ICT & DATA ANALYSIS

1. ICT & DATA ANALYSIS ProfEmmanuel N. Aguwa

2. Study Objectives • At the end of this presentation, the students should be able to: • - define data • -identify types of data • -analyze different types of data • -understand the ICT tools used in data analysis • -understand basic concepts in SPSS and EPI INFO

3. What are data? • Facts or information used usually to calculate, analyze, or plan something • Information output by a sensing device or organ that includes both useful and irrelevant or redundant information that must be processed to be meaningful

4. Types of data • QUALITATIVE DATA is a categorical measurement expressed not in terms of numbers, but rather by means of a natural language description. • Nominal Categories: When there is not a natural ordering of the categories. Examples are gender, race, religion, or sport. • Ordinal Variables: When the categories may be orderede.g.small, medium, large, etc.). Attitudes (strongly disagree, disagree, neutral, agree, strongly agree) are also ordinal variables. • Note that the distance between these categories is not something we can measure.

5. QUANTITATIVE DATA is a numerical measurement expressed not by means of a natural language description, but rather in terms of numbers. However, not all numbers are continuous and measurable. For example, the social security number is a number, but not something that one can add or subtract. • Quantitative data are always associated with a scale measure. • Quantitative : Discrete or continuous

6. What is Data Analysis? • Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.

7. Why do we analyze data? • The purpose of analyzing data is to obtain usable and useful information. The analysis, irrespective of whether the data is qualitative or quantitative, may: • •describe and summarize the data • •identify relationships between variables • •compare variables • •identify the difference between variables • •forecast outcomes

8. REMEMBER

11. Data analysis plan Early in the research process we need to develop a data analysis plan: • - To make sure the questions and your data collection instrument will get the information you want. • - To align your desired “report” with the results of analysis and interpretation. • - To improve reliability--consistent measures over time.

12. Key components of a data analysis plan • Purpose of the evaluation • What necessary questions should be asked • What do I hope to learn from the question • Analysis technique • How data will be presented

14. Data entry & analysis by computer (ICT) • By Computer • Excel (spreadsheet) • Microsoft Access (database mgt) • Quantitative analysis: SPSS • Qualitative analysis: Epi info; In ViVo, etc.

17. Analyzing and Interpreting Quantitative Data • Descriptive statistics e.g. Frequencies • Cross-tabulations • Test of significance – Chi Square, Student t-test • Test of Association • - Correlation • -Regression

18. Frequency Tabulations

19. Bar Chart 1

20. Bar Chart 2

22. Histogram

23. Bar Chart Vs Histogram

24. Pie Chart

25. Line Graph

26. Cross-tabulation

27. TEST OF ASSOCIATION

28. Chi Square (2 )Test

32. How to use the Chi-Square Test • Determine null hypothesis • Use formula to calculate 2 • Find critical value using table (Use p = 0.05) • If 2< Critical Value, then ACCEPT null hypothesis. Difference in data are due to chance alone • If 2> Critical Value, REJECT the null hypothesis; Differences in data are NOT due to chance alone

35. Correlational Design = a study that assesses the extent to which two variables are related • Defines the relationship in quantitative terms • Correlational (“co-related”) When one variable changes in value, what happens to the other variable?

36. Correlation Example Is there a relationship between self-esteem and GPA? • Need to have different levels of my first variable: self-esteem Very high self-esteem -------- ? Moderately high self-esteem--? Average self-esteem -----------? Moderately low self-esteem --? Very low self-esteem ----------?

37. Correlation Example Raw Data: Self-esteem score GPA Tim 42 3.8 Bart 10 1.4 Kelsey 15 2.5 Kim 22 3.1 Etc.

38. Correlation Example See scatterplot of data

39. Direction of Correlation • Scatterplot showed a positive correlation • As one variable increased, the second variable also increased • As self-esteem goes up, academic achievement also goes up • Think of some examples of positively correlated variables • Negative (inverse) correlation • As on variable increases, the second variable decreases (i.e. one gets bigger, the other gets smaller) • As amount of alcohol intake increases, motor control decreases • Think of examples of negatively correlated variables = direction of the correlation

40. Strength of Correlation How strongly related are the two variables of interest? • the “sloppiness” of association • Degree of accuracy with which you can make a prediction about 2nd variable given value of the first variable • Ranges from -1 to 1 • -1 and 1 are very strong (perfect) correlations • 0 is no correlation; no relationship

41. Correlation – strength and direction

42. The value of r ranges between ( -1) and ( +1) • The value of r denotes the strength of the association as illustratedby the following diagram. strong intermediate weak weak intermediate strong -1 0 1 -0.75 -0.25 0.25 0.75 indirect Direct perfect correlation perfect correlation no relation

43. If r = Zero this means no association or correlation between the two variables. • If 0 < r < 0.25 = weak correlation. • If 0.25 ≤ r < 0.75 = intermediate correlation. • If 0.75 ≤ r < 1 = strong correlation. • If r = l = perfect correlation.

44. Correlation Example • High Self-esteem and GPA Does (A) lead to (B)? Or is the other way around? Or, are there other factors that lead to both (A) and (B)? • Two independent carefully conducted studies found that there is no causal relationship between these two factors. They are correlated because both of them are correlated to some other factors: intelligence and family social status. • **Correlations do NOT tell us that one variable CAUSES the other variable.

45. Correlational research • Strengths • Can study a broad range of variables • Can look at multiple variables at one time • Large samples are easily obtained • Weaknesses • Relationships established are associational, not causal • Individuals not studies in-depth • Potential problems with reliability and validity of self-report measures

46. Regression Analyses • Regression: technique concerned with predicting some variables by knowing others • The process of predicting variable Y using variable X

47. Regression • Uses a variable (x) to predict some outcome variable (y) • Tells you how values in y change as a function of changes in values of x

48. Correlation and Regression • Correlation describes the strength of a linear relationship between two variables • Linear means “straight line” • Regression tells us how to draw the straight line described by the correlation

49. Regression Equation • Regression equation describes the regression line mathematically • y = a + bx • Where • a = intercept on y axis • b = slope

50. Hours studying and grades