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- 1. Chapter 17 Data analysis and reporting
- 2. What the specification says… Candidates should be familiar with the following features of data analysis, presentation and interpretation: 1. Presentation and interpretation of quantitative data including graphs, scattergrams and tables 2. Analysis and interpretation of quantitative data. 3. Measures of central tendency including median, mean, mode. 4. Measures of dispersion including ranges and standard deviation 5. Analysis and interpretation of correlational data. 6. Positive and negative correlations and the interpretation of correlation coefficients 7. Presentation of qualitative data 8. Processes involved in content analysis
- 3. Data analysis and reporting: graphs (pg. 559-562) • Describe a range of ways of presenting data graphically, including tables, graphs, bar charts, histograms, frequency polygons and scattergrams. • Demonstrate your understanding of when a particular method of graphical representation can be used by selecting and evaluating appropriate methods
- 4. Types of data Type of data Definition Nominal data Distinguish between different mutually exclusive categories of a variable. E.g. Smoking/non smoking Ordinal data Data is organised in to logical categories. The differences between the categories are not standard. Interval Data is organised in to logical order. The differences between the categories are equal as they are based on a standard measurement. Ratio Measurement on a scale that has equal intervals and also has a genuine zero point. For example John who is 2m tall is twice as tall as Suzie who is 1m tall. Discrete data Can only take whole values Continuous data Can take decimal places
- 5. What type of data? Ordinal data Ordinal data
- 6. What type of data?
- 7. What type of data? Nominal data Ratio data Interval data
- 8. What type of data? Ordinal data Ordinal data
- 9. Appropriate selection of graphical representations • • • • • Graphs Bar charts Histograms Frequency polygons Scattergraphs For A2 you are required to draw the appropriate representation in the exam
- 10. Line graph • Tend to be used to show change over time or trials. • Time or number of trials is usually plotted on the x-axis • The measure is presented in the yaxis
- 11. Frequency diagrams • How often certain measures occur • Frequency is always recorded in the y-axis • The variable of interest is plotted on the x-axis Examples include: Bar charts Histograms Frequency polygons
- 12. Bar charts • Use nominal or ordinal data • 75-80% rule: • y-axis should be 75-80% of the x-axis • Make the bars a sensible width and leave small spaces between them • Use alphabetical order for nominal • Use natural order for ordinal
- 13. Histogram • Use – interval and ratio data – Discrete data • 75-80% rule: • y-axis should be 75-80% of the x-axis • Important that the bars touch • It may be necessary to group categories on the x-axis for the sake of clarity (6-8 classes ideally) • You would then need to recalculate the frequencies
- 14. Frequency Polygons • Use – interval and ratio data – Continuous data • 75-80% rule: • y-axis should be 75-80% of the x-axis • Important that the bars touch • Can have unlimited number of classes
- 15. Scattergrams • Essential first step to work out correlational analysis • Provides an initial indication if there is a relationship between two variables • If you think that one variable predicts another variable for example children’s age predicts attention span – the predictor variable (age) is presented on the horizontal axis x-axis
- 16. Activity Turn to page 562 and complete all practice questions To be handed in next Research Methods lesson

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