RESEARCH STATISTICSRESEARCH STATISTICS Never Ever Give Up ! Data Analysis Basic with SPSS (PART 1) DR CHUA YAN PIAW (UM) DR CHUA YAN PIAW (UM) Prof. Dr Chua Yan Piaw Institute of Educational Leadership (IEL,UM) Unit for the Enhancement of Academic Performance (ULPA,UM) University of Malaya 1. Concept of Data analysis 2. Prepare Data for Analysis 3. Checking the normality of a data RESEARCH STATISTICS RESEARCH STATISTICS Targets: Research Methods and Statistics Reference Books 4. Establishing reliability of a questionnaire DR CHUA YAN PIAW (UM) DR CHUA YAN PIAW (UM) 5. Descriptive analysis 6. Inferential analysis 7. Analyse data and report the data 1 Volume 2: Fundamental Research Statistics Volume 1: Research Methods Chapter 1: Introduction to Research Chapter 2: Research Ethics Chapter 3: Literature Review Chapter 4: Research Design Chapter 5: Experimental Study Chapter 6: Quasi-experimental Study 2006 Chapter 7: Survey Study Chapter 8: Field Study Chapter 9: Case Study Chapter 10: Action Study Chapter 11: Historical Study Chapter 12: Probability Sampling Procedures Chapter 13: Non-probability Sampling Procedures Chapter 14: Measurement in Research Chapter 15: Index. Scales and Specific Measurement Procedures Chapter 16: Pilot Study Chapter 17: Research Instrumentation Chapter 18: Format of Writing Research Report 2011 Chapter 1: Descriptive Statistics Chapter 2: Inferential Statistics and Significance Test Chapter 3: Qualitative Data Analysis Chapter 4: Data Preparation for SPSS Program Chapter 5: Reliability of Research Instrument Chapter 6: Chi-Square Tests 2012 Chapter 7: T Tests Chapter 8: ANOVA Tests Chapter 9: Correlation Tests Chapter 10: Multiple Regressions Chapter 11: Reporting the Results of Data Analysis Based on the APA Format 2015 2006 2015 Volume 4: Advanced Research Statistics: Univariate and Multivariate Tests Volume 3 (2nd edition): Fundamental Research Statistics: Data Analysis for Likert Scale Chapter 1: Measurement Scales and Statistical Test Chapter 2: Data Preparation for SPSS Program Chapter 3: Data Transformation Chapter 4: Mann-Whitney U Test Chapter 5: Wilcoxon T Test Chapter 6: Kruskal-Wallis H Test Chapter 7: Friedman Test Chapter 8: Spearman Correlation Test Chapter 9: Contingency Table Data Analysis Chapter 10: Cramer V Correlation Test Chapter 11: Reporting the Results of Data Analysis Based on the APA Format 2008 2013 Chapter 1: Research Statistics Concept and Data Preparation for SPSS Program Chapter 2: One-Way ANOVA Test Chapter 3: Two-Way ANOVA Test Chapter 4: SPANOVA Test Chapter 5: ANCOVA Test Chapter 6: Independent Samples MANOVA Test Chapter 7: Repeated Measures MANOVA Test Chapter 8: MANCOVA Test Chapter 9: Trend Analysis Chapter 10: Method of Writing High Impact Journal Paper 2009 2014 2 . Volume 5: Advanced Research Statistics: Regression Test. Factor Analysis and Structural Equation Modeling Analysis Mastering Research Methods Chapter 1: Data Preparation for SPSS Program Chapter 2: Partial Correlation Test Chapter 3: Hierarchical Multiple Regressions Analysis Chapter 4: Hierarchical Binary Logistics Analysis Chapter 5: Log-Linear Analysis Chapter 6: Factor Analysis Chapter 7: Discriminant Analysis Chapter 8: Cluster Analysis and Chapter 9: Structural Equation Modeling Analysis Using AMOS 2009 Chapter 1: Introduction to Research Chapter 2: Research Ethics Chapter 3: Literature Review Chapter 4: Research Design Chapter 5: Experimental Study Chapter 6: Quasi-experimental Study Chapter 7: Survey Study Chapter 8: Field Study Chapter 9: Case Study Chapter 10: Action Study Chapter 11: Historical Study Chapter 12: Probability Sampling Procedures Chapter 13: Non-probability Sampling Procedures Chapter 14: Measurement in Research Chapter 15: Index. Scales and Specific Measurement Procedures Chapter 16: Pilot Study Chapter 17: Research Instrumentation Chapter 18: Format of Writing Research Report 2014 2011 Mastering Research Statistics Concept of Data analysis Chapter 1: Descriptive Statistics Chapter 2: Inferential Statistics and Significance Test Chapter 3: Qualitative Data Analysis Chapter 4: Data Preparation for SPSS Program Chapter 5: Reliability of Research Instrument Chapter 6: Chi-Square Tests Chapter 7: T Tests Chapter 8: ANOVA Tests Chapter 9: Correlation Tests Chapter 10: Multiple Regressions Chapter 11: Multiple Responses Analysis Chapter 12: Reporting the Results of Data Analysis Based on the APA Format Amazon.com or MPH Online 3 . the researcher will find difficult to analyse the data after collecting it. 4 . No sample is drawn from the population. Types of study: a. Sample size How to recognize whether the study is a descriptive study or an inferential study? In a descriptive study. 1. Type of Statistical tests Factor 3. Type of measurement scales in an instrument DR CHUA YAN PIAW (UM) DR CHUA YAN PIAW (UM) Planning a design without the knowledge of statistics. RESEARCH STATISTICS RESEARCH STATISTICS Research design and statistics Factor 3. Descriptive study DR CHUA YAN PIAW (UM) DR CHUA YAN PIAW (UM) b.A quantitative researcher should understand statisics before planning his research design. Type of study RESEARCH STATISTICS RESEARCH STATISTICS Factor 1 Research Statistics Factor 2. Inferential study Determined by several factors: Factor 1. Respondents are the whole population of the study. The results/ findings are not generalised to any subject outside the population . 3. It represents the motivation level of the students DR CHUA YAN PIAW (UM) Median Frequency Mode Percentage Mean Standard deviation Distribution of Scores The results are nearly 100% correct for that population. So no inferential statistical test is needed. Descriptive statistics describes the characteristics (variables) in the population.X X DR CHUA YAN PIAW (UM) •No sample is drawn from the population. 1. No sample is drawn from the population. 2. Calculate the mean score. Respondents are the whole population of the study. Collect data (motivation score) from the students. RESEARCH STATISTICS For example: Motivation level of the students. RESEARCH STATISTICS A descriptive study In a descriptive study. Did I select a sample? Can I generalize the result to other group? Students from other universities? The result is nearly 100% correct (if the measurement is reliable and is correctly done). 5 . so the results is not generalised. Results from the sample is generalised to the population. RESEARCH STATISTICS RESEARCH STATISTICS Determine the sample size Study is conducted on the sample. For an inferential study: An inferential study Statistical test is needed to generalised the results. A sample DR CHUA YAN PIAW RESEARCH STATISTICS B. to form a sample. Respondents are subjects selected randomly from a population. Inferential statistics Tests of differences T tests Mann-Whitney U N = Population size ANOVA tests Chi-square tests S = Sample size Tests of relationship DR CHUA YAN PIAW (UM) DR CHUA YAN PIAW (UM) Krejcie & Morgan (1970) A population Kruskal-Wallis test Pearson r Spearman rho Cramer V 6 . Statistics tests is used to analyse data collected from the sample. The results / findings are generalised back to the population from where the sample was selected. *Nominal scale 1= Male 2= Male . 13.*Interval / Ratio scale Math Score: 1. 12.000.distances among scales are identical Temperature : 50C.Rank . 4= Agree. 1. no inferential test / test of significance is needed. 7.*Nominal scale .categories/groups of data 2= Female 1= Female Overweight: Climate change phenomenon: Ethnicity: 1= Yes 2= No 3= Unsure 1= Pollution 2= Fires 3= Drought 4= Flood 1= M 2= C 3= I 1= M 2= I 3= C 2.distances among scales are identical Questionnaire / other quantitative data measurement devices/ tests Types of analysis is determined by the Scales of measurement in an instrument Scales of measurement Gender: Gender: 1. 2= Disagree. 42 yo… 130C.distances among scales are different Level of agreement 1= Strongly disagree.000.*Interval / Ratio scale . 420C… Income: 5. 26… .*Ordinal scale . Age: 5.For a descriptive study. 4…100 3. test of significance is needed to generalise the result to the population.categories/groups of data RESEARCH STATISTICS RESEARCH STATISTICS SCALES OF MEASUREMENT DR CHUA YAN PIAW (UM) DR CHUA YAN PIAW (UM) For an inferential study (a sample is used).*Ordinal scale . 5= Strongly agree Income: 1= < $1000 2= $1001-3000 3 = >$3000 Attitude towards sport management 1= Negative 2= Neutral 3= Positive Score: 0. 120C. 13.distances among scales are different DR CHUA YAN PIAW (UM) DR CHUA YAN PIAW (UM) 3. 2. … 7 .Rank . 12. 2. 3= Undecided. 3. RESEARCH STATISTICS RESEARCH STATISTICS Summary: Factor 2: Types of measurement in an instrument Research Instruments : 1. No What kind of measurement for this data? The most appropriate price for an original DVD is: Less than RM 5 ………………….. Under 21 93 77 44 66 88 85 55 3=$41-$80 55 4>$80 Human Activities 1.5 Yearly Incomes (X$1.…….……3 RM 21 to RM 30 ………………. Big 2.000) 24 76 35 58 94 1 = <$20 Your attitude towards the performance of our sport school is positive.. 2 3 4 5 Strongly disagree DR CHUA YAN PIAW (UM) DR CHUA YAN PIAW (UM) 1 Do you agree that the main cause of obesity is too much intake of oily foods. 1. Small Sport team 1.. The main factor of air pollution at your place is: A.Agriculture 4.Urbanisation 2. Chemicals D..4 More than RM 30 …………….Continuous data? No Are the distances among scales identical? Can the data be categoried? Example: male/female Yes No Yes Interval / ratio scale Ordinal scale Nominal scale 1..Fisheries 8 . Under 18 3. Under 16 2. Fertilizers 2... Oil C.Industry 5. Heavy metals B.Tourism 3..…1 RM 5 to RM 12 ………….……………2 RM 13 to RM 20 …………. RESEARCH STATISTICS RESEARCH STATISTICS Statement DR CHUA YAN PIAW (UM) DR CHUA YAN PIAW (UM) Yes RESEARCH STATISTICS RESEARCH STATISTICS Identifying the scale of a measurement 2 = $20-$40 Company type 1. Yes 2.…. Strongly agree 3.……. exp: 34.agree . The Difference between male and female residents: use the parametric test .T test Strongly disagree . 34. 55) Level of agreement Non-parametric tests are used: Parametric tests are used: data: Ordinal scale (1=Very low. 33. 2=Low.RESEARCH STATISTICS RESEARCH STATISTICS Scales of measurement Interval and Ratio data are parametric data (the data are assumed normal distributed) Example: Maths scores of a class DR CHUA YAN PIAW (UM) Nominal and Ordinal data are nonparametric data (the data are assumed not normal distributed) RESEARCH STATISTICS RESEARCH STATISTICS Scales of measurement Example: Stress level of the residents during air pollution period data: Ratio scale (stress score. 5=Very high) The Difference between male and female residents: use the nonparametric test . etc 8. 67. 45.disagree -------.undecided -------. 3=Average. 4=High. 56. 78.strongly agree 1 2 3 4 5 DR CHUA YAN PIAW (UM) DR CHUA YAN PIAW (UM) Chi-square tests (Nominal/ordinal data) Mann-Whitney test (Ordinal data) Kruskal-Wallis test (Ordinal data) Spearman rho Correlation test (Ordinal data) Cramer V correlation test (Nominal data) etc T tests. 23.Mann-Whitney test 9 . ANOVA tests Pearson Correlation test. Two assumption are: 1. Data for categorical data are notnormally distribued. Each sub-sample => 5 Any question? DR CHUA YAN PIAW (UM) DR CHUA YAN PIAW (UM) In inferential statistics. Each sub-sample: n =>15 RESEARCH STATISTICS RESEARCH STATISTICS Factor 3: Sample size RESEARCH STATISTICS Take 5? DR CHUA YAN PIAW (UM) 10 . The data for continouos data (interval / ratio) is normal distributed when n => 30.2.