Causation and Correlation in Experimental Research
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Questions and Answers

How can causation be shown?

An experimental study in which an independent variable is manipulated to bring about an effect in the dependent variable.

What is the purpose of mediation analyses?

To test a variable as a potential pathway between an independent and dependent variable.

What does correlation determine?

The relationship between two variables.

What is a prediction (regression) equation used for?

<p>To predict some measure of performance based on the relationship between predictor variables and the criterion.</p> Signup and view all the answers

The higher the relationship between two variables, the less accurately you can predict one from the other.

<p>False</p> Signup and view all the answers

What is perfect correlation?

<p>1.00, either +1.00 or -1.00</p> Signup and view all the answers

What is the significance of a 0.00 relationship?

<p>No relationship</p> Signup and view all the answers

What is the purpose of statistics that test for differences between groups?

<p>To establish whether the groups are different, and also the strength of the association between the independent and dependent variables.</p> Signup and view all the answers

What are the ways to increase statistical power? (Select all that apply)

<p>Increase the difference between the means</p> Signup and view all the answers

Why is obtaining power in research desirable?

<p>Because the odds of rejecting the null hypothesis are increased.</p> Signup and view all the answers

Study Notes

Causation in Experimental Studies

  • Causation can only be shown in experimental studies where an independent variable is manipulated to affect the dependent variable.

Mediation Analyses

  • A method to analyze how a variable can act as a pathway between an independent and dependent variable.

Correlation and Prediction

  • Correlation measures the relationship between two variables.
  • The stronger the relationship, the more accurately one variable can predict the other.
  • Regression equations can be used to predict one variable based on the relationship with other variables

Testing Differences Between Groups

  • Statistical tests can determine if groups differ and the strength of the relationship between the independent and dependent variables.
  • Perfect correlation is represented as a value of +1.00 or -1.00, while no relationship is 0.00.
  • Increasing the difference between group means and decreasing the variance within groups increases statistical power.
  • A larger sample size also improves statistical power.

Repeated Measures Designs

  • Used to isolate individual differences and increase power.
  • Studying phenomena over time with fewer participants.

Carryover Effect

  • Refers to the effect of previous conditions on subsequent conditions in an experiment

Hypothesis Testing

  • A p-value determines if the observed data is consistent with the null hypothesis.

  • Power is the probability of rejecting a false null hypothesis.

Statistical Tests

  • Dependent and independent samples t-tests are used to examine differences between groups.
  • Positive and negative patterns in variables can indicate a relationship.
  • Scattered data points suggest no relationship.
  • Curvilinear patterns show an indirect relationship.

Types of Sampling

  • Convenience sampling is quick, inexpensive, and easy but has low generalizability.
  • Stratified sampling divides a population by characteristics before random selection.
  • Effect sizes describe the magnitude of experimental effects.
  • Mediators show how one variable influences another.
  • Interaction effects show how the influence of one variable depends on another.

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Experimental Study Methods PDF

Description

This quiz explores key concepts in experimental studies, including causation, mediation analyses, and the differences between groups. It also delves into correlation and prediction methods, highlighting how variables interact in statistical analyses. Test your understanding of these critical research principles.

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