Data analysis for quantitative research.

1. Ronald C. Lucasia
Discussant
DATA ANALYSIS FOR QUANTITATVE
RESEARCH
The Philippine Women’s University
School of Education
Advanced Research Methods
Dr. Layla P. Padolina
Research Professor

2. DATA ANALYSIS
Describe and summarize the data.
Identify relationship between variables
Compare variables
Identify difference between variables
Forecast outcomes

3. 5 Most Important Method for Data Analysis
1. Mean
2. Standard Deviation
3. Regression
4. Sample Size Determination
5. Hypothesis Testing

4. Parametric Technique=
makes various kinds of
assumptions about the nature
of the population from which
samples involved in the
research study

5. Non-parametric Technique=
make few assumptions about
the nature of the population
from which the samples are
drawn

6. DATA ANALYSIS TECHNIQUE

7. Four Levels of Data Measurement
1. Nominal Data= data that is used for naming or labelling
variables.
2. Ordinal Data= is a categorical, statistical data type where
the variables have natural, ordered, categories and
distances between categories is not known..
3. Interval Data= a type of data which is measured along a
scale in which each point is placed at an equal distance
from one another.
4. Ratio Data= a quantitative data with an equal and
definitive ratio between each data and absolute zero being
treated as a point of origin.

8. Problem statement
• “This study will evaluate association between
politics and history TV channels
• Preference among different age groups of the
population. It will provide Statistical evidence to
support if such association exists”
• Researcher chooses quantitative research design
• Researcher randomly select sample size of 200
people
• Using simple random sampling
• Questionnaire design and data collection
EXAMPLE: Topic: Television rating study

10. Coding Nominal/Ordinal data sets
• For data that is not numeric (nominal or ordinal), you
first label it with code.
For example, in our study we have two TV programs and
three age categories
Coding would like this Politics = 0 History = 1
• Coding would like this under 20 = 1 20-30 = 2 above
30 = 3
• Then you create variables for this data in the variable
view

11. GRAPH VISUALIZATION
Preliminary inspection of the bar chart
Shows that there is association between
Viewer age and program preference
A = politics
B = history

12. Frequency Polygon= It
provides most information

13. Research hypothesis: There is an association between politics and
history TV channels preferences and viewer age”
In this study, we select test significance level as α = 5% , and sample test statistic as
Chi-square 𝝌 𝟐
We accept the Research hypothesis if p-value < α

14. SUBGROUP COMPARISON

15. Data Analysis for Correlational Study
Writing the results section for Correlational study.
1.r - the strength of the relationship.
2.p value - the significance level. "Significance" tells
you the probability that the line is due to chance. ...
3.n - the sample size.

16. MULTIVARIATE ANALYSIS
The analysis of the simultaneous relationships among
several variables.
TABLE 6.1:
Multivariate
Relationship: Religious
Attendance, gender, and
Age

17. Experimental Research
• T-test= helps examine whether the
differences between two samples are
statistically significant.
• One-way ANOVA= examines
differences between more than two
groups.
• Chi-Square= compare frequencies
observed in a sample with some
theoretically expected frequencies.

18. Sample Data Analysis for
Ttest
Groups Mean
Standard
Deviation
Tabular t Computed t Description Decision
Non-Hybrid
Group
(control)
9.74 2.99
1.99 0.18
Not
Significant
Accept Ho
Hybrid
Group
(experiment
al)
9.63 2.43

19. Sample Data Analysis for Likert Scale

20. THANK YOU
FOR
LISTENING!!