Data Analysis

    OCR
    GCSE

    Data Analysis in Psychology bridges the gap between raw observation and theoretical validation. It encompasses Descriptive Statistics (summarising data via central tendency and dispersion) and Inferential Statistics (determining the probability that results are due to chance). Mastery requires navigating the transition from raw scores to calculated values, interpreting significance levels (typically p ≤ 0.05), and justifying the selection of statistical tests based on experimental design and data levels (Nominal, Ordinal, Interval). Candidates must demonstrate precision in calculation (AO2) and sophistication in interpreting the implications of Type I and Type II errors on psychological knowledge (AO3).

    5
    Objectives
    4
    Exam Tips
    4
    Pitfalls
    3
    Key Terms
    4
    Mark Points

    Learning Objectives

    What you need to know and understand

    • Calculation of Mean (sum/n), Median (middle value), Mode (most frequent), and Range (highest - lowest)
    • Distinction between Qualitative (descriptive) and Quantitative (numerical) data
    • Rules for Histograms: Continuous data on x-axis, frequency density/frequency on y-axis, bars must touch
    • Standard form notation (e.g., 3.4 x 10^3) and conversion to decimal
    • Types of Correlation: Positive, Negative, Zero (No correlation)

    Example Examiner Feedback

    Real feedback patterns examiners use when marking

    • "Calculation is correct, but you must show the working to secure method marks in case of minor arithmetic errors"
    • "You selected the mean, but failed to account for the outlier which skews the data; the median was more appropriate"
    • "Graph axes are labeled, but you used a bar chart for continuous data; a histogram was required"
    • "Interpretation of the scatter diagram correctly identifies the direction of the relationship but misses the strength"

    Marking Points

    Key points examiners look for in your answers

    • Award marks for precise calculation of mean, median, mode, and range, showing working where requested
    • Credit accurate plotting of data points on scatter diagrams and histograms, ensuring axes are labeled with operationalized variables
    • Responses must justify the selection of central tendency measures (e.g., selecting median over mean in the presence of outliers)
    • Analysis must correctly interpret correlation coefficients, distinguishing strength and direction without implying causation

    Examiner Tips

    Expert advice for maximising your marks

    • 💡Always show the step-by-step calculation for mathematical questions; method marks are often available even if the final answer is incorrect
    • 💡When interpreting graphs, explicitly reference the axes labels and data values to support conclusions
    • 💡Check for extreme scores (outliers) before deciding whether the mean or median is the most appropriate measure of central tendency
    • 💡Memorize the conversion between fractions, percentages, and decimals to save time on arithmetic questions

    Common Mistakes

    Pitfalls to avoid in your exam answers

    • Confusing histograms (continuous data, touching bars) with bar charts (discrete data, separate bars)
    • Failing to rank order data sets before identifying the median value
    • Incorrectly rounding to decimal places instead of significant figures (or vice versa) as specified in the question
    • Interpreting a strong correlation as direct evidence of cause-and-effect relationships

    Study Guide Available

    Comprehensive revision notes & examples

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    Key Terminology

    Essential terms to know

    Likely Command Words

    How questions on this topic are typically asked

    Calculate
    Interpret
    Justify
    Evaluate
    Draw
    Compare

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