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Experiments vs Non-Experiments
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Experiments vs Non-Experiments
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Experiments vs Non-Experiments

      An experiment is any study in which a treatment is introduced.

  A new method of teaching

      A non-experimental study does not introduce a treatment.

  Comparing opinions from natural groups

 

Experiments

      Any study in which a treatment is introduced is an experiment.

      Control: Scientists investigate the effect of various factors one at a time in an experiment.

      An experiment has at least one independent variable and at least one dependent variable.

      A true experiment involves random assignment of participants to treatment groups.

 

Treatment Groups

      Experimental Group: group receiving treatment

      Control Group: group not receiving treatment

  Represents expected results for experimental group if no treatment is given

  Represents population before treatment or if no treatment.

      Secondary Experimental Group: receives treatment of lesser interest

Randomization

      True experiment involves assignment to treatment groups based on random selection

      All participants have equal chance of being chosen for experimental group or control group

      The larger the number of participants the greater the chance that groups will represent the population

 

Basic Terms of Sampling

      Population: set of all cases of interest. For example:

    current students at your institution

    current residents of your city

    citizens of the United States

    citizens in North America

      Sampling Frame: list of the members of a population.

    For example, registrar’s list of all currently registered students

      Sample: subset of the population used to represent the population.

    Students in your class as a sample of current students at your institution (or your city, United States, North America)

      Element: each member of the population.

Obtaining a Sample (continued)

      Goal: Sample should represent the population.

   Characteristics of participants in the sample should be similar to those of the entire population.

   Example: Which sample represents a population that is 30% freshman, 30% sophomore, 20% junior, 20% senior?

 

              Sample A                                       Sample B   

30 freshmen, 30 sophomores,        60 freshmen, 60 sophomores,

20 juniors, 20 seniors                     40 juniors, 40 seniors

 

Both! But note: The samples are representative on one feature only!

Obtaining a Sample (continued)

      A biased sample occurs when the characteristics of the sample differ systematically from those of the target population.

  A sample may under-represent a segment of the population, or

  over-represent a segment of the population.

    For example, most samples in psychology research overrepresent college students and underrepresent individuals who are not in college.

    Most research underrepresents individuals from diverse cultures.

Approaches to Sampling

      Sampling refers to the procedures used to obtain a sample.

 

      Two basic approaches to sampling are

 

  nonprobability sampling, and

  probability sampling.

Approaches to Sampling (continued)

      Nonprobability sampling: No guarantee that each member of the population has an equal chance of being included in the sample.

   “Convenience sampling” occurs when the researcher selects individuals who are available and willing to respond to the survey.

  Examples: Magazine surveys, Internet surveys

   Lots of psychological research uses convenience samples (but this can be OK).

Approaches to Sampling (continued)

      Probability sampling: All members of a population have an equal chance of being selected for the survey (this is called a “simple random sample”).

   Need to have a sampling frame (list) of people in the population, or

   use random-digit dialing (but not all members of the population may be included).

Approaches to Sampling (continued)

      Stratified Random Sample: The population is divided into subpopulations called “strata.”

      Random samples are then drawn from the strata.

      Stratified random sampling increases the likelihood that the sample will represent the population.

Variables

      Trait or characteristic with two or more categories

  Participants vary in terms of which category they belong

      Categories should be mutually exclusive

  Each participant belongs to one and only one category

      Categories should be exhaustive

  Variable has a category for each participant

Independent Variable

  Independent Variable (IV): A factor that researchers control or manipulate in order to determine the effect on behavior.

   A minimum of two levels: The treatment (experimental) condition and the control condition

   Secondary treatment level is preferred

   Presumed to be the cause

Dependent Variable

  Dependent Variable (DV): The measure of behavior that is used to assess the effect of the independent variable.

   In most psychology research, several dependent variables are measured to assess the effects of the independent variable.

   Presumed to be the effect of treatment

Experimental Design

      Design 1:

  Group A             R O X O

  Group B             R O     O

      Design 2:

  Group A             R  X O

  Group B             R      O

      Solomon Randomized 4-group Design:

  Group A             R O X O

  Group B             R O     O

  Group A             R      X O

  Group B             R         O

Experiments

      Look for Cause and Effect Relationships

      Must make effort to eliminate plausible alternative explanations for differences between Experimental and Control Groups

      No randomization with treatment is still an experiment, but not true experiment.

 

Non-Experimental Studies

      Variables are often still referred to as IV and DV

      Independent Variable

  One presumed to be cause

  Observed first

  Better referred to as Predictor

      Dependent Variable

  One presumed to be effect

  Observed later

  Better referred to as Criterion

 

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