Research Design and Correlation Coefficients

These flashcards cover key definitions and concepts related to research design and Pearson's r correlation coefficient as outlined in the lecture.

Correlation

A statistical procedure used to describe the strength and direction of the relationship between two factors.

Pearson's r

A measure of the linear relationship of two factors in which the data for both factors are measured on an interval or ratio scale.

Positive correlation

When one variable increases, the other also increases.

Negative correlation

When one variable increases, the other decreases.

No correlation

There’s no clear relationship between the variables.

Repeated Measures Design

A research design in which the same participants are observed in each group or treatment.

Matched Samples Design

An experimental method in which pairs or groups of participants are matched based on common characteristics or traits they share.

Confounding variables

Factors that influence both the group identity and the outcome, potentially misleading interpretations of correlation.

Correlation coefficient

A number summarizing the degree of correlation between two variables, typically between -1 and +1.

Independent samples

Participant groups are selected from different populations and are unrelated.

Within subjects

A research design in which the same participants are subjected to different conditions or treatments.

Statistical significance

The probability that the observed difference or relationship is not due to chance.

Assumption of linearity

The assumption that the best way to describe the pattern of data is using a straight line.

Magnitude of correlation

The strength of the relationship between two variables, indicated by the value of the correlation coefficient.

Causation vs. correlation

The principle that correlation does not imply causation; a relationship between two variables does not mean one causes the other.

Bivariate normal distribution

When the data from both variables are plotted together, they form a normal distribution.