Quantitative Research Methods and Data Collection

These flashcards cover key concepts from the lecture notes on quantitative research methods and data collection, addressing differences between observational studies and experiments, confounding, and aspects of experimental design.

What is the main distinction between observational studies and experiments in quantitative research methods?

Observational studies observe individuals and measure variables without influencing responses, while experiments deliberately impose treatments to measure their effects.

What is confounding in observational studies?

Confounding occurs when two variables are associated such that their effects on a response variable cannot be distinguished from each other, limiting the ability to infer causality.

What is the goal of experiments as compared to observational studies?

The goal of experiments is to determine if the treatment causes a change in the response, while observational studies aim to describe a group or examine relationships between variables.

What is a response variable?

A response variable (dependent variable) measures the outcome of a study, influenced by changes in the explanatory variable.

What is an explanatory variable?

An explanatory variable (independent variable) helps explain or predict changes in a response variable.

What is the purpose of random assignment in a completely randomized design?

Random assignment ensures that treatments are assigned using a chance process, creating groups that are roughly equivalent at the beginning of an experiment.

What is a placebo?

A placebo is a treatment that has no active ingredient but is otherwise similar to other treatments, used to provide a baseline for comparison.

What is the placebo effect in the context of experiments?

The placebo effect describes how some subjects respond favorably to any treatment, including inactive treatments, based on their expectations.

How can confounding be identified in an observational study?

Confounding can be identified by considering other variables associated with the explanatory variable that might cause changes in the response variable.

What is a completely randomized design in experiments?

A completely randomized design assigns experimental units to treatments completely at random, ensuring unbiased treatment allocation.