnician Sr Engineer Above manager Sig. Intention Subset for alpha =.05 N Means for groups in homogeneous subsets are displayed. a. Uses Harmonic Mean Sample Size = b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed.

58 Correlation - Command

59 Correlation (Interval/ratio) Question: Are the variables related? Attitude subjective Pbcontrol Intention Actual Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Correlations **. Correlation is significant at the 0.01 level (2-tailed). Attitude subjective Pbcontrol Intention Actual 1.697**.212**.808**.606** ** **.552** ** ** **.653**.281** 1.817** **.552** **

60 Table Presentation Attitude subjective Pbcontrol Intention Attitude subjective Pbcontrol Intention Actual 1.740** 1.201** **.662**.326** 1 Actual.660**.553** ** 1 *p< 0.05, **p< 0.01

61 Command

62 Multiple Regression Question: Which variables can explain the intention to share? Model 1 Variables Entered/Removed b Variables Variables Entered Removed Method Pbcontrol, subjective, Attitude a. Enter a. All requested variables entered. b. Dependent Variable: Intention Model 1 Model Summary b Adjusted Std. Error of Durbin- R R Square R Square the Estimate Watson.832 a a. Predictors: (Constant), Pbcontrol, subjective, Attitude b. Dependent Variable: Intention

63 Multiple Regression Model 1 Model 1 Regression Residual Total ANOVA b Sum of Squares df Mean Square F Sig a a. Predictors: (Constant), Pbcontrol, subjective, Attitude b. Dependent Variable: Intention (Constant) Attitude subjective Pbcontrol Unstandardized Coefficients a. Dependent Variable: Intention Coefficients a Standardized Coefficients Collinearity Statistics B Std. Error Beta t Sig. Tolerance VIF

64 Assumptions (Multicollinearity) Collinearity Diagnostics a Model 1 Dimension a. Dependent Variable: Intention Condition Variance Proportions Eigenvalue Index (Constant) Attitude subjective Pbcontrol

65 Assumptions (Outliers) Case Number Casewise Diagnostics a Predicted Std. Residual Intention Value Residual a. Dependent Variable: Intention

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67 After Removing Outliers Model 1 Model 1 Model Summary b Adjusted Std. Error of Durbin- R R Square R Square the Estimate Watson.900 a a. Predictors: (Constant), Pbcontrol, subjective, Attitude b. Dependent Variable: Intention Model 1 Regression Residual Total (Constant) Attitude subjective Pbcontrol Unstandardized Coefficients a. Dependent Variable: Intention ANOVA b Sum of Squares df Mean Square F Sig a a. Predictors: (Constant), Pbcontrol, subjective, Attitude b. Dependent Variable: Intention Coefficients a Standardized Coefficients Collinearity Statistics B Std. Error Beta t Sig. Tolerance VIF

68 Assumptions Advanced Diagnostics (Hair et al., 2006) Predicted Value Std. Predicted Value Standard Error of Predicted Value Adjusted Predicted Value Residual Std. Residual Stud. Residual Deleted Residual Stud. Deleted Residual Mahal. Distance Cook's Distance Centered Leverage Value a. Dependent Variable: Intention Residuals Statistics a Std. Minimum Maximum Mean Deviation N

69 Frequency Assumptions (Normality) Histogram Dependent Variable: Intention Mean = -1.99E-17 Std. Dev. = N = 192 Regression Standardized Residual

70 Expected Cum Prob Assumptions (Normality of the Error term) Normal P-P Plot of Regression Standardized Residual Dependent Variable: Intention Observed Cum Prob 1.0

71 Regression Studentized Residual Assumptions (Constant Variance) Scatterplot Dependent Variable: Intention Intention

72 Intention Assumptions (Linearity) Partial Regression Plot Dependent Variable: Intention Attitude

73 Intention Assumptions (Linearity) Partial Regression Plot Dependent Variable: Intention subjective

74 Intention Assumptions (Linearity) Partial Regression Plot Dependent Variable: Intention Pbcontrol 0 1

75 Table Presentation Variable Attitude Subjective Norm Perceived Control R 2 Adjusted R 2 F Value D-W Dependent = Intention Standardized Beta 0.607** 0.238** 0.105** *p< 0.05, **p< 0.01

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