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Pie Chart
A pie chart is a circle segmented to illustrate shares or percentages of the total; each category appears as a “slice” of the “pie.” Bar charts can be used for the same purpose, but a pie chart gives a more readily comprehensible picture of how cases are “shared out” between categories, provided there are relatively few categories. (A histogram or bar chart would be used in preference to a pie chart for showing trends across a variable—e.g., over time or by geographical locality.)
Figure 1, for example, illustrates what people living in a town center had to say about their area and what was good about it.

Figure 1 What Is Good About Living in Your Area?

Figure 2 Purpose of Visit to the Citizens' Advice Bureau in Middlesbrough
Pie charts can also be used to make simple comparisons between categories. Again, bar charts can be used for the same purpose and may, indeed, be preferable when there are several different groups to be compared, but pie charts give a graphic contrast between, say, two groups that can be seen easily and immediately. The “pies” in Figure 2, for instance, illustrate the difference in reasons for using a Citizens' Advice Bureau by ethnic group. The Citizens' Advice Bureaus are a network of volunteer offices across Britain that offer advice and guidance on dealing with the bureaucracy in the face of which the poor are otherwise powerless—with central and local government, taxation, landlords, shops, and other financial and legal matters—and they also offer counseling on debt and financial management. Figure 2 shows, unsurprisingly, that immigration and nationality are issues that are often brought up by the Asian population of Middlesbrough and very seldom by the White population. The White population is a little more likely than the Asian population to raise issues about housing and benefits (government support payments) and substantially more likely to have debt problems that they want to discuss.
References
- Analysis of Variance
- Association and Correlation
- Association
- Association Model
- Asymmetric Measures
- Biserial Correlation
- Canonical Correlation Analysis
- Correlation
- Correspondence Analysis
- Intraclass Correlation
- Multiple Correlation
- Part Correlation
- Partial Correlation
- Pearson's Correlation Coefficient
- Semipartial Correlation
- Simple Correlation (Regression)
- Spearman Correlation Coefficient
- Strength of Association
- Symmetric Measures
- Basic Qualitative Research
- Basic Statistics
- F Ratio
- N(n)
- t-Test
- X¯
- Y Variable
- z-Test
- Alternative Hypothesis
- Average
- Bar Graph
- Bell-Shaped Curve
- Bimodal
- Case
- Causal Modeling
- Cell
- Covariance
- Cumulative Frequency Polygon
- Data
- Dependent Variable
- Dispersion
- Exploratory Data Analysis
- Frequency Distribution
- Histogram
- Hypothesis
- Independent Variable
- Measures of Central Tendency
- Median
- Null Hypothesis
- Pie Chart
- Regression
- Standard Deviation
- Statistic
- Causal Modeling
- DISCOURSE/CONVERSATION ANALYSIS
- Econometrics
- Epistemology
- Ethnography
- Evaluation
- Event History Analysis
- Experimental Design
- Factor Analysis and Related Techniques
- Feminist Methodology
- Generalized Linear Models
- HISTORICAL/COMPARATIVE
- Interviewing in Qualitative Research
- Latent Variable Model
- LIFE HISTORY/BIOGRAPHY
- LOG-LINEAR MODELS (CATEGORICAL DEPENDENT VARIABLES)
- Longitudinal Analysis
- Mathematics and Formal Models
- Measurement Level
- Measurement Testing and Classification
- Multilevel Analysis
- Multiple Regression
- Qualitative Data Analysis
- Sampling in Qualitative Research
- Sampling in Surveys
- Scaling
- Significance Testing
- Simple Regression
- Survey Design
- Time Series
- ARIMA
- Box-Jenkins Modeling
- Cointegration
- Detrending
- Durbin-Watson Statistic
- Error Correction Models
- Forecasting
- Granger Causality
- Interrupted Time-Series Design
- Intervention Analysis
- Lag Structure
- Moving Average
- Periodicity
- Serial Correlation
- Spectral Analysis
- Time-Series Cross-Section (TSCS) Models
- Time-Series Data (Analysis/Design)
- Trend Analysis
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