(Group 4) Quantitative Reserach - Techniques of Data Analysis.pdf

analysis of quantitative data

1. Techniques of
Data Analysis
Quantitative Research
Shofia Lina Nafisah
Nuzul Zahrotushsholikhah
Kartika Kusumawati

2. Descriptive Statistics1.
Descriptive statistics summarize and organize the characteristics of a data set. A data set is
a collection of responses or observations from a sample or entire population.

3. 1a. Distribution
A data set is made up of a distribution of values, or scores. In tables or graphs, you can
summarize the frequency of every possible value of a variable in numbers or
percentages.
From this table, you
can see that more
women than men
or people with
another gender
identity took part in
the study
From this table, you can see that most people visited
the library between 5 and 16 times in the past year

4. 1b. Measures of central Tendency
estimate the centre, or average, of a data set.
The mean, or M, is the most commonly used
method for finding the average.
add up all response values and divide the sum by
the total number of responses
Mean
the value that’s exactly in the middle of a data set.
order each response value from the smallest to the
biggest. Then, the median is the number in the middle
Median

5. 1b. Measures of central Tendency
the most popular or most frequent response value. A data set can
have no mode, one mode, or more than one mode.
order your data set from lowest to highest and find the response
that occurs most frequently.
Mode

6. 1c. Measures of Variability
We measure variation in three ways: range, percentile, and standard deviation.
Range is the simplest. It consists of the largest and
smallest scores
Range
Percentiles tell us the score at a specific place within
the distribution
Percentiles
Standard deviation is the most difficult to compute
measure of dispersion; it is also the most
comprehensive and widely used
Standard Deviation
The range and percentile are for ordinal-,
interval-, and ratio-level data, but the
standard deviation requires an interval or
ratio level of measurement

7. 2. Hypothesis
Testing
Hypothesis testing is a statistical
method used to assess the plausibility
of a hypothesis by using sample data
The four key steps in hypothesis
testing are:
Specify the null and alternative
hypotheses.
1.
Decide the significant level.2.
Collect data and decide whether
to reject the null hypothesis.
3.
Interpret the results.4.

9. Thank You
For Your
Attention