Involves developing valid and reliable measures to assess social concepts or constructs and to assign numerical values to them.
Conceptualization: The process of defining and clarifying concepts that will be used in measurement and scale construction.
Operationalization: The process of selecting specific indicators, measures, and variables that will be used to measure the conceptualized concept.
Reliability: The extent to which a measurement is consistent and stable over time or across different observers.
Validity: The extent to which a measurement accurately reflects the concept it is intended to measure.
Types of variables: Categorical or nominal variables, ordinal variables, interval variables, and ratio variables.
Scales of measurement: Nominal, ordinal, interval, and ratio scales.
Item analysis: The process of evaluating individual items or questions in a measurement instrument to ensure that they are measuring the intended concept.
Factor analysis: A statistical technique used to identify the underlying factors or dimensions of a set of variables.
Reliability and validity in measurement: Different types of reliability such as test-retest reliability, inter-rater reliability, and internal consistency reliability; different types of validity such as face validity, content validity, criterion-related validity, and construct validity.
Classical Test Theory (CTT): A framework used to measure and evaluate the reliability and validity of a test.
Item Response Theory (IRT): A framework used to assess the relationship between the responses given to test items and the underlying construct being measured.
Likert scales: A commonly used scale that asks respondents to indicate their level of agreement or disagreement with a set of statements.
Response bias: The tendency for respondents to answer questions in a particular way that does not reflect their true beliefs or attitudes.
Social desirability bias: The tendency for respondents to provide socially desirable responses rather than their true beliefs or attitudes.
Data analysis: The process of analyzing data collected from measurement instruments and drawing conclusions based on the results.
Nominal Measurement: Nominal measurement refers to data that can be categorized into distinct, non-overlapping categories. Examples of nominal data include gender, race, and religion.
Ordinal Measurement: Ordinal measurement assigns numbers to categories based on their rank order. This means that responses can be placed in order from highest to lowest or vice versa. Examples of ordinal data might include income brackets or educational attainment levels.
Interval Measurement: Interval measurement involves measuring the distance between numerical responses. An example of interval data would be temperature measures in Celsius or Fahrenheit.
Ratio Measurement: Ratio measurement is similar to interval measurement, but with an absolute zero point. Examples of this include height, weight, and distance.
Likert Scale: The Likert Scale involves measuring attitudes or opinions by asking respondents to rate their level of agreement on a statement. For instance, a survey might present a statement such as "I agree with the following statement: 'Social media is harmful.'" The respondent would then select from a range of responses, such as "Strongly Disagree" to "Strongly Agree.".
Thurstone Scale: Thurstone scales assign numbers to statements that represent various degrees of an attitude or concept. Respondents rate statements along a numeric scale, and those scores are then statistically analyzed.
Guttman Scaling: Guttman scaling involves designing test items in such a way that correctly responding to one item implies that one is likely to respond correctly to others. Items increase in difficulty along a logical continuum, and the goal is to measure how well respondents fit within that pattern.