Data Handling and Analysis

What is qualitative data

Data that is expressed in words/verbally /written description

What is quantitative data

Data that's expressed numerically can be analysed statistically and be converted into graphs

Define primary data

Information that has ben obtained first hand by a researcher for purposes of a research project

Define Secondary data

Information that's already been collected by someone else and pre-dates the current research project

Define Meta-analysis

The process of combining the findings from a number of studies to a particular topic to produce an overall statistical conclusion based on a range of studies

• should not be confused with a review where a number of studies are compared and discussed

What are strengths of qualitative data

• Offers the researcher rich detail about It's much broader and allows the participant to fully elaborate on their thoughts, feelings and emotions on a given subject

• Thus, it has high external validity as it provides a more meaningful insight to the participant

What are the limitations of qualitative data

• Difficult to analyse and draw conclusions

• Conclusions that can be drawn can be subjective to the researcher's interpretation and be subjective to bias

What are strengths of quantitative data

• Simple to analyse

• Comparisons and conclusions can easily be drawn

• Numerical data tends to be more subjective and less sensitive to bias

What are the limitations of quantitative data

doesn't provide detailed insight to the participant fails to represent 'real life'

What are strengths of primary data

• Authentic data obtained for the investigation's purpose

• The investigation can be tailored to obtain the data/establish cause and effect

What are the limitations of primary data

• Requires the researcher's time and effort

• Requires considerable planning and resources

What are strengths of secondary data

• Quick to obtain in comparison to primary data

• Inexpensive, easily accessed and requires minimal effort

What are the limitations of secondary data

• There may be substantial variation in the quality and accuracy of the data

• Data may be outdated and incomplete

• Content of the data may not match the researcher's needs and objectives

Why might the validity of conclusions made with secondary data be challenged

It uses data that may be outdated, lacks quality and accuracy as well as not fully matching with the needs and objectives of the researcher

What are measures of central tendency

Any measure of the average value in a set of data

What are measures of dispersion

Any measure of the spread or variation in a set of scores

What is standard deviation

• A sophisticated measure of dispersion in a set of scores

• Tells us how a set of scores deviates from the mean

• Standard deviation provides insight into how clustered or spread out the scores are from the mean

What does a low standard deviation show

Indicates that the scores are clustered tightly around the mean which indicates the reliability of the data set

What does a high standard deviation show

indicates that the scores are more spread out from the mean which indicates lower reliability

How is standard deviation calculated

1. Calculate the mean

2. Subtract the mean from each score in the data set

3. Square the scores which have just been calculated at step

4. Add all of the squared scores together

5. Divide the total squared score by the number of scores minus

6. Work out the square root of the variance

Strengths of the range

• The range provides a broad overview of the data which can be useful for some research purposes

• The range is simple and easy to calculate

Limitations of the range

• The range provides no information as to all of the other scores in the data set

• This means that it lacks validity as it does not indicate the degree of variation from the mean

• The range is not very stable or representativeIt can vary from one sample to another as sample size increases

Strengths of standard deviation

• Standard deviation provides information as to how the scores are distributed across a data set

• This is a strength as it can indicate to what extent the data set is reliable and consistent

• Standard deviation is more sensitive than the range as it uses all the scores in the data set

• This means that it is a more valid representation of the data set

Strengths of the mean

• The mean is the most sensitive measure of central tendency as it takes all scores in the data set into account

• The mean is more likely than other measures of central tendency to provide a representative score

• This means that it is the most reliable measure of central tendency

Limitations of the mean

• The mean is sensitive to extreme scores (outliers) so it can only be used when the scores are reasonably close

• This means that it would not be a suitable measure for some data sets

• The mean score may not be represented in the data set itself

Strengths of the median

• The median is not affected by extreme scores

• This means that it can be used on data sets with anomalous scores

• The median is the best measure of central tendency when dealing with qualitative data where ranking of categories or themes is used instead of measurement or counting

Limitations of the median

• The median does not necessarily represent a typical average as it does not include all of the data in its calculation

• It does not account for extreme scores making it less reliable than the mean

• Arranging the data in ascending or descending order is time-consuming

• This makes the median more problematic in dealing with large data sets

Strengths of the mode

• The mode is not affected by extreme values

• The mode is often useful for the analysis of qualitative data

• This type of data may require frequencies of theme to be analysed

Limitations of the mode

• A data set may include two modes or more which blurs the meaning of the data, making it difficult for the researcher to form conclusions

• This means that the mode is the least reliable of the measures of central tendency

• The mode is likely to be of little use on small data sets as it may provide an unrepresentative central measure meaning it lacks validity

Outline Bar charts

• Shows discrete data

• Bars are separated to determine that we're dealing with separate conditions

Outline histograms

• Shows continuous data

• Bars touch

Outline scattergrams

Don't depict differences, but associations between co-variables

Define Peer review

The assessment of scientific work by others who are specialists in the same field to determine that any research intended for publication is of high quality

What are the main (3) aims of peer review

• To allocate research funding

• To validate the quality and relevance of research

• To suggest amendment or improvements

Evaluate Peer Review

• Anonymity usual practice that the 'peer' remains anonymous to produce a more honest appraisal

  • But a minority of reviewers may use their anonymity as grounds to criticise their rival researchers

• Publication bias - Publishers may only want to publish 'headline grabbing' findings to increase circulation of their publication as well as positive results - research that doesn't meet this could be discredited or disregarded

• Burying groundbreaking research - May suppress opposition to mainstream theories to align with the scientific status quo or researchers may be critical of research that contradicts their own

How might research into psychology have implications on the economy

Research in psychology can produce a 'ripple effect' in society as it can be the springboard for:

• social changeadoption of new ideas

• the modification of some existing ideas and practices