Types of Data (AQA A Level Psychology)

Quantitative data

• Quantitative data is data in the form of numbers for example:

  • 53 out of 125 participants answered 'yes' to this question 

  • 89% of participants were slower in condition A 

  • there is a -0.4 correlation coefficient in this study

• Quantitative data can be transformed into tables, graphs, charts, percentages, fractions etc.

• Quantitative data can be statistically analysed using statistics e.g.

  • mean, mode, range (descriptive statistics)

  • Mann-Whitney test, Spearman's rho, related-t test (inferential statistics)

• Research methods that tend to generate quantitative data include

  • experiments e.g. scores on a memory test per condition

  • observations e.g. tally charts that record frequencies of specific behaviours

  • correlations e.g. a correlation coefficient of +0.7

  • questionnaires/surveys via the use of closed questions

Evaluation of quantitative data

Strengths

• Quantitative data tends to be reliable as it is easy to analyse and compare

  • This is because the techniques used to collect it tend to be replicable e.g.

    • standardised procedures, correlational analysis, meta-analysis

• Quantitative data can highlight trends and patterns which is useful when researchers wish to apply general laws of behaviour

Limitations

• Quantitative data can reveal the what behind a behaviour but not the why

  • This means that it lacks explanatory power

  • Thus it is low in validity

• Quantitative data tends to over-simplify the complex, multi-faceted nature of human behaviour and experience

  • This limits its usefulness as a means of gaining insight into people's motives, dreams, fears etc.

Qualitative data

• Qualitative data in the form of words or images e.g.

  • thoughts and feelings expressed in a diary or journal

  • feelings, attitudes/ideas/beliefs discussed in an interview

  • a painting created to express inner turmoil/conflict/anxiety

  • a focus group interview on the impact of social media on body image

• Qualitative research methods/techniques include

  • interviews (individual or focus group)

  • diary entries

  • naturalistic observations

  • open-ended questions

• Qualitative data can be analysed using content analysis or thematic analysis which generates both quantitative and qualitative findings respectively

Evaluation of qualitative data

Strengths

• Qualitative data allows researchers to gain insight into the nature of individual experience and meaning

  • This makes it high in ecological validity

• Qualitative data can be used to expand on and deepen knowledge of complex behaviours e.g.

  • The case of HM involved a man with extreme memory loss

  • The interviews and observations of HM shed light on and helped to confirm the quantitative results e.g. memory tests

Limitations

• Qualitative techniques tend to use small sample sizes

  • This means that the results are difficult to generalise to a wider population

• The subjective nature of qualitative methods does not embrace the features of science (e.g. a lack of objectivity and control)

  • This means that qualitative data lacks reliability

Primary data

• Primary data is collected at the source e.g.

  • a researcher collects two sets of scores (from condition 1 and condition 2) after running an experiment

  • a researcher conducts a questionnaire from which they are able to analyse a range of responses

• Primary data refers specifically to the research aim e.g.

  • Loftus & Palmer (1974) collected data in the form of speed estimates based on their manipulation of key verbs to test the reliability of eyewitness testimony

• Primary data has not been previously published 

Evaluation of primary data

Strengths

• Primary data may be more reliable and valid than secondary data as the researcher has full control over how the data is collected

• Primary data may be more trustworthy than secondary data

  • The researcher knows that their research will be subjected to a peer review which, if negative, would harm their reputation

  • Thus it makes sense for the researcher to take the necessary care to present the best-designed and delivered study possible

Limitations

• Primary data is derived from a single study compared to secondary data which can amass huge samples

  • This limits the potential statistical power of the primary data

• Primary research is expensive and time-consuming compared to the use of secondary data which can be gathered very quickly

  • If the researcher does not find a significant result then they may feel that the time and money spent on the research was wasted

Secondary data

• Secondary data consists of any research findings/results which are pre-existing

  • They have not been collected at source; it is not original data 

• Secondary data is that which has been obtained by other researchers who will each have been working to achieve their specific aim 

• Secondary data has been previously published 

• Secondary data allows a non-interested researcher (meaning they were not involved in the original research process) to gain a clear overview of the topic

• Secondary data is derived from multiple sources e.g.

  • a meta-analysis consists of the quantitative findings of a range of research studies on the same topic e.g.

    • Smith & Bond (1996) conducted a meta-analysis of cross-cultural replications of Asch's conformity study

Evaluation of secondary data

Strengths

• The research studies used in secondary data techniques such as meta-analysis have already been peer-reviewed and the significance of each study has already been established

  • This means that time and money have not been wasted and the researchers can have confidence in the data

• Secondary data may provide new insight into existing theories and research

  • As several studies on the same topic are analysed this allows the researcher to see patterns, trends or interesting features that are unlikely to emerge with the analysis of just one study

Limitations

• Secondary data may not directly address the aim or the topic of the research

  • The researcher's lack of familiarity with the data means that they misinterpret some aspects of the original research

  • This would affect the validity of the secondary data

• As the researcher has not run the original studies themselves they do not know the degree of control and rigour exercised by the original researcher

  • This lack of control affects the reliability of the data

Nominal, ordinal & interval

• When carrying out research, psychologists collect data

• Much research in psychology generates quantitative data

  • The data collected varies in how precise it is

• Levels of measurement (LOM) refer to these differences in precision

  • It is important to assess the level of measurement of a particular set of data because this will determine how it can be analysed statistically

Nominal data

• Nominal data is the most basic LOM

  • It is used when data is put into  categories; for this reason it is sometimes referred to as categoric data e.g.

    • number of people who chose a high-fat snack/people who chose a low-fat snack

  • Nominal data provides very little detailed information or insight as it is a head-count

    • It only tells the researcher how many people are in a group or how many times a specific behaviour occurred

Tally chart as an example of the use of nominal data to define specific behavioural categories in an observation:

Ordinal data

• Ordinal data is used when participant scores can be arranged in order e.g.

  • 1^st 2^nd, 3^rd in terms of the ranking of scores e.g.

    • the highest score was 18 out of 20 (this is given the rank of 1); the second highest score was 15 (this is given the rank of 2) etc

• Ordinal data refers to quantities that make sense in terms of who is in the group when the test was conducted or the choices were made e.g.

  • the ranking of favourite films, people's places in a queue, the finishing order of runners in a marathon, the choice of item on a rating scale from 1 to 7

• Ordinal data provides no certainty as to the intervals between each value

• The units of measurement between each score (the intervals) are not of equal, definable size e.g.

  • the highest score was 18, followed by 15 (an interval of 3), followed by 14 (an interval of 1), followed by 10 (an interval of 4) etc.

• Ordinal data cannot tell the researcher what the gap is between 1^st and 2^nd, or between 4^th and 5^th rank

  • On a 7-point scale, the difference between 6 and 7 is not necessarily the same difference as the difference between a 2 and 3 as the rating is subjective

  • One person’s rating of 6 may mean something else to another person, it may be their version of a 5

Interval data

• Interval data provides the most sensitive and sophisticated level of measurement

  • There is an equal interval between each unit of measurement e.g.

    • centimetres (the gap between 2 and 3 cm is the same as the gap between 10 and 11 cm)

    • timings (the gap between 1 and 3 seconds is exactly double the gap between 1 and 2 seconds)

• Interval data can be converted to ordinal data as the interval values can then be ranked e.g.

  • Tuesday was the hottest day at 25^o then it was Friday with 22^o, then Wednesday with 21^o

    • Temperature is interval data

• Ordinal data cannot be converted to interval data as it does not assume equal intervals between scores/values

• Rating scales rarely use interval data, even if the points on the scale are of equal intervals

  • This is because there is no absolute, agreed value to each score due to the subjective nature of scoring a rating scale

• The value zero does not mean 'nothing' in terms of interval data, it is just another measurement on the scale e.g.

  • 0^oC does not mean that there is no temperature, it refers to the freezing point

• Zero does not figure as a measurement when using ordinal data as it means 'nothing' i.e. 'did not place/rank/do anything'

Summary

• Nominal data is arranged into categories

• Ordinal data is can be ranked without each value being equal in measurement

• Interval data has equal intervals between each value