Types of data + Meta-analysis

Types of data

What is Quantitative data?

What are the strengths/weaknesses?

What is it?

• Numerical data → represents how much, or how long, or how many there is of the measured variable (DV)

• Descriptive stats summaries it, and this can be displayed on tables/graphs

Strengths:

• Easy/quick to analyse → eg: w/ descriptive stats/graphs/tables + can then compare

• Objective/consistent/reliable → less chance of bias/inconsistent results bcs limited options → increases scientific credibility

Weaknesses:

• Less detail than qualitative, which means researchers may not fully understand the reasons behind behaviour/beliefs/actions + cannot explain context SO may miss out important info by focusing what can mathematically be measured

Types of data

What could give you quantitative data?

• Reaction times using a stop watch

• Self-rating an emotion on scale of 1-10

• Tally of the number of times someone performs an operationalised behavioural categories

• Closed questions

Types of data

What is Qualitative data?

What are the strengths/weaknesses?

What is it?

• Data in the form of words

• Expresses what people think/feel

• Can be quantified through content analysis

Strengths:

• Rich in information is provided which gives us more detail when explaining the complex issues we look at

• Multiple methods for gathering data on sensitive subjects i.e. Observation (unstructured), open questions in self reports etc

Weaknesses:

• Can be open to interpretation + potentially can be biased

• Can be challenging to summarise bcs produces extensive range of data → don’t want it to loose its meaning etc when taken out of context (eg: looking at quantitative data from Milgram, may think the Ps were cold/sadistic BUT when watch qualitative data see the emotional distress they were feeling)

• Less reliable bcs open-ended questions have almost unlimited different answers that can be given → hard to get consistent results + harder to compare

Types of data

what could give you qualitative data?

• Interviews

• focus group

• observations

• open questions

• etc

Types of data

What is Primary Data?

What are the strengths/weaknesses?

What is it?

• Observed and collected directly from first-hand experience

• In the case of psychology - the data that is collected by the researcher within that study

• The data collected would be specifically related to the aims and/or the hypothesis of the study

Strengths:

• The control the researcher has over the data, ability to fit the aims and hypothesis of the Study → likely more valid

• It's easier for them to test its reliability and validity + can control the data collection process carefully

Weaknesses:

• Time consuming + often expensive for researcher → eg: paying Ps for their time/other researchers for working/paying for materials/running pilot studies etc

Types of data

What is involved in collecting primary data?

• Designing the study

• Gaining ethical approval

• Piloting the study

• Recruiting + testing participants

• Analysing the data collected and drawing conclusions

Types of data

What is Secondary Data?

What are the strengths/weaknesses?

What is it?

• Known as ‘second-hand data’ → researchers use info previously collected by a third-party (eg: other researchers/studies)

• Collected initially for another reason than the current research Q

Strengths:

• Cheap – less time and equipment needed vs primary data bcs alr done by a third party

• Data may have been subjected to statistical testing and thus it is know whether it is significant

Weaknesses:

• The data may not exactly fit the needs of the study

• You have no control over data quality/extraneous variables/quality of analysis

Types of data

Examples of Secondary Data:

• Gov stats

• Business stats

• Records

• Previously published studies

Meta-analysis

What is a meta analysis?

• Collects + combines + reviews data from previous studies asking similar research Qs together

• Review can include: statistically combing all data to produce overall effect size/trends + conclusion

Meta-analysis

Strengths of meta-analysis

• Larger sample size → reviewing the results can increase the validity of conclusions drawn vs singular studies

• Bcs M-As look at overall patterns of results across many studies, a small number of individual studies affected by bias/extraneous variables are unlikely to change overall pattern of results → makes M-A more trustworthy vs individual studies

• Can help gain deeper insights into cause/effect relationships → eg: cross-cultural studies w/ same/different results may tell us that cultural differences do/don’t impact that type of behaviour

Meta-analysis

Limitations of meta-analysis:

• The data may not exactly fit the needs of the study bcs of differing reseach Qs

• You have no control over data quality/extraneous variables/quality of analysis

• The research designs in the different studies may vary, which means studies may not be truly comparable

• The choice of which studies to include/exclude could be biased (eg: excluding results that do not support the researcher’s desired outcome OR due to publication bias → only have access to studies showing a certain result)