Types of Data & Correlations

• Quantitative Data is expressed numerically
• Quantitative data is simple & visual to analyse, and more objective
• Quantitative data is less detailed so may oversimplify
• Qualitative Data is expressed in words
• Qualitative data is more in-depth, and has greater external validity
• Qualitative data is difficult to analyse and draw conclusions from, and rely on subjective interpretations so may be biased
• Primary Data is original data collected by the researcher for the purpose of the investigation
• Primary data is authentic
• Primary data requires time & effort to produce
• Secondary Data is data that has already been collected by someone else
• Secondary data is inexpensive and easily accessible so saves time & effort
• Secondary data can vary in quality & accuracy, may be outdated, or not match the researcher's needs
• Meta-analysis is when a researcher looks at findings from many different studies and produces an overall statistic
• Meta-analysis allows us to create a varied sample which can be generalised to a larger population
• Conclusions from meta-analysis may be biased as the researcher may purposefully exclude things
• Correlations are when there is no manipulation of the variables therefore we cannot assume causation
• Correlations are useful for establishing a possible relationship however cannot be certain
• Correlations investigate the relationship between two co-variables
• Correlations can be positive, negative, or have no correlation
• Correlations may uncover a trend that warrants further investigation so are useful for encouraging research
• Correlations do not involved manipulation of the independent variable so do not show direct causation
• Correlations can lead to stigmatisms and prejudices
• Reviews are secondary sources collected to investigate a hypothesis
• Systematic Reviews use a set of inclusion criteria to search databases and journals
• Reviews use large samples which increases reliability and externa validity
• Reviews must use studies similar in methodology in order to produce a viable comparison
• Reviews do not use primary data
• Longitudinal studies compare the same group of people across time
• Longitudinal studies have high internal validity but use up time and funding
• Cross-sectional studies compare age differences by using different people at one point in time
• Cross-sectional studies use up less time and funding but also have lower validity due to participant variables
• Nominal (categorical) data is the weakest level of measurement where data is allocated into categories by counting frequency of occurence within particular categories
• Ordinal data is when you have measured something whose values are capable of being placed into rank order from highest to lowest so the scores can be meaningfully compared
• Interval data is measured in fixed units with equal distances between all the points on the scale concerned however is arbitrary as there is no absolute zero
• Ratio data is measured in fixed units with equal distances between all points on the scale and zero does equal zero which provides a baseline