ICT & DATA ANALYSIS Prof Emmanuel N. Aguwa

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

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

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 ordered e.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.

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

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.

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

REMEMBER

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.

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

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

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

Frequency Tabulations

Bar Chart 1

Bar Chart 2

Histogram

Bar Chart Vs Histogram

Pie Chart

Line Graph

Cross-tabulation

TEST OF ASSOCIATION

Chi Square (2 ) Test

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

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?

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 ----------?

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.

Correlation Example See scatterplot of data

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

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

Correlation – strength and direction

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

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.

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.

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

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

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

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

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

Hours studying and grades

Predict the final grade of… Predicted final grade in class = 59.95 + 3.17*(hours of study) Predict the final grade of… Someone who studies for 12 hours Final grade = 59.95 + (3.17*12) Final grade = 97.99 Someone who studies for 1 hour: Final grade = 59.95 + (3.17*1) Final grade = 63.12

Exercise A sample of 6 persons was selected the value of their age ( x variable) and their weight is demonstrated in the following table. Find the regression equation and what is the predicted weight when age is 8.5 years.

Weight (y) Age (x) Serial no. 12 8 10 11 13 7 6 5 9 1 2 3 4

Multiple Regression Multiple regression analysis is a straightforward extension of simple regression analysis which allows more than one independent variable.

Common descriptive statistics Count (frequencies) Percentage Mean Mode Median Range Standard deviation Variance Ranking ANOVA

NOTE: For measures of central tendency The median is usually preferred to other measures of central tendency when your data set is skewed (i.e., forms a skewed distribution) or you are dealing with ordinal data. However, the mode can also be appropriate in these situations, but is not as commonly used as the median.

Standard Distribution and Normal Distribution Curve

ANOVA One-Way ANOVA – similar to a t-test, except that this test can be used to compare the means from THREE OR MORE groups (t-tests can only compare TWO groups at a time, and for statistical reasons it is generally considered “ illegal” to use t-tests over and over again on different groups from a single experiment) - Stats for dummies

Two-Way ANOVA – A very useful statistical test, because it’s the only one that allows you to compare the means of TWO OR MORE groups in response to TWO DIFFERENT INDEPENDENT VARIABLES. With this test available, you can set up an experiment in which each member of your sample is exposed to varying level of two different treatments. In a field study, this test allows you to compare a mean Response Variable relative to two different environmental conditions.

Analyzing and Interpreting Qualitative Data Qualitative data is thick in detail and description. Data often in a narrative format Data often collected by observation, open-ended interviewing, document review Analysis often emphasizes understanding phenomena as they exist, not following pre-determined hypotheses

Analyzing qualitative data “Content analysis” steps: Transcribe data (if audio taped) Read transcripts Highlight quotes and note why important Code quotes according to margin notes Sort quotes into coded groups (themes) Interpret patterns in quotes Describe these patterns

Ensuring Validity in Qualitative Analysis Be systematic Use multiple raters Attend to context (e.g. keep track of who said what) Account for outlying and surprising statements Triangulate

Types of Qualitative Analysis Content analysis Narrative analysis Discourse analysis Framework analysis Grounded theory

Meta-Analysis Definition. A subset of systematic reviews; a method for systematically combining pertinent qualitative and quantitative study data from several selected studies to develop a single conclusion that has greater statistical power.

Top Free Analysis Software http://www.predictiveanalyticstoday.com/top-data-analysis-software/ ELKI, Dataiku, DSS, ITALASSI, R, Data Applied, DevInfo, Tanagra, Waffles, Weka, Gephi, OpenRefine, Fusion Tables, DataMelt, Orange, Wrangler Encog, RpaidMiner, PAW, SCaVi, ILNumerics.Net, ROOT, Julia, MOA, NumPy, SciPy, KNIME, NetworkX, matplotlib, Ipython, SymPy, Scilab, FreeMat, jMatlab, NodeXLab, NodeXL Basic, Fluentd, and Tableau Public FOR QUALITATIVE ANALYSIS: ATLAS.ti, Nvivo, Qiqqa, QDA Miner,Saturate, Annotations, webQDA, f4analyse, HyperRESEARCH, MAXQDA, Xsight, Quirkos, Dedoose, Focuss On, Raven’s Eye, etc

Resource materials Data Analysis, Interpretation and Presentation: http://www.uio.no/studier/emner/matnat/ifi/INF4260/h10/undervis ningsmateriale/DataAnalysis.pdf Analyzing and interpreting data. Matt Calvert, UW-Extension Youth Development Specialist. PDAC Wisline Web www.uwex.edu/ces/4h/evaluation/documents/dataanalysis.ppt Wahab MMA. Correlation and Regression. www.pitt.edu/~super4/33011-34001/33851.ppt

Recommended Materials SPSS Step-by-Step: http://www.datastep.com/SPSSTutorial_1.pdf A Handbook of Statistical Analysis using SPSS by Sabine Landau and Brian S. Everitt: http://www.academia.dk/BiologiskAntropologi/Epidemiologi/PDF/SP SS_Statistical_Analyses_using_SPSS.pdf https://www.coursera.org/learn/data-analysis-tools (enrolment starts 31st Oct)

Questions or Comments Practice Session – Introduction to Epi Info and SPSS