Topic Summaries

Data handling and analysis

A-Level > Psychology > AQA > A-Level Psychology Topic Summaries > Research methods > Data handling and analysis
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Addiction

  • Reporting psychological investigations: the features of a scientific report are:
    • Title: short description of the research.
    • Abstract: a summary of the research which includes a hypothesis, method(s), results and a conclusion.
    • Introduction: a broad overview of the context of the research. Then you focus on the particular study and mention its aims, hypothesis and why it was carried out.
    • Method: this is a detailed description of the procedure used in the study. This is so it can be evaluated by also replicated. It mentions: the study design (method used e.g.independent groups or repeated measures, operationalisation of variables), the participants (target population that was studied, sampling method and how many were used), equipment used (description of any specialised equipment and how it was used), standardised procedure (step-by-step description of how it was carried out) and controls (how extraneous variables were controlled).
    • Results: a summary of data and key findings collected from the research.
    • Discussion: explanation of results and how they relate to the experimental hypothesis.They address any issues that came up when conducting the research, suggestions for future research and how the results fit other research.
    • Conclusion: short summary of key findings.
    • References: a list of sources that may have been used in the study.
  • Types of data:
    • Quantitative data: numerical data that is more objective, collected from techniques like closed-question questionnaires.
    • Qualitative data: not numerical and provides more insight into the individual’s life, collected from techniques like open-ended interviews.
    • Primary data: the researcher collects the data first-hand, such as in an interview.
    • Secondary data: the researcher uses other data to draw own conclusions on their research (e.g.meta-analysis – a study of other studies that involves taking studies from the same research area and using it to identify trends to create a larger study).
  • Descriptive statistics:
    • Measures of central tendency:
      • Mean: the average of a set of data, calculated by adding all numbers together and dividing by total amount of numbers.
      • Median: the middle result from a set of data, calculated by arranging all the numbers in a set from smallest to largest and then finding the middle number.
      • Mode: the most common figure, calculated by finding the most frequently occurring number in a set.
    • Measures of dispersion:
      • Range: the range from the smallest to largest number.
      • Standard deviation: how much numbers in the set deviate from the mean. To calculate, first find the mean, then subtract the mean from each number in the set and square these numbers. Add all of the numbers together and then divide the result by the number of numbers. The square root of these numbers is the standard deviation.
    • Percentages change: work out the difference between the original number and the other number, divide the difference by the original number, then multiply by 100.
  • Distribution:
    • Normal distribution: is a symmetrical curve where the majority of the scores are on/near the mean average, and there is an equal number of scores above and below the mean.
    • Skewed distribution: an asymmetrical curve with an unequal distribution of scores either side of the mean. This shape is caused by anomalous results which lead the curve to skew either positively or negatively.
      • Positive skew: a high score makes the mean a lot higher than most of the scores which makes most scores below the mean (i.e.mean > median).
      • Negative skew: a low score makes the mean a lot lower than most of the scores which makes most below the mean (i.e.mean < median).
  • Correlation: the relationship between two variables. They will either be closely related and have a positive correlation or be weakly related and have a negative correlation. Correlations are measured using correlation coefficients. These range between +1 and -1.
    • r=+1 means two things are perfectly correlated. One goes up, the other goes up by the same amount.
    • r=-1 means two things are negatively correlated.One goes down, the other goes down by the same amount.
    • r=0 means two things aren’t correlated.
  • Levels of measurement:
    • Nominal data: named data that can be separated into categories that do not overlap.
    • Ordinal data: placed into an order or scale.
    • Interval data: comes in a numerical form that is standardised.
  • Inferential testing:
    • Statistical testing – the sign test: a way to calculate the statistical significance of differences between related pairs of nominal data. If the observed value is equal to or less than the critical value, then the results are statistically significant.
      • Look if your experimental hypothesis is two-tailed (a change is expected in either direction) or one-tailed (a change is expected to go in one direction).
      • Find the p value – this determines significance of a result.
      • Look for your critical value in a critical values table.
      • Calculate your observed value (count when the less frequent sign appears).
    • Inferential tests: how to decide which test to use:
  Test of difference Test of correlation
Related Unrelated
Nominal Sign test Chi squared Chi squared
Ordinal Mann Whitney Wilcoxon Spearman's Rho
Interval Unrelated T tes Related T test Pearson's R
  • Type 1 and type 2 errors:
    • Type 1 error – false positive: where researchers decide an effect is real (reject the null hypothesis) but there is no effect.
    • Type 2 – false negative: where researchers decide there is no effect and accept the null hypothesis but there is a real effect. 

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