15
Jeff Beard Lisa Helma David Parrish Start Presentation

Jeff Beard Lisa Helma David Parrish Start Presentation

Embed Size (px)

Citation preview

Page 1: Jeff Beard Lisa Helma David Parrish Start Presentation

Jeff BeardLisa Helma

David Parrish

Start Presentation

Page 2: Jeff Beard Lisa Helma David Parrish Start Presentation

To establish a possible cause and effect between your independent and dependent variable

When it is possible to control all variables that influence the outcome, except for the independent variable. Then when the independent variable influences the3 dependent variable, we can say the independent variable caused, or probably caused the dependent variable

Page 3: Jeff Beard Lisa Helma David Parrish Start Presentation

Random assignment Control over extraneous variables Manipulation of the treatment conditions Outcome measures Group comparisons Threats to validity

Page 4: Jeff Beard Lisa Helma David Parrish Start Presentation

The process of assigning individuals at random to groups

Any bias in the personal characteristics of individuals in the experiment is distributed equally◦ “Equating the groups”

Page 5: Jeff Beard Lisa Helma David Parrish Start Presentation

Extraneous factors are any influences in the selection of participants, the procedures, the statistics, or the design likely to affect the outcome and provide an alternative explanation for our results than what we expected.

Some control procedures include:◦ Pretests, Covariates, Matching of Participants,

Homogeneous Samples, and Blocking Variables

Page 6: Jeff Beard Lisa Helma David Parrish Start Presentation

May be used to equate the characteristics of a group

Measures a specific attribute or characteristic in participants before they receive treatment.

Disadvantages:◦ Takes time and effort to administer◦ Can raise the participants’ expectations about the

outcome◦ May influence the experimental treatment◦ May affect post test scores

Page 7: Jeff Beard Lisa Helma David Parrish Start Presentation

Variables that the researcher controls that relate to the dependent variable but not the independent variable

Scores must be adjusted to account for covariance.

Page 8: Jeff Beard Lisa Helma David Parrish Start Presentation

Matching is the process of identifying one or more personal characteristics that influence the outcome and assigning individuals with that characteristic equally to the experimental and control groups◦ Ex: gender, pretest scores, or individual abilities

Page 9: Jeff Beard Lisa Helma David Parrish Start Presentation

Selecting people who vary little in personal characteristics.

Page 10: Jeff Beard Lisa Helma David Parrish Start Presentation

A blocking variable is a variable that the researcher controls before the experiment starts by dividing (blocking) the participants into subgroups and analyzing the impact of each subgroup on the outcome◦ Ex: gender, grade level

Makes homogeneous subgroups

Page 11: Jeff Beard Lisa Helma David Parrish Start Presentation

Identify a treatment variable: The independent variable the researcher manipulates in order to determine how it affects the outcome, or dependent variable (how teaching style affects learning in individuals with Aspergers)

Page 12: Jeff Beard Lisa Helma David Parrish Start Presentation

Identify the conditions (or levels) of the variable: Need to identify the categories of a treatment variable. (i.e. video vs. audio vs. reading teaching styles)

Page 13: Jeff Beard Lisa Helma David Parrish Start Presentation

Manipulate the treatment conditions: The researcher physically manipulates one or more of the conditions in the independent variable, so that the participants in each group experience something different.

Page 14: Jeff Beard Lisa Helma David Parrish Start Presentation

Dependent Variable Group Comparison: Obtaining scores for

individuals or groups and comparing the means and variance within the group and between the groups.

Page 15: Jeff Beard Lisa Helma David Parrish Start Presentation

Experiments must be designed in a way that minimizes compromises in drawing good conclusions from the scores obtained.

Design issues may threaten the experiment so that the conclusions reached from data may provide a false reading about probable cause and effect

Two commonly discussed threats are of internal validity and external validity.