ATAF/ATRN RESEARCH METHODS WORKSHOP QUANTITATIVE … · 6 Descriptive Statistics – Measures of...
Transcript of ATAF/ATRN RESEARCH METHODS WORKSHOP QUANTITATIVE … · 6 Descriptive Statistics – Measures of...
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ATAF/ATRN RESEARCH METHODS WORKSHOP
QUANTITATIVE METHODS - Basic Tools and Principles for Data Analysis
Presented by: Alex Oguso
June 3 -6, 2019
Kenya School of Monetary Studies (KSMS)
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OUTLINE Descriptive statistics
Inferential Statistics
Hypothesis Testing
Simple Regression model
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Descriptive Statistics – Definitions
Variable – a characteristic or attribute of a unit or subject that can assume different values. The values that a variable can assume are called data.
Dependent/explained/endogenous vs independent/explanatory/exogenous variables
Discrete variable – has a basic unit of measurement that cannot be subdivided e.g number of taxpayers in a country
Continuous variable – unit of measurement can be subdivided infinitely e.g amount of tax payments.
Parameter: value that describes a population
Statistic: a value that describes a sample
Parametric tests - Assume data follows a particular distribution e.g. normal distribution
Non-parametric test - Nonparametric techniques are usually based on ranks/ signs rather than actual data
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Descriptive Statistics
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Descriptive Statistics – Measures of Central Tendency
Mean – is the arithmetic average of all observations
Median – the middle value in the data (50% cases above/50% cases
below). Is insensitive to extreme cases (outliers)
Mode – the most frequently occurring observation
Quartiles/Percentiles - Split Ordered Data into 4 Quarters or percentages
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Descriptive Statistics – Measures of Dispersion
• Range – difference between the smallest and the largest observation in a dataset
• Variance – average deviation of observations from the mean. Based on
sum of squared deviations from the mean:
High variance means that most scores are far away from the mean.
Low variance indicates that most scores cluster tightly about the mean
• Standard Deviation – square root of variance:
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Descriptive Statistics – Measures of Symmetry
Skewness
When most of the scores in a distribution land on one side of the scale or the other; not symmetrical; typically have a tail on one of the scale or the other.
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Descriptive Statistics – Measures of Asymmetry
Kurtosis
• If a distribution is symmetric, the next question is about the central peak: is it
high and sharp, or short and broad?
• A high kurtosis distribution has a sharper "peak" and fatter "tails", while a low kurtosis distribution has a more rounded peak with wider "shoulder”
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Descriptive Statistics – Statistical Graphs
• Graphs for numerical data:
- Histograms
- Frequency polygons
- Pie Charts
• Graphs for categorical data
- Bar graphs
- Pie Charts
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Inferential Statistics
• Involves use of information from samples to make inferences about the population
• Population - the total collection of all cases that the researcher wishes to understand better.
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Hypothesis Testing
• A hypothesis is an assumption about the population parameter. Hypotheses are mutually exclusive & exhaustive
Hypothesis Testing Steps
(i) State Null and alternative hypotheses
(ii) Compute the Test statistic (e.g sample mean)
(iii) P-value and interpretation (can be one sided or two-side P-value)
(iv) Significance level (optional) – e.g 95% level of significance
Hypothesis Testing - Example
• Null Hypothesis, H0 = States the Assumption to be tested e.g. The Presumptive tax compliance rate in Nairobi is not higher than the Country’s average (H0: 𝒙 < 50%)
• Alternative Hypothesis, H1 = Is the opposite of the null hypothesis e.g. The Presumptive tax compliance rate in Nairobi is higher than the Country’s average (H1: 𝒙 > 50%)
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Hypothesis Testing…
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Hypothesis Testing – Errors in Making Decisions
Type I Error - reject Null Hypothesis yet it is true
Type II Error – fail to reject Null Hypothesis yet it is false
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Practical Session
Simple regression model:
1. Running a simple regression on Excel & Eviews
2. Hypothesis testing using the regression results
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Q & A Session
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References • Henk Van Elst, H. (2013). Foundations Of Descriptive And Inferential Statistics. Lecture notes for the
Bachelor degree programmes, Karlshochschule International University, Karlsruhe, Germany.
• Fedor-Freybergh, P.G. & Mikulecky, M. (2005). From the descriptive towards inferential statistics: Hundred years since conception of the Student‘s t-distribution. Neuroendocrinology Letters No.3 Vol.26, June 2005.