Graphs — AQA A-Level Psychology
Test yourself on Graphs with AQA A-Level practice questions.
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Graphs explained
Candidates must translate information between graphical, numerical and algebraic forms.
Read the full explanation
This includes using a set of numerical data (a set of scores) from a record sheet to construct a bar graph. Scores can be grouped into categories and counted in a frequency table, drawn as a bar graph, expressed algebraically using symbols such as $<$ and $>$, or substituted into formulas such as the sign test. For a bar graph, categories sit on the horizontal axis, frequency on the vertical axis, bars are of equal width and stand apart for discrete data, axes carry labels, and the vertical scale starts at zero. Translating back involves reading a chart to find a numerical total, calculating a percentage, or expressing a relationship algebraically.
Plot two variables from experimental or other data (to include: sketching a scatter diagram using two sets of data from a correlational investigation)
Plotting two variables from experimental or other data requires selecting an appropriate graph. For experimental data, a bar chart plots a categorical independent variable against a continuous dependent variable (e.g., mean words recalled for two groups). A line graph plots a continuous independent variable against a continuous dependent variable. You must also be able to sketch a scatter diagram using two sets of data from a correlational investigation. Each point represents a participant's scores on both co-variables. Axes must be clearly labelled and evenly scaled. The plot is analysed for the direction and strength of the relationship; points rising from left to right indicate a positive correlation.
Your focus
- Construct a bar graph from a set of numerical data (a set of scores) on a record sheet, ensuring bars have equal widths and gaps.
- Translate information between graphical and numerical forms by reading frequencies from a chart and calculating percentages.
- Translate numerical findings into algebraic forms using appropriate mathematical symbols and formula substitutions, such as for the sign test.
Show all 7 objectives
- Explain why a bar graph is appropriate for discrete categorical data.
- Demonstrate how to plot two variables from experimental or other data using appropriate graphical formats like bar charts and line graphs.
- Accurately sketch a scatter diagram using two sets of data from a correlational investigation.
- Interpret scatter diagrams to determine the direction and strength of the association between two co-variables.
Graphs exam tips
Marking Points
- Translating numerical data (a set of scores) from a record sheet to construct a bar graph: categories on the horizontal axis, frequency on the vertical axis, equal width bars with clear gaps, labelled scales, and a title.
- Summarising numerical forms: counting the observations in each category and checking that the category frequencies sum to the total number of observations ($N$).
- Translating into algebraic forms: substituting numerical data into algebraic formulas (like the sign test) or using inequality symbols to express findings (e.g., Condition A $>$ Condition B).
- Justifying the bar graph over a histogram: data fall into separate, discrete categories rather than a continuous scale, so bars must not touch.
- Translating graphical forms back into numerical/algebraic forms: stating the frequency a bar represents or calculating the difference between bars.
- Accurately plotting two variables from experimental data, selecting bar charts for categorical independent variables and line graphs for continuous variables.
- Sketching a scatter diagram using two sets of data from a correlational investigation by plotting one point for each participant where their two scores intersect.
- Ensuring both axes on a scatter diagram are clearly labelled with the co-variables and feature evenly spaced numerical scales.
- Identifying the relationship shown by the points (positive, negative, or zero correlation) and expressing it in terms of the specific co-variables.
- Recognising that a scatter diagram displays an association between two measured variables and cannot be used to establish a cause-and-effect relationship.
Examiner Tips
- 💡When using a set of numerical data (a set of scores) from a record sheet to construct a bar graph, use a ruler, label both axes with the variable and unit, and leave equal gaps.
- 💡If asked to translate a relationship algebraically, use standard mathematical symbols rather than just words.
- 💡Where a record sheet gives frequencies, you may need to translate this numerical data into an algebraic formula for a statistical test, such as calculating $S$ for the sign test.
- 💡When instructed to sketch a scatter diagram using two sets of data from a correlational investigation, always include a descriptive title and ensure your axis scales are linear and evenly spaced.
- 💡If asked to interpret a scatter diagram, explicitly state the direction (e.g., positive) and strength (e.g., strong) of the correlation, referencing the specific co-variables plotted.
Common Mistakes
- Drawing bars touching, which turns the display into a histogram, losing presentation marks for discrete categorical data. Correction: Leave equal gaps between bars for discrete data.
- Starting the vertical axis part way up the scale, inflating the visual difference between conditions. Correction: Always start the vertical frequency axis at zero.
- Failing to translate numerical data into algebraic forms when required, such as forgetting to use standard symbols ($<$, $>$) or formula notation. Correction: Use standard mathematical symbols rather than just words when asked for algebraic forms.
- Connecting the points in a scatter diagram with a line in the order they were plotted. Correction: Leave the individual points unconnected to accurately represent a correlational investigation.
- Using a scatter diagram to plot experimental data that features a categorical independent variable. Correction: Select a bar chart to plot a categorical independent variable against a continuous dependent variable.
- Using causal language when interpreting a scatter diagram, such as stating one variable 'caused' a change in the other. Correction: Use associative language, as correlational data does not involve manipulation of variables.