Secondary Data Analysis

Question 1

What is secondary data analysis?

Answer 1

Secondary data analysis ; retrospective analysis ; using existing data sets

Conducting a research project without collecting ‘fresh’ data - i.e. using data that has already been collected in a fresh context.

Question 2

When would secondary data analysis occur?

Answer 2

• your supervisor / a colleague might have data collected from a previous study
• you could use a publicly available research dataset where lots of things are measured (these are often big, longitudinal, population representative data)
• you could use historical or archival data

Question 3

What are some data repositories?

Answer 3

• APA
• ICPSR (database for versions of datasets): Inter-university Consortium for Political and Social Research i

Question 4

What are the benefits of secondary data analysis?

Answer 4

• easier (can be faster, doesn’t involve you collecting data)
• its richer (access to populations and resources that you wouldn’t have access to - time, money, vulnerable populations) and involves types of research studies you couldn’t do yourself (longitudinal data, birth cohorts)
• ethical use of data (advancing the benefits of the data, no additional distress on participants)
• can increase research transparency

Question 5

What are the disadvantages of using secondary data?

Answer 5

• you don’t have control over how the data were collected
• often mismatch between the study design, measures used and your rq/hypotheses (study was designed for a different RQ to yours, often non-experimental, maybe not ideal measures used)
• sometimes it costs to access the data set
• working with other people
• easy to engage in questionable research practices

Question 6

What are the traditional research steps?

Answer 6

observation
question
hypothesis
prediction
experiment
results
theory

Question 7

What are the steps involved for secondary data analysis?

Answer 7

1. Determine RQ (possibly tentative hypotheses)
2. Find appropriate dataset for your RQ
3. Identify the study’s design, variables
4. Refine hypotheses in light of actual variables
5. Analyse data
6. Make conclusions and write up results

Question 8

The first step of secondary data analysis

Answer 8

Determine RQ
- what is the area?
- literature review (what’s known, what is the gap?)
- define research question
- hypotheses -what do you want to investigate, what are your predictions?

Question 9

Step 2

Answer 9

Find appropriate dataset for your RQ
- fits your area / phenomena
- may need to apply to access and use the data for research purposes

Sometimes the first 2 steps are reversed because data presents itself!

Question 10

Step 3

Answer 10

Identify the study’s design, variables, etc

• what was the original RQ
• what was the study design
• who was the population
• what constructs were targeted, how were they operationalized

Question 11

Step 4

Answer 11

Refine hypotheses in light of actual variables
- planned RQ might not be testable on this data
- revise and refine hypotheses in light of which data is actually there

Question 12

Step 5

Answer 12

Analyse data
- descriptive statistics to summarise
- formal analyses to address hypotheses

Question 13

Step 6

Answer 13

Make conclusions and write up results

Question 14

Important considerations for secondary data analysis

Answer 14

• may still need ethical approval at the start as the data will be used for purposes other than the original intention
• at what point does compromise undermine your conclusions Too much
• analytical methods need to match the sampling design
• don’t data snoop

Question 15

Important considerations for BIG datasets

Answer 15

• longitudinal projects often have a lot of missing data –> is this differential attrition? how will you deal with this?
• need to consider and make decisions about significance levels and effect size (as p value is inversely proportionate to sample size)