1 / 25

Comprehensive Overview of Measurement and Descriptive Statistics in Research

This review explores the fundamentals of measurement and descriptive statistics, emphasizing how measured scores reflect true underlying scores while accounting for measurement error. It outlines different measurement scales—nominal, ordinal, interval, and ratio—and their implications on statistical analysis. Key concepts such as central tendency, variability, and frequency distributions are examined, alongside the significance of normal distributions, skewness, and kurtosis in data interpretation. This foundational knowledge aids researchers in accurately representing and analyzing data in their studies.

xanto
Télécharger la présentation

Comprehensive Overview of Measurement and Descriptive Statistics in Research

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Review of Basics

  2. REVIEW OF BASICS PART I • Measurement • Descriptive Statistics • Frequency Distributions

  3. Measured Scores • Any measured score represents: • True underlying score • Measurement error • Lower measurement error means higher reliability

  4. Statistical Models • How well can we represent the data? • Outcomei = modeli + errori • The model may be as simple as the mean of the data set, but often takes the form of a linear equation: • Y = mX+ b

  5. MEASUREMENT SCALES • What assumptions can you make about a score? • Many statistics require a certain measurement scale. • The measurement scale is a property of the data.

  6. 1. Nominal Scale • Numbers classify into groups. • Math, other than counting, is not meaningful.

  7. 2. Ordinal Scale • Numbers are rank orders. • Math, other than counting, is not meaningful.

  8. 3. Interval Scale • Numbers represent amounts, with equal intervals between numbers. • Math, other than ratio comparisons, is meaningful.

  9. 4. Ratio Scale • Numbers represent amounts, with equal intervals and a true zero • true zero: score of zero represents a complete absence • Math, including ratios, is meaningful.

  10. Why You Can’t do Ratios on an Interval Scale

  11. The Same Temperatures on Another Interval Scale

  12. The Same Temperatures on a Ratio Scale (Rankine = F + 459.6)

  13. The Same Temperatures on a Ratio Scale (Kelvin = C + 273.15)

  14. DESCRIPTIVE STATISTICS • Central Tendency • Variability • Frequency Distributions

  15. Central Tendency – Typical Score • mean: arithmetic average • median: middle score • mode: most frequent score

  16. Variability – Spread of Scores • deviation: difference between observed score and model (e.g., mean) • sum of squares(SS): sum of squared differences from the mean

  17. Variability • variance: average squared difference from the mean • standard deviation: average unsquared difference from the mean

  18. FREQUENCY DISTRIBUTIONS • frequency: number of times a score occurs in a distribution • frequency distribution: list of scores with the frequency of each score indicated

  19. Normal Distributions • symmetrical • equal mean, median, and mode • bell-shaped

  20. Why Be Normal? • Many variables are affected by many random factors. • Effects of random factors tend to balance out.

  21. Skewness • Extent to which scores are piled more on one end of the distribution than the other • positive skew • negative skew

  22. Skewness • Skewness = 0 for a normal distribution • Skewness < 0 for a negatively skewed distribution • Skewness > 0 for a positively skewed distribution

  23. Kurtosis • Measure of the steepness of the curve • Platykurtic: flat • Leptokurtic: steep

  24. Kurtosis • Kurtosis = 0 for a normal distribution • Kurtosis < 0 when the distribution is flatter than a normal • Kurtosis > 0 when the distribution is steeper than a normal

  25. Choosing Stats A researcher manipulates whether participants are put in a conflict situation or not (a confederate either agrees or disagrees with the participant). The participants are then given a survey which measures their level of self-confidence. The researcher would like to know whether conflict affects level of self-confidence.

More Related