NonExperimental designs Correlational Quasiexperimental designs Psych 231 Research
![Non-Experimental designs: Correlational & Quasi-experimental designs Psych 231: Research Methods in Psychology Non-Experimental designs: Correlational & Quasi-experimental designs Psych 231: Research Methods in Psychology](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-1.jpg)
![n Sometimes you just can’t perform a fully controlled experiment n n Because of n Sometimes you just can’t perform a fully controlled experiment n n Because of](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-2.jpg)
![n Looking for a co-occurrence relationship between two (or more) variables n Used for n Looking for a co-occurrence relationship between two (or more) variables n Used for](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-3.jpg)
![n Looking for a co-occurrence relationship between two (or more) variables n Example 1: n Looking for a co-occurrence relationship between two (or more) variables n Example 1:](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-4.jpg)
![Hours study Exam perf. X 6 1 Y 6 2 5 6 3 4 Hours study Exam perf. X 6 1 Y 6 2 5 6 3 4](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-5.jpg)
![Linear Form Non-linear Linear Form Non-linear](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-6.jpg)
![Positive Negative Y • X & Y vary in the same direction Direction Y Positive Negative Y • X & Y vary in the same direction Direction Y](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-7.jpg)
![r = -1. 0 “perfect negative corr. ” -1. 0 r = 0. 0 r = -1. 0 “perfect negative corr. ” -1. 0 r = 0. 0](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-8.jpg)
![Rel A r = -0. 8 Rel B r = 0. 5 -. 8 Rel A r = -0. 8 Rel B r = 0. 5 -. 8](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-9.jpg)
![n For our example, we have a linear relationship, it is positive, and fairly n For our example, we have a linear relationship, it is positive, and fairly](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-10.jpg)
![n Looking for a co-occurrence relationship between two (or more) variables n n n n Looking for a co-occurrence relationship between two (or more) variables n n n](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-11.jpg)
![n n For descriptive case, it doesn’t matter which variable goes where n Correlational n n For descriptive case, it doesn’t matter which variable goes where n Correlational](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-12.jpg)
![n Advantages: n Doesn’t require manipulation of variable • Sometimes the variables of interest n Advantages: n Doesn’t require manipulation of variable • Sometimes the variables of interest](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-13.jpg)
![n Disadvantages: n Don’t make casual claims • Third variable problem • Temporal precedence n Disadvantages: n Don’t make casual claims • Third variable problem • Temporal precedence](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-14.jpg)
![n Disadvantages: n Example 2: Suppose that you notice that kids who sit in n Disadvantages: n Example 2: Suppose that you notice that kids who sit in](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-15.jpg)
![What are they? n n n Almost “true” experiments, but with an inherent confounding What are they? n n n Almost “true” experiments, but with an inherent confounding](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-16.jpg)
![n Advantages n n n Allows applied research when experiments not possible Threats to n Advantages n n n Allows applied research when experiments not possible Threats to](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-17.jpg)
![Program evaluation n – Research on programs that is implemented to achieve some positive Program evaluation n – Research on programs that is implemented to achieve some positive](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-18.jpg)
![n Nonequivalent control group designs n with pretest and posttest (most common) (think back n Nonequivalent control group designs n with pretest and posttest (most common) (think back](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-19.jpg)
![n Interrupted & Non-interrupted time series designs n Observe a single group multiple times n Interrupted & Non-interrupted time series designs n Observe a single group multiple times](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-20.jpg)
- Slides: 20
![NonExperimental designs Correlational Quasiexperimental designs Psych 231 Research Methods in Psychology Non-Experimental designs: Correlational & Quasi-experimental designs Psych 231: Research Methods in Psychology](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-1.jpg)
Non-Experimental designs: Correlational & Quasi-experimental designs Psych 231: Research Methods in Psychology
![n Sometimes you just cant perform a fully controlled experiment n n Because of n Sometimes you just can’t perform a fully controlled experiment n n Because of](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-2.jpg)
n Sometimes you just can’t perform a fully controlled experiment n n Because of the issue of interest Limited resources (not enough subjects, observations are too costly, etc). • • • n Surveys Correlational Quasi-Experiments Developmental designs Small-N designs This does NOT imply that they are bad designs n Just remember the advantages and disadvantages of each Non-Experimental designs
![n Looking for a cooccurrence relationship between two or more variables n Used for n Looking for a co-occurrence relationship between two (or more) variables n Used for](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-3.jpg)
n Looking for a co-occurrence relationship between two (or more) variables n Used for • Descriptive research • do behaviors co-occur? • Predictive research • is one behavior predictive of another? • Reliability and Validity • Does your measure correlate with others (and itself)? • Evaluating theories • Look for co-occurrence posited by theory. Correlational designs
![n Looking for a cooccurrence relationship between two or more variables n Example 1 n Looking for a co-occurrence relationship between two (or more) variables n Example 1:](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-4.jpg)
n Looking for a co-occurrence relationship between two (or more) variables n Example 1: Suppose that you notice that the more you study for an exam, the better your score typically is. n n This suggests that there is a relationship between study time and test performance. We call this relationship a correlation. n 3 properties: form, direction, strength Correlational designs
![Hours study Exam perf X 6 1 Y 6 2 5 6 3 4 Hours study Exam perf. X 6 1 Y 6 2 5 6 3 4](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-5.jpg)
Hours study Exam perf. X 6 1 Y 6 2 5 6 3 4 3 2 Y 6 Scatterplot 5 4 3 2 1 1 2 3 4 5 6 X
![Linear Form Nonlinear Linear Form Non-linear](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-6.jpg)
Linear Form Non-linear
![Positive Negative Y X Y vary in the same direction Direction Y Positive Negative Y • X & Y vary in the same direction Direction Y](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-7.jpg)
Positive Negative Y • X & Y vary in the same direction Direction Y X • X & Y vary in opposite directions X
![r 1 0 perfect negative corr 1 0 r 0 0 r = -1. 0 “perfect negative corr. ” -1. 0 r = 0. 0](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-8.jpg)
r = -1. 0 “perfect negative corr. ” -1. 0 r = 0. 0 “no relationship” r = 1. 0 “perfect positive corr. ” 0. 0 The farther from zero, the stronger the relationship Strength +1. 0
![Rel A r 0 8 Rel B r 0 5 8 Rel A r = -0. 8 Rel B r = 0. 5 -. 8](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-9.jpg)
Rel A r = -0. 8 Rel B r = 0. 5 -. 8 -1. 0 . 5 0. 0 Which relationship is stronger? Rel A, -0. 8 is stronger than +0. 5 Strength +1. 0
![n For our example we have a linear relationship it is positive and fairly n For our example, we have a linear relationship, it is positive, and fairly](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-10.jpg)
n For our example, we have a linear relationship, it is positive, and fairly strong Y 6 5 4 3 2 1 1 Scatterplot 2 3 4 5 6 X
![n Looking for a cooccurrence relationship between two or more variables n n n n Looking for a co-occurrence relationship between two (or more) variables n n n](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-11.jpg)
n Looking for a co-occurrence relationship between two (or more) variables n n n Explanatory variables (Predictor variables) Response variables (Outcome variables) Example 1: Suppose that you notice that the more you study for an exam, the better your score typically is n For our example, which variable is explanatory and which is response? And why? n It depends on your theory of the causal relationship between the variables Correlational designs
![n n For descriptive case it doesnt matter which variable goes where n Correlational n n For descriptive case, it doesn’t matter which variable goes where n Correlational](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-12.jpg)
n n For descriptive case, it doesn’t matter which variable goes where n Correlational analysis For predictive cases, put the response variable on the Y axis n Regression analysis Y 6 Response (outcome) variable 5 4 3 2 1 1 2 3 4 5 6 X Explanatory (predictor) variable Scatterplot
![n Advantages n Doesnt require manipulation of variable Sometimes the variables of interest n Advantages: n Doesn’t require manipulation of variable • Sometimes the variables of interest](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-13.jpg)
n Advantages: n Doesn’t require manipulation of variable • Sometimes the variables of interest can’t be manipulated n n Allows for simple observations of variables in naturalistic settings (increasing external validity) Can look at a lot of variables at once Correlational designs
![n Disadvantages n Dont make casual claims Third variable problem Temporal precedence n Disadvantages: n Don’t make casual claims • Third variable problem • Temporal precedence](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-14.jpg)
n Disadvantages: n Don’t make casual claims • Third variable problem • Temporal precedence • Coincidence (random co-occurence) n Correlational results are often misinterpreted Correlational designs
![n Disadvantages n Example 2 Suppose that you notice that kids who sit in n Disadvantages: n Example 2: Suppose that you notice that kids who sit in](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-15.jpg)
n Disadvantages: n Example 2: Suppose that you notice that kids who sit in the front of class typically get higher grades. n This suggests that there is a relationship between where you sit in class and grades. Daily Gazzett Children who sit in the back of the classroom receive lower grades than those who sit in the front. Possibly implied: “[All] Children who sit in the back of the classroom [always] receive worse grades than [each and every child] who sits in the front. ” Better: “Researchers X and Y found that children who sat in the back of the classroom were more likely to receive lower grades than those who sat in the front. ” Misunderstood Correlational designs Example from Owen Emlen (2006)
![What are they n n n Almost true experiments but with an inherent confounding What are they? n n n Almost “true” experiments, but with an inherent confounding](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-16.jpg)
What are they? n n n Almost “true” experiments, but with an inherent confounding variable General types 1) An event occurs that the experimenter doesn’t manipulate • Something not under the experimenter’s control • (e. g. , flashbulb memories for traumatic events) 2) Interested in subject variables – high vs. low IQ, males vs. females 3) Time is used as a variable Quasi-experiments
![n Advantages n n n Allows applied research when experiments not possible Threats to n Advantages n n n Allows applied research when experiments not possible Threats to](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-17.jpg)
n Advantages n n n Allows applied research when experiments not possible Threats to internal validity can be assessed (sometimes) Disadvantages n n n Threats to internal validity may exist Designs are more complex than traditional experiments Statistical analysis can be difficult • Most statistical analyses assume randomness Quasi-experiments
![Program evaluation n Research on programs that is implemented to achieve some positive Program evaluation n – Research on programs that is implemented to achieve some positive](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-18.jpg)
Program evaluation n – Research on programs that is implemented to achieve some positive effect on a group of individuals. – – e. g. , does abstinence from sex program work in schools Steps in program evaluation – – – Needs assessment - is there a problem? Program theory assessment - does program address the needs? Process evaluation - does it reach the target population? Is it being run correctly? Outcome evaluation - are the intended outcomes being realized? Efficiency assessment- was it “worth” it? The the benefits worth the costs? Quasi-experiments
![n Nonequivalent control group designs n with pretest and posttest most common think back n Nonequivalent control group designs n with pretest and posttest (most common) (think back](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-19.jpg)
n Nonequivalent control group designs n with pretest and posttest (most common) (think back to the second control lecture) Independent Non-Random Dependent Variable Assignment Measure Experimental group Dependent Variable Measure participants Measure Control group Measure – But remember that the results may be compromised because of the nonequivalent control group (review threats to internal validity) Quasi-experiments
![n Interrupted Noninterrupted time series designs n Observe a single group multiple times n Interrupted & Non-interrupted time series designs n Observe a single group multiple times](https://slidetodoc.com/presentation_image_h2/a00a5e2776e8617bc8259df4d6f77dde/image-20.jpg)
n Interrupted & Non-interrupted time series designs n Observe a single group multiple times prior to and after a treatment Obs Obs Treatment Obs Obs • Look for an instantaneous, permanent change • Interrupted – when treatment was not introduced by researcher, for example some historical event n Variations of basic time series design • Addition of a nonequivalent no-treatment control group time series OOOTOOO & OOO_OOO • Interrupted time series with removed treatment • If treatment effect is reversible Quasi-experiments
Correlational study
Experimental vs nonexperimental research
Experimental vs non experimental
Ap psychology unit 1 test
Correlational studies
Experimental method of psychology
Descriptive research design
What are the 5 types of non experimental research design
Descriptive
The kinds of quantitative research
What is a correlational study
Correlational research topics
Descriptive vs correlational vs experimental research
Correlational study example
Correlational research design
Sential
Strengths and weaknesses of correlational research
Goal of correlational research
Non experimental research correlational design
Complex correlational design
Strength and weaknesses of correlational research