by Selim Bora

1
Business Research Methods

• Lecture 3
• by Selim Bora

2
Experiments

• A response variable is a variable that measures
  an outcome or result of a study.
• An explanatory variable is a variable that we
  think explains or causes changes in the response
  variable.
• The individuals studied in an experiment are
  often called subjects.
• A treatment is any specific experimental
  condition applied to the subjects. If an
  experiment has several explanatory variables, a
  treatment is a combination of specific values of
  these variables.

3
Lurking Variables

• A lurking variable is a variable that has an
  important effect on the relationship among the
  variables in a study but is not one of the
  explanatory variables studied.
• Two variables are confounded when their effects
  on a response variable cannot be distinguished
  from each other. The confounded variables may be
  either explanatory variables or lurking variables.

4
Placebo Effect

• A placebo is a dummy treatment with no active
  ingredients.
• Many patients respond favorably to any treatment,
  even a placebo.

5
Randomized Cooperative Experiments

• Goal in designing an experiment is to ensure that
  it will show us the effect of the explanatory
  variables on the response variables.
• Confounding variables often prevent that. The
  remedy is to compare two or more treatments with
  randomized cooperative experiments.
• Random assignment to groups, and groups of large
  enough size are essential.
• Control groups allow u us to control the effects
  of lurking variables.

6
Logic of Experimental Design

• Randomization produces groups of subjects that
  should be similar in all respects before we apply
  any treatments.
• Comparative design ensures that influences other
  than the experimental treatments operate equally
  on all groups.
• Therefore, differences in the response variable
  must be due to the effects of the treatments.

7
Principals of Experimental Design

• Control the effects of lurking variables on the
  response, most simply by comparing two or more
  treatments.
• Randomize-use impersonal chance to assign
  subjects to treatments.
• Use enough subjects in each group to reduce
  chance variation in the results.

8
Statistical Significance

• An observed effect of a size that would rarely
  occur by chance is called statistically
  significant.
• Statistically significant results from randomized
  comparative experiments are the best available
  evidence that changing the explanatory variable
  really causes changes in the response.

9
Observational Studies

• Good studies are comparative even when they are
  not experiments.
• Observational studies of cause-and-effect
  questions are more impressive if they compare
  matched groups and measure as many lurking
  variables as possible to allow statistical
  adjustment.
• Matching involves selection of samples to be
  compared wih similar characteristics.
• Statistical adjustment is used to eliminate or
  reduce the effects of lurking variables.

10
Double-Blind Experiments

• In a double-blind experiment, neither the
  subjects nor the people who work with them know
  which treatment each subject is receiving.
• Allows equal treatment for all subjects except
  for the actual treatment the experiment is
  comparing.

11
Refusals, Nonadherers, Dropouts

• Clinical trials are medical experiments involving
  human subjects.
• Individuals may feel reluctant to join such
  experiments.
• Subjects who participate but dont follow the
  experimental treatment are called nonadherers.
• Experiments that continue over an extended period
  of time also suffer dropouts, subjects who begin
  the experiment but do not complete it.

12
Can Experiments Be Generalized?

• The first step is to be sure that our findings
  are statistically significant, that they are too
  strong to often occur just by chance.
• Most common weakness in experiments is that we
  cant generalize the conclusions widely.
• Some experiments apply unrealistic treatments,
  some use subjects from some special group such as
  college students, and all are performed at some
  specific place and time.
• We want to see similar experiments at other
  places and times confirm important findings.

13
Various Designs for Experiments

• In a completely randomized experimental design,
  all the experimental subjects are allocated at
  random among all the treatments.
• A matched pairs design compares two treatments by
  giving one to each pair of similar subjects or by
  giving both to the same subject in random order.
• In a block design, the random assignment of
  subjects to treatments is carried out separately
  within each block.
• A block is a group of experimental subjects that
  are known before the experiment to be similar in
  some way that is expected to affect the response
  to the treatments.
• Randomization, control, and adequate numbers of
  subjects remain the keys to convincing
  experiments.