1. Quantitative Methods
Topic 1 Research Design

2. Subject Aims
Data analysis methods appropriate for investigating issues across a range of topics in education.
Conceptual understanding of statistics rather than formal mathematical derivations
Univariate and bivariate statistics.
Skills in questionnaire design

3. Assessments
There will be 10 exercises (30%), and 1 project (4000 words) (70%)

4. SPSS is required
SPSS (Statistical Package for the Social Sciences)

5. Accessing materials online

6. Reading materials for Topic 1
Module1.pdf:
Educational research: some basic concepts and terminology
T. Neville Postlethwaite

7. Topic 1 outline Types of educational research
Planning educational research
Research questions

8. Issues/headlines in Education
Australian students lag in maths, science
Funding splurge fails to improve student results
Best assessment practice
Boys lag behind girls
Accountability policies
Use technology in the classrooms
Decide if quantitative and/or qualitative methods may be used to investigate each of the issues.

9. Types of Research Questions addressed by quantitative methods
Descriptive
Association/Correlational
Causal/Explanatory

10. Descriptive Problems - 1
Which subject fields are more frequently chosen by Australian Year 11 and 12 students?
Do girls choose different subject fields compared with boys? If so, what are the subjects preferred by girls?
Are there differences in subject choice according to differences in social status and ethnic family backgrounds?
What are the areas that Professional Development Program for teachers should cover?
Are all primary school teachers qualified to teach?
What are the status of school building in a country?

11. Descriptive Problems - 2
May not be as straightforward as the computation of frequencies and averages.
Break-in
On the radio, an advertisement for an insurance company ran as follows: “Every 10 minutes, a car is stolen in Zedland. Every 21 minutes, a house is broken into. Take up an insurance policy today.”
Using only the information given in the advertisement, can you conclude
anything about the chance a car will be stolen in Zedland?
that it is more likely to have a car theft than a house break-in?
Give reasons to support your answer.

12. Association - 1
Are there any relationships between students’ reading achievement and their mathematics achievement?
Is there an association between reading achievement and the number of books in the home ?
Association does not always lead to causal.

13. Association - 2
In a town in Europe, the number of storks is positively correlated with the number of babies born.
Crime rate is positively correlated with ice cream sales.

14. Causal relationships - 1
Does smaller class size increase student performance?
Do students’ performance improve if more homework is assigned?
Does parental attitude have an impact on student achievement?

15. Causal relationships - 2
Causal relationship is very difficult to identify.
Number of books at home is positively correlated with student achievement.
Can student achievement be improved by placing more books in a home?
Mediating variables
High parental expectations of achievement is correlated with both number of books at home and student performance.

16. Causal relationships - 3
Study design to establish casual relationships needs to be “confirmatory” than “exploratory”.
Confirmatory approach
E.g., we hypothesise that increasing homework can increase student performance.
Carry out a study where some students are assigned more homework and some are not.
Exploratory approach
Obtain student performance data and other information including homework, school administration, hours of teaching, etc.
Find correlations between student performance and other variables.

17. Research Stages Stage 1: Research aims Stage 2: Literature
Stage 3: Research design
Stage 4: Instrumentation
Stage 5: Piloting
Stage 6: Data collection
Stage 7: Data cleaning and Data analysis
Stage 8: Research report

18. Research aim(s)
Example: To identify factors influencing student withdrawal from school and explore the extent to which each factor contributes to student withdrawal from school
This example will take you through the process of:
1-operationalising a research aim into research questions.
2-how literatures were reviewed and the proportions established
3- hypotheses
4-resech design
5-developping instruments

19. Literature Review Review the related literature
What have been done in the field?
What are controversial debates? School of thought?
What are the “gaps”? (knowledge and methods)
What are the findings?
Develop a theoretical framework

20. Research questions
What are the factors that influence students’ academic achievement?
Which method of instruction is most effective in teaching young children to read?

21. Propositions
Gender, ethnicity, family economic status, parents’ education and occupation, parents’ and students’ attitudes, were important factors relating to students' academic achievement.
School effectiveness has an impact on student achievement
Teacher qualification has an impact on student achievement
Propositions were established as a result of literature review . Propositions formed the research theoretical framework for your project.

22. Research design At this stage the following should be identified:
Source of information
Who is appropriate to provide the necessary information
Characteristics of the target population
Data collection methods

23. Data collection methods
Cross-sectional vs longitudinal
If longitudinal, how many times?
Sample vs cohort
If sample, how many?
Questionnaire, test, interview, published statistics
How many questions?

24. Establishing the link between information needed, source of information and methods of data collection
Information needed/variables to be measured
Source of information
Methods of data collection
The table that presents a link between information needed, and from whom and whore information can be obtained, and how to obtain this information (structured interview or mail questionnaire…)

25. Link between the information needed, sources and methods
Data collection
Student gender
Students
Questionnaire
Family characteristics
parents
Parent attitudes
Parents
Student attitudes
Student achievement
tests
School characteristics
School/Principal
Questionnaire/interview

26. Questionnaires Needed
  Student questionnaire
  Parent questionnaire
  School questionnaire

27. Instrumentation
Develop/validate and pilot instruments (test or questionnaires)
Once the research design was completed. The next step is to design the instruments and pipot the instruments.

28. Variables included in the Parent Questionnaire
Family social status/wealth
Mother Education
Father Education
Mother Occupation
Father Occupation
Family size
Parent attitudes
It is important to identify the variables to be coved in the questionaries.

29. Variables included in the Student Questionnaire
Student gender
Ethnicity
Student attitudes
Student academic achievement
Student behaviour

30. Data collection and data management
Field work supervision
Entering the data into data file
Cleaning the data

31. Data Analysis Descriptive Correlational Causal
Note that statistics can only provide correlational information. Any causal interpretation is made by people.

32. Writing up the reports and discussions
Technical report
Policy report
General public

33. A few examples to contemplate
Margins of error in test scores
For Year 5 numeracy, each child is tested on just 40 questions each year in a national test. If David obtained 25 out of 40 on the 2009 test, how much would we expect David’s scores to vary if tests similar to the 2009 test are administered?
For a 40-question test, David’s scores might vary by as much as 5 score points.
In percentage terms, if a student’s score is 70% on a test, we expect the range of this student’s scores on similar tests to be between 58% and 82%.

34. Simpson’s paradox Men Women Arts Sci total
5 men and 5 women apply for university places (
Men
Women
Arts
3 out of 4 (75%)
1 out of 1 (100%)
Sci
0 out of 1 (0%)
1 out of 4 (25%)
total

35. False positives Prevalence rate for a disease in a population is 1%.
Test for this disease
-ve/-ve 95%
+ve/-ve 5% (5% of those who do not have the disease show a +ve result)
+ve/+ve 95%
-ve/+ve 5% (5% of those who have the disease show a -ve result)
Is this a good test?
Suppose 10,000 people were tested. 600 had +ve result. What is the proportion of the people with +ve result who actually have the disease?