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Experimental Research

Experimental research is a crucial approach in scientific inquiries, beginning with a hypothesis and involving key elements such as modifying variables, random assignment, and comparing outcomes among subjects. The design includes defining treatment (independent variable) and response (dependent variable), utilizing control groups, and considering various validity types (internal and external). It’s essential to understand confounding variables, subject effects, and how to implement effective randomization. This overview covers fundamental concepts to ensure reliable results in experimental design.

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Experimental Research

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  1. Experimental Research

  2. Experiments • Begin with a Hypothesis • Modify Something in a Situation • Compare Outcomes • Cases or People are Termed “Subjects”

  3. Random Assignment • Probability of Equal Selection • Allows Accurate Prediction • An Alternative to Random Assignment is Matching

  4. Parts of the Classic Experiment • Treatment or Independent Variable • Dependent Variable • Pretest • Posttest • Experimental Group • Control Group • Random Assignment

  5. Variations on Experimental Design • Pre-experimental Design • One-shot Case Study • One-group Pretest-Posttest Design • Static Group Comparison • Quasi-Experimental and Special Designs

  6. Types of Validity • External Validity • Do the results apply to the broader population? • Internal Validity • Is the independent variable responsible for the observed changes in the dependent variable?

  7. Confounding Variables That Threaten Internal Validity • Maturation • Changes due to normal growth or predictable changes • History • Changes due to an event that occurs during the study, which might have affects the results

  8. Confounding Variables That Threaten Internal Validity • Instrumentation • Any change in the calibration of the measuring instrument over the course of the study • Regression to the Mean • Tendency for participants selected because of extreme scores to be less extreme on a retest • Selection • Any factor that creates groups that are not equal at the start of the study

  9. Confounding Variables That Threaten Internal Validity • Attrition • Loss of participants during a study; are the participants who drop out different from those who continue? • Diffusion of treatment • Changes in participants” behavior in one condition because of information they obtained about the procedures in other conditions

  10. Subject Effects • Participants are not passive • They try to understand the study to help them to know what they “should do” • This behavior termed “subject effects” • Participants respond to subtle cues about what is expected (termed “demand characteristics”) • Placebo effect: treatment effect that is due to expectations that the treatment will work

  11. Experimenter Effects • Any preconceived idea of the researcher about how the experiment should turn out • Compensatory effects

  12. Types of Control Procedures • General control procedures (applicable to virtually all research) • Control over subject and experimenter effects • Control through the selection and assignment of participants • Control through specific experimental design

  13. Principles of Experimental Design • Control the effects of lurking variables on the response, most simply by comparing two or more treatments • Randomize • Replicate

  14. Randomization • The use of chance to divide experimental units into groups is called randomization. • Comparison of effects of several treatments is valid only when all treatments are applied to similar groups of experimental units.

  15. How to randomize? • Flip a coin or draw numbers out of a hat • Use a random number table • Use a statistical software package or program • Minitab • www.whfreeman.com/ips

  16. Statistical Significance • An observed effect so large that it would rarely occur by chance is called statistically significant.

  17. A few more things… • Double-blind: neither the subjects nor the person administering the treatment knew which treatment any subject had received • Lack of realism is a major weakness of experiments. Is it possible to duplicate the conditions that we want?

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