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STUDY UNIT 2b QUANTITATIVE RESEARCH METHODS Analysis of Quantitative Data
STUDY UNIT 2b QUANTITATIVE RESEARCH METHODS
Analysis of Quantitative Data
Analysing Quantitative Data: Inferential Statistical Concepts
Statistical significance - The scientific method requires us to go beyond describing our data—we need to test our hypotheses and make inferences about our data.
To do that, we need to use
inferential statistics
(P value/ANOVA/etc.)
Statistical Inference
Goal is to statistically affirm:
Can we infer results taken from a sample to the population? •
How likely is it that these sample effects reflect true population effects versus random error?.
Null Hypothesis Significance Testing (NHST)
:
This method is used to determine the probability that sample data are good estimates of population data.
NHST Process
The NHST process is described as follow:
Assume Null Hypothesis is true
Conduct Appropriate Statistical Test
Selecting an appropriate statistical test depends on the following
Type of research question
Number of variables and level of measurement
Research design: e.g., between-subjects vs. within-subjects; experimental vs. non-experimental
Identify p-value
Interpret p-value
If the p-value is low (less than .05):
Reject Null Hypothesis
This suggests that the likelihood of obtaining such a relationship is less than 5% if there truly is no such relationship within the population
If the p-value is high (higherthan .05):
Accept the Null Hypothesis
This suggests that the likelihood of obtaining such a relationship is greater than 5%, if there truly is no such relationship within the population
Decide whether or not to reject H0
What happens if you get a non-significant finding (i.e., high p-value)?
Replicate study (again and again)
Increase the power of study by increasing the following:
Level of statistical significance
Sample size
Sensitivity of measurement
Organising Quantitative Data for Analysis Purposes
1. Entering the data
in table form on a spreadsheet for example.
3. Cleaning the Data
Ensure that there are no missing data
(i.e. participants did not submit a complete form)
4. How to deal with incomplete data
One possible way is to exclude that participant from our data analysis (list-wise deletion, i.e., delete the entire row of that participant’s information)
Other ways include replacing the missing data with estimates
2. Coding non-numerical data
I.e. assigning a digit to the data so that we can graphically present it
Describing Quantitative Data: Descriptive Statistical Concepts
The first step in data analysis is to describe your data using descriptive statistics.
some common descriptive statistics includes:
Frequency counts (or distribution)
This gives you the counts and the
distribution for category
of a variable.
For example, you might have 90 males and 10 females in your study. In terms of the frequency distribution of gender in your study, you can see that it is heavily skewed towards males.
Measures of central tendency
These measures help you to describe the centre of the frequency distribution. Includes:
Mean
(Average)
Median
(Mid point of the distribution)
Mode
(The most common or frequently occurring number)
Measures of variation
These measures help you to describe the
variability of
the
frequency distribution
around its centre. Includes:
Range
= Maximum Value – Minimum Value
Standard Deviation (SD)
: Average distance between all the scores in the
distribution and its mean
Quantitative Report and Proposal Writing
Title,
Abstract,
Introductory sentence.
Summary of methods
Prediction and description of results
A conclusion that states the support of hypothesis and for future work
Introduction,
Research topic
Literature review
Method
Participants
Research design
Material
Procedrue
Data analysis
Results
Discussion