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