Professor Andrew Olu Fadoju University of Ibadan

1. OUTLINE OF DATA ANALYSIS, PRESENTATION, DISCUSSION, SUMMARY CONCLUSION AND RECOMMENDATIONS IN A RESEARCH PROJECT REPORT IN ENVIRONMENTAL HEALTH Professor Andrew Olu Fadoju University of Ibadan <fadoju.andrew@dlc.ui.edu.ng>

2. DATA ANALYSIS AND REPORT WRITING: Data Preparation • The data, after collection, has to be prepared for analysis. • The collected data is raw and it must be converted to the form that is suitable for the required analysis. • The results of the analysis are affected by the form of the data. So, proper data preparation is a must to get reliable results

3. Data Preparation Process • Questionnaire checking • Editing • Coding • Classification • Tabulations • Graphical representation • Data cleaning • Data adjusting.

4. SCALES OF MEASUREMENT: Statistical data analysis depends on several factors such as: • the type of measurement scale used • the sample size • sampling technique used • the shape of the distribution of the data.

5. Types of Scales of Measurement • There are typically four scales or levels of measurement that are defined : • Nominal • Ordinal • Interval • Ratio

6. QUANTITATIVE DATA ANALYSIS:Parametric and Non-Parametric Techniques • Parametric statistical test is one that makes assumptions about the parameters (defining properties) of the population distribution(s) from which one's data are drawn…….. • Non-Parametric Techniques • is one that makes no such assumptions. In this strict sense, "non-parametric" is essentially a null category, since virtually all statistical tests assume one thing or another about the properties of the source population(s).

7. QUANTITATIVE DATA ANALYSIS........ Parametric Techniques • The conditions in Parametric Techniquesare as follows: • The sample size is greater than 30. • Data are normally distributed. • Data are measured in interval or ratio scales. • Variances of different sub-groups are equal or nearly equal. • The sample is selected randomly. • Observations are independent.

8. QUANTITATIVE DATA ANALYSIS........ Non-Parametric Techniques • Does NOT subject itself to the conditions of: • The sample size being greater than 30. • Data being normally distributed. • Data can be measured on nominal and ordinal scales • Variances of different sub-groups don’t have to be equal or nearly equal. • Randomisation is not vital. • Observations may not be independent

9. Descriptive Data Analysis • Descriptive statistics are used to present quantitative descriptions in a manageable form. Measures of Central Tendency • Mean • Median • Mode Measures of Variability • the standard deviation • skewness • and kurtosis.

10. Descriptive Data Analysis Fiduciary Limits • These indicates the interval (or the fiduciary limits) within which the Mean of the population will lie at 0.95 or 0.99 levels of confidence. Graphical Presentation of Data • bar diagrams, • pie chart • line graph

11. Bar and Pie Charts

12. Line Graph

13. Line Graph Contd.

14. Inferential Data Analysis • Descriptive data analysis only describes the data and the characteristics of the sample in terms of statistics. • Its findings could not be generalised to larger population. • BUT the findings of the inferential analysis can be generalised to larger population. Use of Excel in Data Analysis • The MS-Excel is an excellent tool for analysing data using statistical techniques descriptive statistics such as the Mean, Medial, Mode, SD, Skewness and Kurtosis and inferential techniques including t-test, ANOVA, correlation. It also helps in presenting data graphically through bar diagrams, line graph and pie chart.

15. Concepts, Use and Interpretation of Statistical Techniques Correlation • When the variables are in the interval or ratio scale, correlation and regression coefficients are computed. • A Pearson product-moment correlation coefficient is a measure of linear association between two variables in interval or ratio scale.

16. Correlation Contd…… • Interpretation of “r”: This takes into account four major aspects as follows: • a. Level of significance (usually at 0.01 or 0.05 levels in behavioural research). • b. Magnitude of ―r: In general, the following forms the basis of interpreting the magnitude of the ―r:

17. Correlation contd……..

18. Correlation contd…….. • c. Direction of ―r: The ―r could be positive, negative or zero.   • d. The Coefficient of Determination: It refers to the percentage of variability in one variable that is associated with variability in the other variable.

19. t-test • A t-test is used to compare the Mean Scores obtained by two groups on a single variable. • It is also used when F-ratio in ANOVA is found to be significant and the researcher wants to compare the Mean scores of different groups included in the ANOVA. Types of t-test • (a) Independent one-sample t-test • (b) Independent two-sample t-test • (c) Dependent t-test for paired samples

20. Alternatives to the t test • The t test can be used to test the equality of the means of two normal populations with unknown, but equal, variance. To relax the normality assumption, a non-parametric alternative to the t test can be used and the usual choices are: • for independent samples, the Mann-Whitney U test • for related samples, either the binomial test or the Wilcoxin signed-rank test. To test the equality of the means of more than two normal populations, an analysis of variance can be performed. z-test • It is used to compare two coefficients of correlation.

21. ANOVA • Analysis of variance (ANOVA) is used for comparing more than two groups on a single variable. • It a collection of statistical models and their associated procedures, in which the observed variance is partitioned into components due to different explanatory variables. • In its simplest form ANOVA gives a statistical test of whether the means of several groups are all equal, and therefore generalizes.

22. ANOVA CONTD....... There are three conceptual classes of such models: • Fixed-effects model assumes that the data came from normal populations which may differ only in their means. • Random-effects model assumes that the data describe a hierarchy of different populations whose differences are constrained by the hierarchy. • Mixed-effect model describes situations where both fixed and random effects are present.

23. Types of ANOVA • One-way ANOVA: It is used to test for differences among two or more independent groups. • One-way ANOVA for repeated measures: It is used when the subjects are subjected to repeated measures. This means that the same subjects are used for each treatment. • Two-way ANOVA: It is used when the researcher wants to study the effects of two or more independent or treatment variables. it is also known as factorial ANOVA.

24. MANOVA • When one wants to compare two or more independent groups in which the sample is subjected to repeated measures such as pre-test and post-test in an experimental study, one may perform a factorial mixed-design ANOVA • Multivariate Analysis of Variance or MANOVA in which one factor is a between-subjects variable and the other is within-subjects variable. This is a type of mixed-effect model. It is used when there is more than one dependent variable.

25. ANCOVA ANCOVA: While comparing two groups on a dependent variable, if it is found that they differ on some other variable such as their IQ, SES or pre-test, it is necessary to remove these initial differences. This can be done through using the technique of ANCOVA.   Assumptions of Using ANOVA • 1. Independence of cases. • 2. Normality of the distributions in each of the groups. • 3. Equality or homogeneity of variances, known as homoscedasticity i.e. the variance of data in groups should be the same. Levene‘s test for homogeneity of variances is typically used to confirm homoscedasticity. • The Kolmogorov-Smirnov or the Shpiaro-Wilk test may be used to confirm normality. • According to Lindman, F-test is unreliable if there are deviations from normality whereas Ferguson and Takane (2003) claim that the F-test is robust.

26. Chi-square • A chi-square test (χ2 test) is any statistical test in which the test statistic has a chi-squared distribution when the null hypothesis is true • Or any distribution in which the probability distribution of the test statistic (assuming the null hypothesis is true) can be made to approximate a chi-square distribution as closely as desired by making the sample size large enough. • Chi-square is a statistical test commonly used to compare observed data with the expected data according to a specific hypothesis.

27. Methods of Testing of Hypothesis Verification: verifying whether the inferences reached from the propositions are consistent with the observed facts. It can be: • (a) Direct Verification by Observation or Experimentation and • (b) Indirect Verification by deducing consequences from the supposed cause and comparing them with the facts of experience. ExperimentumCrucis: This is known as crucial instance or confirmatory test. Consilience of Inductions

28. Errors in Testing of Hypothesis: • A researcher tests the null hypothesis using some statistical technique. • Based on the test of statistical significance he / she accepts or rejects the null hypothesis and thereby either rejects or accepts the alternate hypothesis respectively. • If the null hypothesis is true and is accepted or when it is false and is rejected, the decisions taken are true. However, error in testing of hypothesis occurs under the following two situations: • (i) If the null hypothesis (H0) is true but is rejected and (ii) If the null hypothesis (H0) is false but is accepted

29. QUALITATIVE DATA ANALYSIS Meaning... • .......the array of processes and procedures whereby a researcher provides explanations, understanding and interpretations of the phenomenon under study on the basis of meaningful and symbolic content of qualitative data

30. QUALITATIVE DATA ANALYSIS CONTD.

31. QUALITATIVE DATA ANALYSIS CONTD.

32. Principles of Qualitative Data Analysis • Proceeding systematically and rigorously (minimise human error). • Recording process, memos, journals, etc. • Focusing on responding to research questions. • Identifying appropriate level of interpretation suitable to a situation. • Simultaneous process of inquiry and analysis. • Seeking to explain or enlighten. • Evolutionary/emerging

33. THE PROCEDURES OF QUALITATIVE DATA ANALYSIS • Coding/indexing • Categorisation • Abstraction • Comparison • Dimensionalisation • Integration • Iteration: repeating a process • Refutation (subjecting inferences to scrutiny) • Interpretation (grasp of meaning - difficult to describe procedurally)

34. STRATEGIES OF QUALITATIVE DATA ANALYSIS • Analytical Induction: a way of building explanations in qualitative analysis by constructing and testing a set of causal links between events, actions etc. • Constant Comparison: ways of approaching your data so that you can do the coding of the data with an open mind and recognize noteworthy patterns in the data. • Triangulation: a term originally associated with surveying activities, map making, navigation and military practices. Three known objects or points used to draw sighting lines towards an unknown point or object.

35. Let your Research work be Genuine

36. RESEARCH REPORTING • Research results/findings are shared and communicated to others for dissemination of knowledge. • On completion of research activities, the researcher has to report the entire research process systematically in writing. • For clear and easy understanding of readers, writing a good research report requires knowledge of the types of research reporting, rules for writing and typing, format and style of research reporting and the body of the report. • However, scholarship, precision of thought and originality of a researcher cannot be undermined in producing a good research report.

37. Always Acknowledge the Frontiers of Knowledge in your Discipline

38. Closing...............

39. Finally………….

40. Finally! Finally!!

41. Let us Discuss Now…………..