Week 3 Flashcards
(20 cards)
Conceptulization
the process of precisely defining ideas and turning them into variables, in the process specifying the units of analysis, dimensions and values of the variables
Operationalization
process of linking conceptualized variables to a set of procedures for measuring the variables
categorical (qualitative) variables
Have category values (e.g., gender, political party).
Always discrete and can be nominal or ordinal scale.
Example: Marital status (single, married, divorced).
Quantitative variables
Have numeric values, can be continuous or discrete.
Can have interval or ratio scale.
Example: Annual income in dollars.
Continuous variables
Can take any value on the number line.
Examples: Distance, weight, time, height, speed.
Discrete variables
a variable that takes on distinct, countable values
Have finite values, even if theoretically infinite.
Examples: Number of children, years of education, income (when measured in specific brackets).
Nominal scale
Data can only be categorized, no ranking.
Example: Place of birth, eye color, gender.
ordinal scale
Data can be categorized and ranked, but differences between ranks are not equal.
Example: Language proficiency (beginner, intermediate, fluent).
interval scale
Data can be categorized, ranked, but no true zero, meaning ratios cannot be calculated.
Example: Temperature in Fahrenheit (0°F does not mean “no temperature”).
ratio scale
Data can be categorized, ranked, has equal intervals, and has a true zero.
Example: School size (0 students = no school).
response bias
People give inaccurate responses due to the way the question is structured.
acquiescence bias
People tend to say “yes” or agree regardless of their actual beliefs.
social desirability bias
People hide traits they believe to be socially undesirable.
Example: Underreporting smoking habits in a health survey.
question order bias
The order of questions affects responses.
Example: Asking about happiness right after asking about income may influence responses.
demand effects
Respondents answer how they think the researcher wants them to.
ecological fallacy
A logical error where assumptions about individuals are made based on group-level data.
Example: Assuming that because a country has a high average income, every person in that country is wealthy.
Open-ended vs close-ended questions
Open-ended: Allow participants to answer freely (e.g., “What do you think about climate change?”).
Close-ended: Provide a set of response options (e.g., multiple choice).
mutually exclusive questions
Response options must not overlap—each participant can select only one.
Example (Incorrect): Age groups (0-18, 18-35, 35-50) → Overlapping at 18 and 35.
Correct: (0-17, 18-34, 35-50).
exhaustive questions
All possible response options are covered.
Example: A survey on political party should include “Other” or “Prefer not to say” as options.
