Descriptive Research Methods and Correlational Analysis

Descriptive Research and Unbiased Observation

  • Types of Descriptive Research:

    • Case study

    • Naturalistic observation

    • Surveys

    • Archival research

    • Cross-sectional research

    • Longitudinal research

  • Eliminating Bias in Observation:

    • Observer bias occurs when an observer has had prior interactions or conflict ("run-ins") with a subject, such as a child.

    • To ensure zero bias and obtain a significantly cleaner observation, an observer can be replaced by a peer who has zero prior interactions with the subject.

    • Giving every subject an equal chance requires utilizing observers who hold no preconceived notions or personal bias toward the individual being observed.

Principles of Correlational Research

  • Definition of Correlational Research:

    • Correlational research determines whether a predictable relationship exists between two or more variables.

  • Numerical Range of Correlations:

    • Correlation coefficients range on a continuous scale from −1-1 to +1+1 (negative one to positive one).

  • Positive Correlation:

    • A positive correlation occurs when two variables move in the same direction: as one variable increases, the other variable also increases.

    • Example: The relationship between height and weight. As physical growth increases height, body weight increases accordingly.

  • Negative Correlation:

    • A negative correlation (referred to in metabolic/physiological contexts as an inverse relationship) occurs when two variables move in opposite directions: as one variable increases, the other decreases.

    • Example: The relationship between sleep duration and fatigue. As the number of hours of sleep decreases, tiredness increases.

  • No Correlation:

    • No correlation indicates that no predictable mathematical or functional relationship exists between variables.

    • Example: The relationship between hours of sleep and shoe size. Sleeping more or fewer hours has no predictive effect on an individual's shoe size.

Correlation versus Causation and the Third Variable Problem

  • Core Rule of Correlational Analysis:

    • Correlation does not equal causation.

    • The presence of a statistical correlation between two variables does not prove that one variable causes the change in the other.

  • The Third Variable Problem:

    • An underlying, unmeasured third variable may be driving the simultaneous changes observed in both correlated variables.

  • Real-World Case Example:

    • Observed Correlation: Ice cream sales and crime rates increase together during the summer months.

    • Mechanism: Neither ice cream purchasing causes crime, nor does criminal activity drive ice cream sales.

    • Identified Third Variable: Temperature. Increased ambient summer temperatures independently drive up both ice cream sales and criminal activity.