The document discusses key techniques for quantitative research, including testing, surveys, observation, and interviewing. It outlines the role of the quantitative researcher and emphasizes the importance of experimental versus quasi-experimental research designs. Some essential aspects of quantitative research covered include establishing reliability and validity, using appropriate sampling methods, eliminating bias, and understanding variables. The document also provides guidance on effective testing, survey design, avoiding common traps in questionnaire design, and the appropriate roles of observation and interviewing. It stresses knowing the basics of statistics, including different variable types and common tests for significance.

1. The Quantitative ResearchThe Quantitative Research
Process:Process:
Techniques for ResearchTechniques for Research
Dr Fiona M Beals

2. Lecture AimsLecture Aims
Review key research methods brought to
quantitative research by experimental
designs
Outline the role of the quantitative
researcher
Introduce and look at methods of:
– Testing
– Surveys
– Observation and Interviewing

3. The SettingThe Setting
 Experimental research vs
Quasi-Experimental Research
 The need for empirical data
 Sampling is key (stratified
random or purposive)
 Key words are reliability and
validity (internal and external)
 Significance is important
 Eliminate bias
 Remember variables

4. TestingTesting

5. Why Test?Why Test?
Established tests tend to have measures of
reliability and validity
Testing before and after an intervention can
show evidence of change (and the direction
of change)
Tests for significance can occur (ANOVA,
Chi Square)

6. What to testWhat to test
 Psychometric variables
 Biological/Physiological
changes
 Educational Changes (IQ etc)

7. How?How?
Don’t create your own
test instead find
established tests which
have measures of
reliability and validity

8. Survey ResearchSurvey Research

9. Survey ResearchSurvey Research
 Types
– Cross-sectional surveys (inc. Census)
– Longitudinal surveys (trend, cohort, panel)
 How/What
– Text/Document Surveys (primary and secondary
sources)
– Questionnaires inc open/closed items, branching and
clear layout

10. Traps in Questionnaire DesignTraps in Questionnaire Design
 Ambiguity – unclear questions
 Assumptions
– Multiple responses when really only one is wanted
– Memory stretching
– Knowledge demands
 Double questions
 Leading questions
 Presuming questions
 Hypothetical questions
 Overlapping categories

11. Getting it rightGetting it right
 Remember most people don’t want to write or
type
 So quick ticks and clicks work
 Follow the KISS principle
 Use likerts for measuring variability in responses
 Connect the question to the response
 NEVER ask two questions in one!!!
 Keep the survey to under seven minutes
 PILOT, PILOT, PILOT

12. Observation and InterviewingObservation and Interviewing

13. Observation and InterviewingObservation and Interviewing
Observation can have an important function
in quantitative designs but tends to focus on
descriptive elements
Interviewing needs
to be structured
Both observation
and interviewing should only be used for
triangulation of data and results

14. The role of statisticsThe role of statistics

15. Know the basicsKnow the basics
 Nominal
– =/ ≠
– Dichotomous (Gender)/Non-dichotomous (nationality)
– Mode
– Qualitative
 Ordinal (rank order without degree of difference)
– =/ ≠, </>
– Dichotomous (truth, beauty, health)
– Non-dichotomous (opinion)
– Median (psychological tests do tend to break this rule)
– Qualitative
 Interval (degrees of difference but without ratios)
– =/ ≠, </>, +/-
– Date, Latitude, Temperature
– Arithmetic mean (average using sum – what usually happens)
– Quantitative with an arbitrary point of origin (0)
 Ratio
– =/ ≠, </>, +/-, ×/÷
– Age, mass, length, duration, energy etc.
– Geometric mean (average using product and the nth root)
– Quantitative with a unique and non-arbitrary zero

16. Know a little moreKnow a little more
 Correct use of percentages
 Data sets need to be over 30
 Basic tests for significance
– Chi square
– T test
– ANOVA
 Read research critically!!!
– Read for bias
– Read for incorrect use of statistics
– Read so you don’t make the same mistakes