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Descriptive Statistics:

This guide explores descriptive statistics techniques for presenting research results. Key methods include comparing group percentages, means, and correlations among multiple variables. You'll learn how to interpret the correlation coefficient, how to construct scatterplots for visualizing relationships, and the importance of measures of central tendency—mean, median, and mode—as well as measures of variability like variance and standard deviation. The insights from these statistics provide a solid foundation for your experimental or quasi-experimental research.

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Descriptive Statistics:

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  1. Descriptive Statistics: Describing your results

  2. Ways of describing your results • Comparing group percentages • Correlations of 2 or more variables • Comparing group means • Experimental or Quasi-experimental research

  3. “What’s your favorite movie genre?”

  4. Figure 1. Student and faculty response to the poll 'Should Avenue High School adopt student uniforms?'

  5. Correlation Coefficient • Numerical index that reflects the relationship between 2 variables • Ranges from –1 to +1 • Pearson product-moment correlation or Pearson’s r

  6. Understanding a correlation:Eyeballing your data

  7. Scatterplot • Illustrates the relationship between variables • X on the horizontal axis • Y on the vertical axis • Positive correlation • Data from lower left to upper right • Negative correlation • Data from upper right to lower left

  8. Scatterplot for + correlation

  9. Scatterplot

  10. Scatterplot for - correlation

  11. Scatterplot for no correlation

  12. Descriptive Statistics • Measures of Central Tendency • Mean: M • The average of the scores. • Add up all the scores and divide by number of scores. • Median: Mdn • The middle score. 50% of scores fall above, 50% fall below. • Put the numbers in order from lowest to highest, and then find middle score. If the number of scores is even, take the average of the two middle scores. • Mode • The most frequent score.

  13. Descriptive Statistics • Measures of Variability • Variance: s2 • Standard deviation: s or SD • N = number of participants

  14. Why Standard Deviation Matters

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