Quantitative Research - Measurement & Data Collection

These flashcards cover key concepts related to measurement and data collection in quantitative research, including definitions of measurement levels, types of reliability and validity, data collection methods, and criteria for critiquing measurement plans.

What are the levels of measurement in quantitative research?

The levels of measurement are nominal, ordinal, and interval/ratio.

What does reliability in quantitative research refer to?

Reliability refers to the stability, internal consistency, and equivalence of a measurement.

What are the types of validity mentioned in quantitative research?

The types of validity include face validity, content validity, criterion-related validity, and construct validity.

What is the difference between existing records and original data in data collection?

Existing records are pre-existing data, while original data is newly collected data from sources like self-reports or observations.

What are the two main types of self-report data collection methods?

The two main types are interviews and questionnaires.

What differentiates open-ended questions from close-ended questions in self-reports?

Open-ended questions allow for free-form responses while close-ended questions provide specific answer options.

What are categories in observational data collection?

Categories are classifications used to record and analyze observed behaviors or events.

What are rating scales used for in observational data collection?

Rating scales are tools used to assess the frequency or intensity of behaviors during observation.

What is the difference between in vivo and in vitro studies in biophysiological measures?

In vivo studies occur within a living organism, while in vitro studies take place outside a living organism, typically in a lab setting.

What should be considered when critiquing a quantitative measurement and data collection plan?

Considerations include the trustworthiness of the data, accuracy and validity of what they reflect, reported reliability and validity, clarity of data collection procedures, appropriateness of data collection methods, and efforts to enhance data quality.