ANALYSING AND INTERPRETING QUANTITATIVE DATA

1. ANALYSING AND INTERPRETING QUANTITATIVE DATA HJ. SHAWAL KASLAM

2. INTRODUCTION ONE OF THE MAJOR THING IN RESEARCH IS THE DATA. THEREFORE UNDERSTANDING THE DATA IS A CRUCIAL PART IN RESEARCH. Some of the fundamental questions have to be considered are: 1. What is the nature of data? 2. How to gather the data? 3. What is the instrument used to gather the data? 4. How to measure the data? 5. How to analyze the data? 6. How to interpret the output?

3. The Objective of this presentation is to describe the fundamental process of analyzing quantitative data. By the end of this session, the participants should be able to:- • describe, combine, and make inferences from numbers. • understand the procedures used to obtain several statistical values. • use, present and interpret the statistical outputs to produce a report. • make conclusion based on the statistical findings.

4. WHAT IS QUANTITATIVE DATA ANALYSIS? QUANTITATIVE DATA ANALYSIS IS A PROCESS OF TRANSFORMING THE RAW DATA OBTAINED FROM QUESTIONNAIRES INTO MEANINGFUL INFORMATION SUCH AS STATISTICAL VALUES [e.g: % value, mean value etc..] AND TO TEST STATISTICAL SIGNIFICANT OF THE DATA.

5. THE STEPS IN DATA ANALYSIS • SELECT THE SOFTWARE [SPSS] • CREATING DATA FILE • KEY IN DATA • EXPLORING DATA • EDITING FILE • ANALYSING DATA • INTERPRETING THE RESULT/OUTPUT

6. DATA ANALYSIS TECHNIQUES AND THE STATISTICAL VALUES DESCRIPTIVE INFERENTIAL PREDICTIVE MEASURE OF FREQUENCY DISTRIBUTION . Percentage value % MEASURE OF CENTRAL TENDENCY . Mean . Mode . Media SKEWNESS & KURTOSIS Exploration of the variables Chi-square Test T-Tests . One Sample Test . Paired T-Test . Independent T-Test ANOVA Z-Test To test statistical significant of the variables Correlation Analysis Regression Analysis To test relationship, statistical significant and predict the impact or changes of the variables

7. BASIC DESCRIPTIVE STATISTICS -used to explore the data collected and to summarise and describe those data. • MEASURES OF FREQUENCY DISTRIBUTION - is a display of the frequency of occurrence of each score value. The frequency distribution can be represented in a tabular form or, with more visual clarity, in graphical form.

9. BASIC DESCRIPTIVE STATISTICS • MEASURES OF CENTRAL TENDENCY – INTERVAL or RATIO DATA STATISTICAL VALUES - MEAN - MODE - MEDIA - STANDARD DEVIATION - MAKSIMUM - MINIMUM

10. MEASURE OF CENTRAL TEDENCY • COMPARE MEAN

11. BASIC DESCRIPTIVE STATISTICS • MEASURES OF SKEWNESS & KURTOSIS – refer to the shape of distribution and are used with interval and ratio level data.

13. CROSS TABULATION – to explore the intersection of two variables BASIC DESCRIPTIVE STATISTIC

14. INTERPRETING BASIC DESCRIPTIVE STATISTICS • BASIC DESCRIPTIVE STATISTICAL VALUES CAN BE USED TO EXPLORE AND EXPLAIN THE RESEARCH QUESTIONS. Example: Research question “is there any different of mean score between the group [male and female] of sample study?

17. INFRENTIAL STATISTICS - tests for difference of means and tests for statistical significance. • The purpose of difference of means tests is to test hypotheses. The most common techniques are called - T-Test - ANOVA - CHI-SQUARE Hypothesis Ho : ∂1 = ∂2 No significant different H1 : ∂1 ≠ ∂2 There is significant different

18. Rule of significant test SPSS • If significant test value ρ < α [0.05 / 0.01] reject Ho [ There is significant difference] • If significant test value ρ > α [0.05 / 0.01] fail to reject Ho [No significant difference] Calculated Test value ρ Critical value α [alfa] By convention, in social science α = .05 or 0.01 CRITERIA OF REJECTION or ACCEPTANCE

19. T-TEST – used to determine whether there is a significant difference between two sets of scores • One-sample T-test – is used when you have data from a single sample of participants and you wish to know whether the mean population from which the sample is drawn is the same as the standard mean. DATA Test score 65 56 58 79 80 65 65 67 68 69 e.g: test whether the mean of students test score is the same as the standard mean = 70

20. The Result from the study is T (29) = -1.008, ρ = 0.322, ρ > 0.05 ∴ Fail to reject Ho Conclusion there is no significant difference between the sample mean of population with the standard mean [Test value].

21. Independent T-Test – is used to test whether the difference between means for the two sets of scores is significant. A study was done to compare job stress between two employee groups (administrative and support). Data were solicited from a randomly selected sample. Test the hypothesis on the difference at .05 level of significance.

22. The result of independent sample T-Test T(18) = .615, ρ = .545 ρ > 0.05 ∴Fail to reject Ho Conclude that there is no significant difference in job stress between administrative and support groups at .05 level of significance.

23. Paired T-Test – used to determine whether the difference between means for the two sets of scores is the same or different. A training program was conducted to improve participants participants’ knowledge on ICT. Data were collected from a selected sample both before and after the ICT training program. Test the hypothesis that the training is effective to improve participants knowledge on ICT at 0.05 level of significant.

24. The result of Paired T-Test T(9) = 4.882, ρ = .001 ρ < 0.05 ∴Reject Ho [Null hypothesis] Conclude that the training program was effective to improve participants knowledge on ICT at .01 level of significance

25. ANOVA One Way Analysis of variance – wish to compare means of more than two groups. • ANOVA is also provide post hoc analysis to determine pair of groups that are significantly difference Data on perception toward management was gathered from a randomly selected sample comprising of three from a randomly selected sample comprising of three employee groups (supervisory, line and support). Test the difference in perception among the three groups at .05 level of significance.

26. The result of INOVA • F (2, 26) = 27.542, p = .000 • Since sig-F (.000) < α (.05) • ∴Reject the null hypothesis • Conclude that there is a significant difference in perception towards management between the three employee groups at .05 level of significance.

27. Post-hoc Analysis – which pair is significance different?

28. Chi-Square Test for independence or relatedness – nonparametric techniques A study was conducted to determine whether job stress is significantly related with employees group. The result Pearson Chi-square = 4.667, p = .862 X2 ( 9, N = 20) = 4.667, p > .05 ∴Fail to reject Ho Conclude that there is no significance relatedness of job stress with employees group.

29. PREDECTIVE STATISTICAL ANALYSIS TECHNIQUES – CORRELATION ANALYSIS • CORRELATION ANALYSIS – used to look at the relationship between two variables in a linear fashion. • The correlation coefficient has a range of possible values from -1 to +1. The value indicates the strength of the relationship, while the sign ( + or - ) indicates the direction.

30. Guildford Rule of Thumb x

31. The result of Correlation analysis R = -.783 Sign r = .000 , p < 0.01 There is a negative and high relationship between anxiety (X) and team cohesiveness (Y)

32. PREDECTIVE STATISTICAL ANALYSIS TECHNIQUES – REGRESSION ANALYSIS • Regression analysis – The result of regression is an equation that represents the best prediction of a dependent variable from several independent variables. • Regression analysis is used when independent variables are correlated with one another and with the dependent variable.

33. The purpose of regression analysis • Determine relationship between one or more IVs and one DV • Predict value of the dependent variable on value of independent variables (X’s)

34. That all, Thank you very much. CONCLUSION Analyzing quantitative data is the most interesting part of a research. It is important that the presentation of the data is effective in bringing the objectives of the study to the forefront and in stating clearly the research outcome.