ANALYSING AND INTERPRETING QUANTITATIVE DATA. HJ. SHAWAL KASLAM. INTRODUCTION. ONE OF THE MAJOR THING IN RESEARCH IS THE DATA. THEREFORE UNDERSTANDING THE DATA IS A CRUCIAL PART IN RESEARCH. Some of the fundamental questions have to be considered are: 1. What is the nature of data?
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HJ. SHAWAL KASLAM
ONE OF THE MAJOR THING IN RESEARCH IS THE DATA. THEREFORE UNDERSTANDING THE DATA IS A CRUCIAL PART IN RESEARCH. Some of the fundamental questions have to be considered are:
1. What is the nature of data?
2. How to gather the data?
3. What is the instrument used to gather the data?
4. How to measure the data?
5. How to analyze the data?
6. How to interpret the output?
The Objective of this presentation is to describe the fundamental process of analyzing quantitative data. By the end of this session, the participants should be able to:-
QUANTITATIVE DATA ANALYSIS IS A PROCESS OF TRANSFORMING THE RAW DATA OBTAINED FROM QUESTIONNAIRES INTO MEANINGFUL INFORMATION SUCH AS STATISTICAL VALUES [e.g: % value, mean value etc..] AND TO TEST STATISTICAL SIGNIFICANT OF THE DATA.
MEASURE OF FREQUENCY DISTRIBUTION
. Percentage value %
MEASURE OF CENTRAL TENDENCY
SKEWNESS & KURTOSIS
Exploration of the variables
. One Sample Test
. Paired T-Test
. Independent T-Test
To test statistical significant of the variables
To test relationship, statistical significant and predict the impact or changes of the variables
STATISTICAL VALUES - MEAN
- STANDARD DEVIATION
BASIC DESCRIPTIVE STATISTIC
Example: Research question “is there any different of mean score between the group [male and female] of sample study?
Ho : ∂1 = ∂2 No significant different
H1 : ∂1 ≠ ∂2 There is significant different
reject Ho [ There is significant difference]
fail to reject Ho [No significant difference]
Calculated Test value ρ
Critical value α [alfa]
By convention, in social science α = .05 or 0.01
CRITERIA OF REJECTION or ACCEPTANCE
e.g: test whether the mean of students test score
is the same as the standard mean = 70
T (29) = -1.008, ρ = 0.322, ρ > 0.05
∴ Fail to reject Ho
Conclusion there is no significant difference between the sample mean of population with the standard mean [Test value].
A study was done to compare job stress between two employee groups (administrative and support). Data were solicited from a randomly selected sample.
Test the hypothesis on the difference at .05 level of significance.
T(18) = .615, ρ = .545
ρ > 0.05
∴Fail to reject Ho
Conclude that there is no significant difference in job stress between administrative and support groups at .05 level of significance.
A training program was conducted to improve participants participants’ knowledge on ICT. Data were collected from a selected sample both before and after the ICT training program. Test the hypothesis that the training is effective to improve participants knowledge on ICT at 0.05 level of significant.
T(9) = 4.882, ρ = .001
ρ < 0.05
∴Reject Ho [Null hypothesis]
Conclude that the training program was
effective to improve participants knowledge
on ICT at .01 level of significance
Data on perception toward management was gathered from a randomly selected sample comprising of three from a randomly selected sample comprising of three employee groups (supervisory, line and support). Test the difference in perception among the three groups at .05 level of significance.
A study was conducted to determine whether job stress is significantly related with employees group.
The result Pearson Chi-square = 4.667, p = .862
X2 ( 9, N = 20) = 4.667, p > .05
∴Fail to reject Ho
Conclude that there is no significance relatedness of job stress with employees group.
R = -.783
Sign r = .000 , p < 0.01
There is a negative and high relationship between anxiety (X) and team cohesiveness (Y)
Analyzing quantitative data is the most interesting part of a research. It is important that the presentation of the data is effective in bringing the objectives of the study to the forefront and in stating clearly the research outcome.