Research Methods: Chapter 4 - Data & Measurement PDF

Data and the Nature of Measurement Graziano and Raulin Research Methods: Chapter 4 Research Variables  Variable: Any characteristic that is measured in a research study  Examples: speed, level of hostility, accuracy of feedback, reaction time, self esteem, aggression How would you define and...

Data and the Nature of Measurement Graziano and Raulin Research Methods: Chapter 4 Research Variables  Variable: Any characteristic that is measured in a research study  Examples: speed, level of hostility, accuracy of feedback, reaction time, self esteem, aggression How would you define and measure aggression? Operational Definitions    The specific procedures by which the researcher measures and/or manipulates a variable In research, every variable should be operationally defined The more careful and complete the operational definition, the more precise the measurement of the variable will be Measuring Variables in Research    Measurement: A process by which we assign numbers to indicate the amount of some variable present Sometimes the number assignment is easy to understand (e.g., speed measured in number of seconds; age) Sometimes it is more arbitrary (e.g., 1 for male and 2 for female) Scales of Measurement   Based on how closely the scale matches the real number system Scales of Measurement Nominal Scales  A naming scale     Each number reflects an arbitrary category label rather than an amount of a variable Examples: gender, diagnostic categories, political affiliations Produces nominal or categorical data Has mathematical property of identity Ordinal Scales  A scale that indicates rank ordering     Reflects the order, but not the amount of a variable Examples: order of finish in a race, class rankings Produces ordered data Has mathematical properties of identity and magnitude Interval Scales  A scale that has equal intervals     The scale indicates amount, but there is no zero point on the scale Examples: temperature on the Celsius or Fahrenheit scale, most psychological tests Produces score data Has the mathematical properties of identity, magnitude, and equal intervals Ratio Scales  A scale that fits the number system well     The scale has a true zero and equal intervals, just like the real number system Examples: time, distance, number correct, weight, frequency of behavior Produces score data Has the mathematical properties of identity, magnitude, equal intervals, and a true zero   Reliability refers to the consistency of measurement Types of reliability    Interrater reliability: degree of agreement between two independent raters Test-retest reliability: degree of consistency over time Internal consistency reliability: degree to which the items of a measure are in agreement Validity   A scale is valid if it measures what it is supposed to measure Validity also refers to how well a scale predicts other variables. For example, an IQ test is likely to be a valid predictor of grades in school. Types of validity     Content validity  Do the items fit our topic well? Construct validity  Is there a solid theory guiding us?  Is that theory the best explanation of results? Predictive validity  Do our survey results predict some future behavior? Concurrent validity  Do our survey results relate to something right now that we would expect? In Class Activity TEST YOUR KNOWLEDGE OF VALIDITY! Win = extra credit points added to the exam Lose = tissues to dry your eyes Scale Attenuation Effects  If the effective range is insufficient, scores will cluster at the top or bottom of the scale (termed scale attenuation effects)    Floor effect: insufficient range at the bottom of the scale, so most low scorers are bunched together Ceiling effect: insufficient range at the top of the scale, so most high scorers are bunched together Scale attenuation effects distort scores, thus reducing both reliability and validity Summary      Measuring variables is central to research Several scales of measurement exist Reduce measurement error with carefully developed operational definitions Want to enhance reliability and validity of your measures The goal is to produce objective, accurate measures of your variables