What is the main difference between independent groups and within groups designs?

Introduction to RESEARCH DESIGNS  (not complete)

The design of an experiment is its general structure,

     or the experimenter's plan for testing the hypothesis.

   It is not its specific content (i.e. it does not depend on the types of IV's or DV's under study)

The design of an experiment is decided mainly on the basis of three factors:

     (1) the number of independent variables in the hypothesis(es),

     (2) the number of treatment conditions needed to make a fair test of the hypothesis, and

     (3) whether the same or different subjects are used in each of the treatment conditions.

A basic assumption behind each experimental design is that subjects in the experiment are typical of the population they represent.

     Researchers use a variety of procedures to obtain the most representative samples; ideally, they use random samples, in which each member of the population has an equal chance of being selected for the experiment.

     Random samples can be obtained through some form of probability sampling, through which the odds of any individual being selected are known or can be calculated. Simple random sampling, stratified random sampling, and cluster sampling are the most frequent examples of this approach.

      For practical reasons, nonprobabiliy samples are often used.

     Quota samples and accidental samples are the most common examples.

Researchers must be extremely cautious in generalizing their results from samples in which subjects were not chosen at random.

      After subjects have been selected for the experiment, they are assigned to treatment conditions in various ways:  eg. randomly, by using whole classes, or friends.  

How about random selection of people going into the Student Center?  How would you describe such a sample?

BASIC DESIGNS

     Several methods of classifying research designs can be used:   Single vs. Multi-variate, Between vs.Within-Subjects etc..

1. Between-Subjects designs are those in which different subjects take part in each condition of the experiment.  Examples of these types are:  Independent  (or Random) Group and Factorial.

     The name comes from the fact that we draw conclusions from between-subjects experiments by making comparisons between the behavior of different (independent) groups of subjects.

     We will look at two kinds of two-group between-subjects designs:  two independent (or random) groups and matched groups.  

NOTE****[Craig & Metz classify Matched Groups as one type of Blocked Designs.  The other is Repeated Measures]****

  The Independent Groups design is used when one independent variable must be tested at two treatment levels or values.

  Usually one of the treatment conditions is a control condition in which the subjects receive the zero value of the independent variable. The other condition is an experimental condition in which the subjects are given some nonzero value of the independent variable.

Diagram of Independent Groups Design:  

                 Treatment Group     Control Group

The Independent Groups design is based on the assumption that subjects are selected randomly from the population and randomly assigned to conditions.

When we use an independent groups design, we assume that randomization was successful.   That is, we must assume that the groups areequivalent (in all important respects) before application of the independent variable.  We then apply the independent variable and measure the difference between our groups

BUT WATCH OUT:  If treatment groups were initially different from each other on a variable related to the dependent variable of the experiment, the results will probably be confounded (contaminated).

     Sometimes, however, especially when the total number of subjects is small, we do not want to rely on randomization. Even with random assignment, sometimes treatment groups start out being different from each other in important ways. This is called sampling error.

     These differences can affect the dependent variable, and we may not be able to separate the effects of the independent variable from the effects of the initial differences between the groups.   This is the essence of confounding.

One-Way Analysis of Variance (ANOVA) Designs (AKA One-Way Factorial) are similar to Independent Groups but utilize more than two groups.

     Diagram of One-Way ANOVA Designs:

          Condition A            Condition B     Condition C           Condition n

                                        ....................

2.  Matched Group Designs {really matched subjects}are another type of Between Subjects designs.

     Instead of relying on randomization, we would use the matched groups approach.

      In a matched groups design, we select a variable that is highlyrelated (correlated) to the dependent variable and measure subjects on that variable.  (e.g.  using IQ in a study comparing teaching methods.)

For a two-matched group experiment, we form pairs of subjects having similar scores on the matching variable and then randomly assign one member of each pair to the experimental condition, and the other member is placed in the control condition.

     [A good example of  matched group designs are Twin Studies, which match subjects based on their genetic makeup;  e.g. identical vs fraternal twins].

     Matching is advantageous because we can increase the probability that our groups start out the same, at least on variables that we think matter.

     However, there are also disadvantages:   we do notalways knowwhat is bestto use as our matching variable.

     Because of the statistical tests used for matched groups, we want to be sure that our matching variable is really related to our dependent variable. If it is not, we will have less chance of showing whether the independent variable had an effect.

     Diagram of a Matched Groups Design:

Scores on Matching Variable:     Treatment A       Treatment B

     S1  98  --> To GroupA

     S2  96  --> To GroupB

     S3  89          .

     S4  87          .

     S5  86          .

     S6  71          .

     S7  68          .

     S8  65          .

3.   [Craig & Metz classify Repeated Measures Designs and Matched Group designs together as Blocked Designs]

     Repeated Measures designs measure the dependent variable on the same subjects under different treatment conditions.  (E.g. Before vs After)

     Diagram of Repeated Measures Designs:

                           Condition A  Condition B               Condition n

                                                                                ...........................

4.  Multivariate Designs -- also called Factorial Designs:

     These apply more than one independent variable in the same experiment.

     The simplest form of a Factorial Design is the 2 X 2.  i.e. Two IVs with two levels each.

Diagram of a 2 X 2 Factorial Design:

                          IV 1

                        Level A          Level B                          

          Level A

IV 2

     Level B

OR

                      Sound

    Level A   Level B

             Male

          Gender

          Female

5.  Single Subject Designs (Developed by B.F. Skinner)

6.  Covariate Designs (i.e. Statistical Control)

What is independent group design?

What is a within groups research design?

What is the difference between a within participants design and a between participants design quizlet?

What is an advantage of using independent groups design?