Observation (continued)

Outline of Today’s Discussion 1.Measurement Scales 2.Important Considerations for Observational Paradigms

Part 1 Measurement Scales

Observation 1.Behavior is typically measured on one of four types of scales: Nominal; Ordinal; Interval; and Ratio. 2.The type of scale will determine what kinds of statistics and analysis can be conducted. 3.Let’s consider each of the four scale-types in turn…

Observation 1.Nominal scales are the crudest of all measurement scales. 2.The levels on a nominal scale are either category names (male, female), or “dummy” numbers ARBITRARILY assigned to categories (male=1, female=2). 3.The only permissible arithmetic operation on nominal scales is “equal, or not equal”. 4.Nominal data are often reported as the percentage of observations in each category. 5.Example: the study on book carrying.

Observation 1.Ordinal scales reflect rankings (i.e., “ordering”). 2.Unlike nominal scales, ordinal scales permit “greater than and less than” comparisons. 3.In 1990, proponents of the Gulf War argued that caution was needed because Iraq had the 4th largest army in the world. 4.What’s misleading about that statement (and other statements that similarly rely on ordinal scales)?

Observation 1.Unlike ordinal scales, interval scales indicate the distance (i.e., the interval) between values on a given dimension. 2.Addition and subtraction can be performed on interval-scale data (multiplication and division can be done too, but only with caution). 3.Interval scales do NOT have an absolute zero point. Ratios cannot be performed. 4.Example: Degrees in Celsius.

Observation 1.The most ideal scale for measurement is the ratio scale, which DOES have an absolute zero point. 2.Ratio scales can only be used if it is possible to have ABSOLUTELY NONE of a variable. Examples: Time, weight, length, heat (Kelvin), money. 3.Multiplication and division (i.e., ratios) can be performed on ratio scales. 4.Example: 10 degrees Kelvin really is half as hot as 20 degrees Kelvin.

Part 2 Important Considerations For Observational Paradigms

Observation Data Reduction - The process of abstracting and summarizing observations. For qualitative observations, data reduction involves identifying summarizing themes. For quantitative observations, data reduction involves identifying central tendency, dispersion, outliers, and “impossible scores”.

Observation Coding – The process of classifying units of behavior –or particular events- according to specific criteria relevant to the study. Example: In a study on bullying, we might code a variable like “physical aggression”, which would require person-to-person contact. Not all person-to-person contact constitutes bullying…we need further specification.

Observation 1.Potential Pop Quiz Question: Would someone please explain the concepts of reactivity and demand characteristics? 2.Potential Pop Quiz Question: Describe how habituation and desensitization are used to minimize the influence of the experimenter?

Observation 3. Potential Pop Quiz Question: In your own words, distinguish demand characteristics from expectancy effects. 4. Potential Pop Quiz Question: What is meant when a study is described as a double-blind study?

Observation 1.To help increase reliability, researchers often use a checklist with pre-defined behaviors. Two (or more) observers independently check- off whether the specified behavior is present or absent at a particular time…

Observation Questions on observation? Formula for observer reliability (a.k.a., inter-rater reliability) Values < 85% are considered poor.