Chpt 1. The Nature of Probability and Statistics. 1-5 Observational and Experimental Studies

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1 Chpt 1 The Nature of Probability and Statistics 1-5 Observational and Experimental Studies 1 /15

2 Chpt 1 Homework 1-5 Read pages p16 Applying the Concepts p /15

3 Chpt 1 Objectives Identify the difference between an observational study and an experimental study. 3 /15

4 Observation vs Experiment Observational studies Merely observe and record No manipulation or control of variables Draw conclusions from observations. Experimental studies Manipulate one or more variables Independent (Explanatory) variable is manipulated to determine effect on Dependent (Response) variable EXPERIMENTS AND OBSERVATION 4/15

5 Observational studies are most found where conditions or ethics prevent experimentation or manipulation of variables. The researcher is only able to observe the data as the data is found. Cigarettes and lung cancer Crime statistics in a neighborhood and success in school Observational Study 5/15

6 In an experiment subjects are randomly assigned to groups in an effort to make groups as alike as possible prior to manipulation of the independent variable. When true random assignment is not possible the study is quasi-experimental Experimental Study 6/15

7 Most studies (experimental or observational) are designed to investigate the relationship between two or more variables. Use extreme caution in hypothesizing a causal relationship between variables. In observational studies no causal relationship can be found. Relationship between variables 7/15

8 Independent Variable (also called explanatory or predictor variable) Manipulated (predetermined levels) by researcher Dependent Variable (also called response or outcome variable) Measured - looking for differences in the dependent variable between the levels of the independent variable. Changes in the dependent variable are attributed to (result from) changes in the independent variable. In other words: Most studies are attempting to discover if changes in the independent variable are accompanied (whether causal or not) by changes in the dependent variable and that those changes are not simply due to chance. Independent and Dependent Variables 8/15

9 Suppose we wish to study the effect of eating breakfast on student test results. Independent variable is manipulated. Subjects would be randomly assigned to two groups: group 1: eats breakfast, group 2: skips breakfast. Dependent variable is measured for each group. Measure test results for group 1 (eats breakfast), measure CST results for group 2 (no breakfast). The test results would be compared between the groups. Example 9/15

10 Be careful not to confuse variables with data. Independent variable is Breakfast. Data (values the variable can take) - yes / no Categorical (also binary or binomial) Dependent variable is CST score. Measured number value of test result Quantitative, interval Variables vs. Data 10/15

11 Subjects watch a brief video of a two car collision. Subjects are then given one of two questionnaires about the crash. One questionnaire asks about the two cars that collided, the second questionnaire asks about the cars that smashed together. Both questionnaires asked if any windows were broken in the accident. Independent variable: Phrasing (of questionnaire) Manipulated by randomly assigning questionnaires to the subjects. Data (values of the variable) - collided / crashed Categorical (also binary or binomial) Dependent variable: recall (memory) Data (values of the variable) - yes / no Categorical Car Crash (Collide or Crashed) 11/15

12 In an experiment a control group is a group of subjects that are not exposed to a manipulation. A treatment group is subject to the variable manipulation. Example: In medical research a control group would be given no medication or a placebo. The treatment group(s) would get the actual medication (perhaps different dosages to create multiple treatment groups.. Control Group 12/15

13 Often there are more than the two groups. There might be a control group that receives no treatment, a group that receives only counseling, a group that receives a placebo, and a group that receives the treatment. The grouping will account for the Hawthorne Effect that when subjects know they are in an experiment they will consciously or unconsciously change their behavior. Hawthorne Effect 13/15

14 Confounding variables are variables that alter the measures in the dependent variable that are not part of the study. Changes in the confounding variable result in changes in the dependent variable, but may be unknown or uncontrollable by the researcher. The confounding variables are either unknown to the researcher or cannot be separated from the independent variable and thus cannot be eliminated. The confounding variables are not just any variable that might influence the dependent variable. Most influencing variables are controlled via random assignment to groups. Confounding Variables 14/15

15 Suppose we wish to investigate the effects of differing therapies on depression. Therapy takes time to have an effect, and as the study progresses the lives of our subjects will change. Those changes may have profound effects on the level of depression experienced by the subject. The changes might be unknown to the researcher and certainly cannot be eliminated from the lives of the subjects. Variables such as gender, race, religion are not confounding variables because they are controlled through assignment to treatment groups. Age might be a confounding variable because we cannot stop subjects from growing older. Example of Confounding 15/15

Chapter 2 Quantitative, Qualitative, and Mixed Research

1 Chapter 2 Quantitative, Qualitative, and Mixed Research This chapter is our introduction to the three research methodology paradigms. A paradigm is a perspective based on a set of assumptions, concepts,