Data Analysis Using SPSS. By: Akmal Aini Othman

Transcription

1 Data Analysis Using SPSS By: Akmal Aini Othman

2 The key to GOOD descriptive research is knowing exactly what you want to measure and selecting a survey method in which every respondent is willing to cooperate and capable of giving you complete and accurate information efficiently Joe Ottaviani-

3 Uncertainty Influences The Type Of Research CAUSAL OR DESCRIPTIVE COMPLETELY CERTAIN ABSOLUTE AMBIGUITY EXPLORATORY Source: Zikmund, 2009

4 Problem Discovery and Definition Problem discovery Sampling Selection of research technique Selection of exploratory research technique Probability Nonprobability Secondary (historical) data Experience survey Pilot study Case study Data Gathering Collection of data (fieldwork) Problem definition (statement of research objectives) Data Processing and Analysis Editing and coding data Research Design Experiment Selection of basic research method Survey Laboratory Field Interview Questionnaire Observation Secondary Data Study Conclusions and Report Data processing Interpretation of findings Report Source: Zikmund, 2009

5 Thesis Contents Chap 1 - Introduction Chap 2 - Literature Review Chap 3 Methodology Chap 4 Findings & Discussion Chap 5 Conclusion and Recommendation

6 Thesis Contents Introduction why & what this research Literature Review who have done this research & how, what results, what shortcomings Research Framework & Data Collection why this framework, hypotheses; measurements, sample, how data can be collected Data Collection & Analysis what methods most appropriate, findings Conclusion have u achieved what you set out to do?

7 Thesis Contents Chap 1 Introduction Background of the study Problem Statement Research Question Research Objective Hypothesis Significance of the study Limitation

8 Thesis Contents Chap 4 Findings and Discussion Descriptive Analysis Test of Goodness of Data e.g Normality & Multicollinearity Factor Analysis Reliability and Validity Test Inferential Analysis / Hypothesis Testing

9 Data Preparation Process Prepare Preliminary Plan of Data Analysis Check Questionnaire Edit Code Transcribe Clean Data Statistically Adjust the Data Source: Malhotra, 2012 Select Data Analysis Strategy

10 Questionnaire Checking A questionnaire returned from the field may be unacceptable for several reasons. Parts of the questionnaire may be incomplete. The pattern of responses may indicate that the respondent did not understand or follow the instructions. One or more pages are missing. The questionnaire is received after the preestablished cutoff date. The questionnaire is answered by someone who does not qualify for participation.

11 Editing Treatment of Unsatisfactory Results Returning to the Field The questionnaires with unsatisfactory responses may be returned to the field, where the interviewers recontact the respondents. Assigning Missing Values If returning the questionnaires to the field is not feasible, the editor may assign missing values to unsatisfactory responses. Discarding Unsatisfactory Respondents In this approach, the respondents with unsatisfactory responses are simply discarded.

12 Coding Data coding Coding the variables Coding the response/items for each variable Eg. Variable for gender = sex Coding item 1=male, 2=female The numerical scale can be coded by using the actual number circled by the respondents (question 6 to 21) Random checks should be conducted to ensure data are coded correctly

13 Table 12.1 Coding of Serakan Co. Questionnaires 1. Age (years) 2. Education 3. Job level 4. Sex [1] Under 25 [1] High school [1] Manager [1] M [2] [2] Some college [2] Supervisor [2] F [3] [3] Bachelor s degree [3] Clerk 5. Work shift [4] [4] Master s degree [4] Secretary [1] First [5] Over 55 [5] Doctoral degree [5] Technician [2] Second [6] Other (specify) [6] Other (specify) [3] Third 5a. Employment Status [1] Part time [2] Full time Here are some questions that ask you to tell us how you experience your work life in general. Please circle the appropriate number on the scales below. To what extent would you agree with the following statements, on a scale of 1 to 7, 1 denoting very low agreement and 7 denoting very high agreement? 6. The major happiness of my life comes from my job Time at work flies by quickly I live, eat and breathe my job My work is fascinating My work gives me a sense of accomplishment My supervisor praises good work The opportunities for advancement are very good here My coworkers are very stimulating People can live comfortably with their pay in this organization I get a lot of cooperation at the workplace My supervisor is not very capable Most things in life are more important than work Working here is a drag The promotion policies here are very unfair My pay is barely adequate to take care of my expenses My work is not the most important part of my life

14 Data Transcription Fig Raw Data CATI/ CAPI Keypunching via CRT Terminal Optical Recognition Digital Tech. Bar Code & Other Technologies Verification: Correct Keypunching Errors Computer Memory Disks Other Storage Transcribed Data

15 Data Cleaning Consistency Checks Consistency checks identify data that are out of range, logically inconsistent, or have extreme values. Computer packages like SPSS, SAS, EXCEL and MINITAB can be programmed to identify out-of-range values for each variable and print out the respondent code, variable code, variable name, record number, column number, and out-of-range value. Extreme values should be closely examined.

16 Data Cleaning Treatment of Missing Responses Substitute a Neutral Value A neutral value, typically the mean response to the variable, is substituted for the missing responses. Substitute an Imputed Response The respondents' pattern of responses to other questions are used to impute or calculate a suitable response to the missing questions. In casewise deletion, cases, or respondents, with any missing responses are discarded from the analysis. In pairwise deletion, instead of discarding all cases with any missing values, the researcher uses only the cases or respondents with complete responses for each calculation.

17 Basic Terms Levels of Measurement Nominal Ordinal Interval Ratio Key Terms Variable Dimension Item Definition Dictionary Operational Variables Independent Dependent Moderating Mediating

18 Research Framework 5 items Management (Independent) 5 items 3 items Advancement (Independent) Job Satisfaction (Mediating) Productivity (Dependent) 4 items Salary (Independent) Gender (Moderating) 4 items Workload (Independent)

19 Scale Nominal Numbers Assigned to Runners Finish Ordinal Rank Order of Winners Finish Interval Performance Rating on a 0 to 10 Scale Third place Second place First place Ratio Time to Finish, in Seconds Source: Malhotra, 2007

20 What is Statistics process of making sense of data Descriptive Stat describe the basic features of data using tables, graphs, summary stats Inferential Stat generalising from samples to populations performing estimations, hypothesis tests, determining relationships and making predictions

21 Descriptive Statistics Norminal data frequencies, %, cross tabulation, mode, pie chart, bar chart Ordinal data - frequencies, %, cross tabulation, mode, median, pie chart, bar chart Interval & Ratio data mean, variance, std deviation, skewness, kurtosis, index number, histogram, box plot, stem and leaf plot

22 Inferential Statistics Statistical Techniques: Exploring differences between groups Exploring relationship Parametric Data must be interval and the distribution must be normal Nonparametric Data is categorical (norminal/ordinal) or interval but distribution is not normal

23 Data analysis Basic objectives: Getting a feel for the data Testing the goodness of data Testing the hypotheses Feel for the data Checking for the central tendency and the dispersion If there is less variability, the questions could be not properly worded Check for similar response for every questions Remember, if there is no variability in the data, then no variance can be explained

24 Data analysis It is always prudent to obtain: Frequency distributions for the demographic variables The mean, standard deviation, range and variance on the other dependent and independent variables An inter-correlation matrix of the variables, regardless whether hypotheses are related to the these analyses. If the correlation between two variables is high, say over.75, we should wonder whether they are really two different concepts or we are measuring the same concepts.

25 Data analysis Testing goodness of data Reliability Cronbach s alpha. The closer Cronbach s alpha is to 1, the higher the internal consistency reliability Split-half reliability coefficient Stability measures include: Parallel from reliability Test-retest reliability Validity Criterion-related validity Convergent validity Discriminant validity

26 Choosing the Test Depends on: Data Norminal or Interval/Ratio Data Samples one/two/k-samples Purpose Describing, Comparing two statistics or Looking at relationship

27 A Classification of Univariate Techniques Univariate Techniques Metric Data Non-numeric Data One Sample * t test * Z test Independent * Two- Group test * Z test * One-Way ANOVA Two or More Samples Related * Paired t test One Sample * Frequency * Chi-Square * K-S * Runs * Binomial Independent * Chi-Square * Mann-Whitney * Median * K-S * K-W ANOVA Two or More Samples Related * Sign * Wilcoxon * McNemar * Chi-Square Source: Malhotra, 2012

28 Univariate Analysis Univariate analysis is the simplest form of analyzing data. Uni means one, so in other words your data has only one variable. It doesn't deal with causes or relationships (unlike regression) and it's major purpose is to describe; it takes data, summarizes that data and finds patterns in the data. It explores each variable in a data set, separately. It looks at the range of values, as well as the central tendency of the values. It describes the pattern of response to the variable. It describes each variable on its own.

29 A Classification of Multivariate Techniques Multivariate Techniques Dependence Technique Interdependence Technique One Dependent Variable More Than One Dependent Variable Variable Interdependence Interobject Similarity * Cross-Tabulation * Analysis of Variance and Covariance * Multiple Regression * 2-Group Discriminant/Logit * Conjoint Analysis * Multivariate Analysis of Variance * Canonical Correlation * Multiple Discriminant Analysis * Structural Equation Modeling and Path Analysis * Factor Analysis * Confirmatory Factor Analysis * Cluster Analysis * Multidimensional Scaling Source: Malhotra, 2012

30 Multivariate Analysis Multivariate Data Analysis refers to any statistical technique used to analyze data that arises from more than one variable. This essentially models reality where each situation, product, or decision involves more than a single variable.

31 Steps Involved in Hypothesis Testing Formulate H 0 and H 1 Select Appropriate Test Choose Level of Significance Collect Data and Calculate Test Statistic Determine Probability Associated with Test Statistic (p value) Compare with Level of Significance, Determine Critical Value of Test Statistic TS CR Determine if TS CAL falls into (Non) Rejection Region Reject or Do not Reject H 0 Draw Research Conclusion

32 Hypothesis Testing Hnull & Halternative A null hypothesis is a statement of the status quo, one of no difference or no effect. If the null hypothesis is not rejected, no changes will be made. An alternative hypothesis is one in which some difference or effect is expected. Accepting the alternative hypothesis will lead to changes in opinions or actions. The null hypothesis refers to a specified value of the population parameter (e.g., m, s, p ), not a sample statistic (e.g., ). X

33 H 1 : p > Hypothesis Testing Hnull & Halternative A null hypothesis may be rejected, but it can never be accepted based on a single test. In classical hypothesis testing, there is no way to determine whether the null hypothesis is true. The null hypothesis is formulated in such a way that its rejection leads to the acceptance of the desired conclusion. The alternative hypothesis represents the conclusion for which evidence is sought. H 0 : p

34 Hypothesis Testing Hnull & Halternative The test of the null hypothesis is a one-tailed test, because the alternative hypothesis is expressed directionally. If that is not the case, then a two-tailed test would be required, and the hypotheses would be expressed as: H 0 : p = H 1 : p

35 One-Tailed & Two-Tailed Test

36 One-Tailed & Two-Tailed Test

37 Test Statistic The test statistic measures how close the sample has come to the null hypothesis. The test statistic often follows a well-known distribution, such as the normal, t, or chisquare distribution. In our example, the z statistic,which follows the standard normal distribution, would be appropriate. z = p - p s p where s p = p ( 1 - p ) n

38 Type I and Type II error Type I Error Type I error occurs when the sample results lead to the rejection of the null hypothesis when it is in fact true. Type II Error Type II error occurs when, based on the sample results, the null hypothesis is not rejected when it is in fact false.

39 Descriptive Analysis

40 Frequencies - Command

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

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

43 Descriptives - Command

44 Descriptives Descriptive Statistics N Minimum Maximum Mean Std. Deviation Skewness Kurtosis Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error JS Mgt WL Slr Adv Valid N (listwise) Question: 1. Is there variation in our data? 2. What is the level of the phenomenon we are measuring?

45 Table in Report Mean Std. Deviation Job Satisfaction Management Work Load Salary Advancement

46 Research Framework 5 items Management (Independent) H1 5 items 3 items Advancement (Independent) H2 Job Satisfaction (Dependent) 4 items Salary (Independent) H3 H4 4 items Workload (Independent)

47 Factor Analysis (FA)- Command

48 Assumptions in FA Question: How valid is our instrument? KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy..890 Approx. Chi-Square Bartlett's Test of Sphericity df 120 Sig..000 KMO should be > 0.5 Bartlett s Test should be significant ie; p < 0.05

49 Measure of Sampling Adequacy MSA Comment 0.80 and above Meritorious Middling Mediocre Miserable Below 0.50 Unacceptable

50 Assigning Questions Communalities Initial Extraction Rotated Component Matrix a Component Management Management Management Management Management WorkLoad WorkLoad WorkLoad Workload Salary Salary Salary Salary Advancement Advancement Advancement Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 6 iterations. Management Management Management Management Management WorkLoad WorkLoad WorkLoad Workload Salary Salary Salary Salary Advancement Advancement Advancement Extraction Method: Principal Component Analysis. Amount of shared, or common variance, among the variables General guidelines all communnalities should be above 0.5

51 Significant Loadings Factor Loading Sample Size Needed

52 How many Factors? Total Variance Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Cumulative Total % of Cumulative Total % of Cumulative Variance % Variance % Variance % Extraction Method: Principal Component Analysis.

53 How many Factors? - Scree Plot

54 Reliability - Command

55 Question: How reliable are our instruments? Reliability Statistics Cronbach's N of Items Alpha Should be preferably > 0.3 Item-Total Statistics Scale Mean if Scale Variance Corrected Item- Cronbach's Item Deleted if Item Deleted Total Alpha if Item Correlation Deleted Management Management Management Management Management

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

57 Computing New Variable - Command

58 Data after Transformation

59 Inferential Analysis

60 Chi Square Test - Command

61 Question: Crosstabulation Is level of sharing dependent on gender? Gender * Inten tion Level Cr osstabu lation Gender Total Male Female Count % wit hin Gender % within Intention Lev el % of Total Count % wit hin Gender % within Intention Lev el % of Total Count % wit hin Gender % within Intention Lev el % of Total Intention Lev el 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 Test Linear-by-Linear Association N of Valid Cases Chi-Square Tests Asy mp. Sig. Value df (2-sided) b a. Computed only f or a 2x2 table b. Exact Sig. (2-sided) Exact Sig. (1-sided) cells (.0%) hav e expected count less than 5. The minimum expected count is

62 T-test - Command

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

64 Paired t-test - Command

65 Question: t-test (2 Dependent) Are there differences between intention to share and actual sharing behavior? Paired Samples Statistics Pair 1 Intention Actual Std. Std. Error Mean N Dev iation Mean Paired Samples Correl ations Pair 1 Intention & Actual N Correlation Sig Paired Samples Test Pair 1 Intention - Actual Paired Diff erences 95% Confidence Interv al of the Std. Std. Error Diff erence Mean Dev iation Mean Lower Upper t df Sig. (2-tailed)

66 One Way ANOVA - Command

67 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 Position Engineer Manager Technician Sr Engineer Abov e manager Sig. Intentio n Subset f or alpha =.05 N Means f or groups in homogeneous subsets are display ed. a. Uses Harmonic Mean Sample Size = b. The group sizes are unequal. The harmonic mean of the group sizes is used. Ty pe I error levels are not guaranteed.

68 Kruskal-Wallis - Command

69 Kruskal-Wallis (k independent) Question: Does the variables vary by position? Ranks Intention Position Technician Engineer Sr Engineer Manager Abov e manager Total N Mean Rank Test Statistics a,b Chi-Square df Asy mp. Sig. Intention a. Kruskal Wallis Test b. Grouping Variable: Posit ion

70 Correlation - Command

71 Correlation (Interval/ratio) Question: Are the variables related? Attitude subjectiv e 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 Correlati ons **. Correlation is signif icant at the 0.01 lev el (2-t ailed). At tit ude subjectiv e Pbcontrol Intention Actual 1.697**.212**.808**.606** ** **.552** ** ** **.653**.281** 1.817** **.552** **

72 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

73 Regression - Command

74 Multiple Regression Question: Which variables can explain the intention to share? Model 1 Variables Entered/Removed b Variables Variables Entered Remov ed Method Pbcontrol, subjectiv e, Attitude a. Enter a. All requested v ariables ent ered. b. Dependent Variable: Intent ion R square how much of the variance in the dependent variable is explained by the model 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

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

76 Regression Equation

77 Thank you