Data analysis
objective
By the end of this lectures, students should understand Steps of data analysis, and Common statistical tests use to analyze data
Data preparation:
1- Editing data (cleaning): data must be inspected for completeness and consistency / missing not more than 10% of the total response.
2- Coding data: process of converting data into numerical form e.g. male – 1, female – 2.
3- Defining your variables
4- Entering data:
Types of variable analysis 1- Univariate analysis
2- Bivariate analysis
3- Multivariate analysis
Univariate analysis
Purpose: description
Bivariate analysis
Purpose: determining the empirical relationship between the two variables
Multivariate analysis
Purpose: determining the empirical relationship among the variables
1- Univariate analysis:
analysis of one variable (e.g.
gender)
1- Distribution (frequency, percentage, rate) 2- Central tendency (mean, median, mode)
3- Dispersion (range, variance, standard deviation)
2- Bivariate analysis:
analysis of two variables in order to determine the relationship between them
(e.g. gender & education)
1- Correlation (chi square, t-test)
2- Difference in Populations (chi square, t- test)
3- Multivariate analysis:
analysis of several variables
(e.g.gender, education, age and occupation) 1- Correlation
2- Regression
Common statistical tests
1. Chi square test
Chi-square is a statistical test commonly used to compare observed data with data we would expect to obtain according to a specific hypothesis
2. T-test
The t-test assesses whether the means of two groups are statistically different from each other.
3. F-test
Test if variances from two
populations are equal.
4. ANOVA (ANalysis of VAriance)
Is a statistical method used to test differences between two or more means.
Data presentation 1. Table
Percentage Frequency
Gender
%40 20
Male
60%
30 Female
100%
50 Total
2. Bar graph
0 1 2 3 4 5
male female
Frequency
Frequency
3. Pie chart
Sales
male female
4. Line graph
0 1 2 3 4 5 6 7
female male
