Research Tools and Techniques

The Research Process: Step 7 (Data Analysis Part A)

Lecture 28

Lecture Topics Covered Previously in the Last Lecture

• Non-Probability Sampling Techniques

• What Should be an Ideal Sample Size

• Introduction to Data Analysis Process

What we are going to Cover in this Lecture

• Introduction to Descriptive Statistics

• Measures of Central Tendency

• Measures of Dispersion

THE RESEARCH PROCESS

                     

 

(1).Observation The Broad Problem Area         (2).Preliminary Data GatheringInterviews and Library Search          

(3).

Problem Definition

 (4).TheoreticalFramework

VariablesIdentification

(5)

Generation of Hypothesis

(6).ScientificResearchDesign

(7).Data Collection and Analysis

(8)Deduction

(9).

Report Writing

(10).

Report Presentation

(11).

Managerial Decision Making

Data Analysis ProcessD

ata

Col

lect

ion

Data Analysis

Getting Data Ready for Analysis

Editing Data

1.Incompleteness/omissions

2.Inconsistencies

3.Legibility

4.Coding Data

5.Categorizing

6.Creating a Data File

Feel for Data

1.Mean

2.Median

3.Mode

4.Variance

5.Frequency Distribution Goodness of

Data

1.Reliability

2.Validity

Hypotheses Testing

Appropriate Statistical Manipulation

(Inferential Statistics)

Interpretation of Results

Discussion

Recommendations

Introduction to Data Analysis Process

STATISTICAL DATA ANALYSIS• UNIVARIATE ANALYSIS/Descriptive Statistics:

The univariate analysis refers to the analysis of one variable at a time. This analysis describes a single variable or phenomena of interest

• BIVARIATE ANALYSIS/Inferential Statistics:

In this statistical analysis, the two variables are analyzed at a time in order to understand whether or not they are related. The hypotheses are tested applying this technique.

Descriptive Statistics

• Frequencies:

Occurrence of number of times of a phenomena ---- %ages

Reason n %

Relaxation 9 10

Maintain or Improve Fitness

31 34

Lose Weight

33 37

Build Strength

17 19

Total 90 100

0

5

10

15

20

25

30

35

Relaxation Fitness Lose Weight BuildStrength

n

Frequency Table Showing Reasons of Visiting Gym

Bar Chart --- For a Variable Caught on a Nominal Scale

Gender n %

Male 60 67

Female 30 33

Total 90 100

Male

Female

I am satisfied by the level of cleanliness in Gym

n %

Strongly Disagree

4 5

Disagree 12 13

Neither Agree nor Disagree

12 13

Agree 52 58

Strongly Agree

10 11

Total 90 100

StronglyDisagree

Disagree

Neither AgreeNor Disagree

Agree

StronglyAagree

Next Variable - Gender

Next Variable Caught on an Interval Scale

Measures of Central Tendency

The Mean Average We can calculate averages for interval scale and ratio scale data only i.e. average age is 33.6 years or nearly 34 years.

The Median Midpoint Arrange all values in ascending or descending order and find the midpoint i.e. 31.

Inflation or deflation by extreme members is controlled.

It can be employed for interval, ratio and ordinal scale variables.

Mode Value occurring most frequently i.e. 28

Can be utilized for all types of variables.

Skew ness The skew ness of a distribution is measured by comparing the relative positions of the mean, median and mode.

Distribution is symmetrical Mean = Median = Mode

Distribution skewed right (Right tail longer than left)

Median lies between mode and mean, and mode is less than mean

Distribution skewed left (Left tail longer than right)

Median lies between mode and mean, and mode is greater than mean

Kurtosis

Measures of Dispersion(Variability in a set of observations)

Range Extreme Values

Difference between the maximum and minimum value i.e.

time spent on cv equipment

25 min 50 min

Range = 25 min

weight machines

10 min 60 min

Range = 50 min (It means more variability on the time spent on weight machines)

Variance Spread of data around mean Formula = (n1-u)2+(n2-u)2+(n3-u)2 NCompany A Product (Sales) = 30, 40, 50Company B Product (Sales) = 10, 40, 70 Variance for company A = 66.7 Variance for company B = 600

Standard Deviation Variance Under RootIn our case for Company A 66.7 Under root = 8.167 Company B 600 Under root = 24.495All observations fall within 3 standard deviations of mean40+3*8.167 = 15 – 65 products40+3*24.495 = 0 – 114 products90% observations fall within 2 standard deviations of mean>50% observations fall within 1 standard deviation of mean

Summary

• Introduction to Descriptive Statistics

• Measures of Central Tendency

• Measures of Dispersion