Lesson 5: Correlational & Quasi-Experimental Designs PDF

# Lesson 5: Correlational & Quasi-Experimental Designs ## Correlational & Quasi-Experimental Designs - Nonexperimental approaches to research, like correlational and quasi-experimental designs, can complement experiments. - While it is much more difficult to establish cause-and-effect relationship...

# Lesson 5: Correlational & Quasi-Experimental Designs ## Correlational & Quasi-Experimental Designs - Nonexperimental approaches to research, like correlational and quasi-experimental designs, can complement experiments. - While it is much more difficult to establish cause-and-effect relationships using these techniques conclusively, they can achieve high external validity. - Correlational and quasi-experimental designs achieve low internal validity, but they can show relationships, predict behavior, and generate research hypotheses that can be tested in future research. ## Four Properties of Correlation Coefficients - A Pearson correlation coefficient is used to calculate simple correlations (between two variables) and may be expressed as r = +.70, p=.001 - **Linearity** means how the relationship between x and y can be plotted as a line (linear relationship) or a curve (curvilinear relationship). - **Sign** refers to whether the correlation coefficient is positive or negative. - **Magnitude** is the strength of the correlation coefficient, ranging from –1 to +1. - **Probability** is the likelihood of obtaining a correlation coefficient of this magnitude due to chance. ## Correlation Coefficient (r) Guidelines - **0** indicates no linear relationship. - **+1** indicates a perfect positive linear relationship - as one variable increases in its values, the other variable also increases in its values through an exact linear rule. - **-1** indicates a perfect negative linear relationship - as one variable increases in its values, the other variable decreases in its values through an exact linear rule. - **Values between 0 and 0.3 (0 and -0.3)** indicate a weak positive (negative) linear relationship through a shaky linear rule. - **Values between 0.3 and 0.7 (0.3 and -0.7)** indicate a moderate positive (negative) linear relationship through a fuzzy-firm linear rule. - **Values between 0.7 and 1.0 (-0.7 and -1.0)** indicate a strong positive (negative) linear relationship through a firm, linear rule. ## What Does a Scatter Plot Show? Scatterplots are a graphic display of pairs of data points on the X and Y axes. A scatterplot illustrates the linearity, signs, magnitude, and probability of a correlation. | Scatter Plot Type | Description | |---|---| | Positive Linear Association | Points rise from left to right in a linear fashion. | | Negative Linear Association | Points fall from left to right in a linear fashion. | | Nonlinear Association | Points do not form a straight line, but show a clear curve. | | No Association | Points are randomly scattered, showing no clear pattern. | ## How Does Range Truncation Affect Correlation Coefficients? - Range truncation is an artificial restriction of the range of X and Y that can reduce the strength of a correlation coefficient. ## How Do Outliers Affect Correlations? - Outliers are extreme scores. - They usually affect correlations by disturbing the trends in the data. - Range truncation removes outliers. ## Why Doesn't Correlation Prove Causation? - Since correlational studies do not create multiple levels of an independent variable and randomly assign subjects to conditions, they cannot establish a causal relationship. - **Causal Direction** - since correlations are symmetrical, A could cause B just as B could cause A. - **Bidirectional Causation** - two variables may affect each other. - **The third variable problem** - mediation or moderation. ## When Do Researchers Use Multiple Correlation? - Researchers use multiple correlations (R) to determine whether there is a relationship between three or more variables. - **Example:** We could measure age, television watching, and vocabulary and find that _R_ = +.61. ## When Do Researchers Use Multiple Regression? - Researchers use multiple regression to predict behavior measured by one variable based on scores on two or more other variables. - **Example:** We could estimate vocabulary size using age and television watching as predictor variables. ## What Are Quasi-Experimental Designs? - Quasi-experimental designs “seem like” experiments but are not. - They are valuable when researchers cannot randomly assign subjects to different treatments. - Researchers can use quasi-experiments to explore the effects of different treatments on preexisting groups of subjects or to study the same naturally occurring events, characteristics, and behaviors that they measure in correlational studies. ## Quasi-Experiments Differ From Actual Experiments - Quasi means “seeming like.” - Quasi-experiments superficially resemble experiments but lack their required manipulation of antecedent conditions and/or random assignment to conditions. - They may study the effects of preexisting antecedent conditions - life events or subject characteristics - on behavior. - **Example:** A quasi-experiment might compare the incidence of Alzheimer’s disease in patients who used ibuprofen since age 50 and those who did not. - In experiments, researchers randomly assign subjects to antecedent conditions that they create. An experimenter might randomly assign subjects to either daily ibuprofen or aspirin use and then measure their incidence of Alzheimer’s. ## When Should We Use Quasi-Experiments Instead of Experiments? - We should use quasi-experiments when we cannot or should not manipulate antecedent conditions. - **Example:** Quasi-experiments could study the effect of spouse abuse on the frequency of child abuse. ## What Is An Ex Post Facto Design? - Ex post facto means “after the fact.” - A researcher examines the effects of already existing subject variables (like gender or personality type) but does not manipulate them. ## What Is A Nonequivalent Groups Design? - A nonequivalent group design compares the effects of treatments on pre-existing groups of subjects. ## Longitudinal and Cross-Sectional Designs - **Longitudinal designs:** The same group of subjects is measured at different points in time to determine the effect of time on behavior。 - **Cross-sectional studies:** Subjects at different developmental stages (classes) are compared at the same point in time. ## Pretest/Posttest Design - In a pretest/ posttest design, a researcher measures behavior before and after an event. - This is quasi-experimental because there is no control condition. ## Solomon 4-Group Design This variation on a pretest-posttest design includes four conditions: 1. A group that received the pretest, treatment, and posttest. 2. A nonequivalent control group that received only the pretest and posttest. 3. A group that received the treatment and a posttest. 4. A group that only received the posttest.