[Home]   PSY 101    [Psychology Images] Class 18: Research II: Looking for Links; Evaluating Research-Flaws, Placebo
Last updated: October 5, 2020

Looking For Links: Descriptive/Correlational Research (Outline)

Descriptive or Correlational research can tell us if there is a link between variables, but not about cause/effect relationships

  • Do overweight people exercise less than normal weight people? (weight vs. exercise)
  • Do people in the South live fewer years than people in the North? (location vs. life expectancy)
  • Do angry people have more heart attacks than peaceful people (emotion vs. heart disease)
  • Do step-fathers treat their step-children worse than their natural children (biological relatedness vs. parental care)
  • Do more hours of athletic practice lead to more successful athletic performance? (practice vs. success)
Vaccination vs. 2020 Presidential Vote

Correlation coefficient (r): A numerical index between -1 and +1 which expresses the strength of relationship between two variables (correlation coefficient is labeled "r")

[Correlation]
[Negative Correlation] {Zero Correlation] [Positive Correlation]
 Correlation (r) = -1.00   Correlation (r) = +0.05  Correlation (r) = +1.00 
* N = neuroticism, E = extraversion, O = openness to experience, A = agreeableness, C = conscientiousness
As a correlation moves from 0.0 toward +1.0 (more positive), the strength of the relationship increases. Similarly, as a correlation moves from 0.0 toward -1.0 (more negative), the strength of the relationship increases. Correlations near a value of 0.00 indicate that there is little to no relationship between two variables.

Hence, the correlation -0.90 is larger or stronger than the correlation +0.75.

The correlations of +0.63 and -0.63 are exactly the same magnitude but in opposite directions. They are equally "strong".    
Correlations say that there is a relationship, NOT that one variable CAUSES the other. It is possible that both variables are actually caused by a third or fourth variable. Or, even, the relationship is purely accidental.

Spurious Correlations: Data may show a relationship between two variables where none really exists. These are "spurious" (= false) correlations. A researcher should have a reason to believe that two variables might be related. Here are two examples of spurious correlations:

Spurious
          Correlation


Zombies vs. Political Parties

Other forms of Descriptive Research

 

Flaws: Evaluating Research

Sampling Bias: Is the sample representative of the population under review?

Placebo Effects: Changes in a person's behavior which come from the EXPECTATION of change, rather than the ingredients or components of the treatment they receive.

Distortions in Self-Report Data: Bias introduced by participants who respond in ways that do not reflect their actual behavior, beliefs, judgments, etc.

  • Social Desirability: Give answers which reflect favorably on yourself
  • Misunderstood or poorly worded questions
      
  • "Response sets" = participant responds in a stereotypical or automatic ways

    • Halo Effect

    • Leniency-Severity-Generosity Effects

    • Extremist or Central Tendency response set: rate everything as terrible or excellent, or as average 

Experimenter Bias

  • "Double-blind" Research: (the GOLD standard) In such an approach to research, neither the participants nor the data collectors know which participants are in the experimental group or in the control group.


Looking at Ethics

The question of deception

Animal Research

Ethical Principles in Research with Human Subjects


 


This page originally posted on 1/28/04 and updated on October 5, 2020