Why Psychologists Conduct Experiments. Variables & Methods. Variables. Variables (con t) Main Types of Designs (con t) Main Types of Designs

Transcription

1 Variables & Methods Psychology 280 Lecture Orange Coast College 2/22/2005 Why Psychologists Conduct Experiments Test hypotheses derived from theories Test the effectiveness of a treatment or program Experiments differ from other research designs because they allow researchers to determine the causes of behavior. What were the goals of science? Describe behavior Predict behavior Determine the causes of behavior Explain behavior Variables A variable is any event, situation, or behavior that varies. Key properties of variables Specific instances will vary called values or levels Will have at least two levels Will have true numeric properties OR Will have non-numeric properties that usually identify different categories Variables (con t) Four general classifications 1. Situational variables describe characteristics of a situation or the environment. 2. Response variables are the responses or behaviors of individuals. 3. Participant or subject variables are individual differences. 4. Mediating variables are psychological processes that mediate the effects of a situational variable on a particular response. Example: Kitty Genovese and diffusion of responsibility Main Types of Designs Descriptive (Nonexperimental) Describe the characteristics of an existing phenomenon Little to no control over variables; no variables manipulated No cause and effect Historical (Nonexperimental) Relate events that have occurred in the past to current events Little to no control over variables; no variables manipulated No cause and effect Correlational design (Nonexperimental) Examine the relationship between variables Low to medium control over variables; no variables manipulated No cause and effect Main Types of Designs (con t) Quasi-experimental design Test for causal relationships among variables without having full control No variables manipulated or random assignment (observed) Moderate control over variables Results are suggestive, but trouble concluding cause and effect Experimental design Test for true cause and effect relationships Manipulating variables Randomly assigning participants into conditions High control over variables Reach better conclusions about cause and effect 1

2 Experimental Research An experiment must include: an independent variable (IV) and dependent variable(s) (DVs). An Independent Variable is manipulated (controlled) by the experimenter, and has at least two different conditions (e.g., treatment and control conditions). Experimental Research Dependent variables are measured by the experimenter, and are used to determine the effect of the Independent Variable. In most experiments, researchers measure several dependent variables to learn the effect of the independent variable. Examples of Experimental Research Let s watch the Psychologist and the Experiment Review: Nonexperimental Versus Experimental Methods Nonexperimental method Relationships studied using observations or measures of the variables of interest Problems with making causal statements using this method Think back to our discussion on the goals of science and determining the causes of behavior. What are the problems with the nonexperimental approach? Review: Nonexperimental Versus Experimental Methods Nonexperimental method (con t) There are two problems with making causal statements 1. Direction of cause and effect Temporal precedence; Covariation of cause & effect 2. Third variable problem Elimination of alternative explanations Review: Nonexperimental Versus Experimental Methods Experimental method One variable is manipulated and the other variable measured Attempts to eliminates the third variable problem (confounding) - Experimental control (example: controlling environment) - Randomization/random assignment (example: controlling participant characteristics) 2

3 Relationships Between Variables Relationships between two variables Do the levels of the two variables vary systematically together? Example: As the number of days absent increases, do grades in the class decrease also? For the purposes of describing relationships, we will use variables that have true numeric properties. Interval scale of measurement Ratio scale of measurement Positive linear relationship (covary in the same direction). Negative linear relationship (covary in opposite directions) Curvilinear relationship increases in one variable produce both increases and decreases in another variable No relationship between the variables 3

4 Research uses a variety of research designs to identify systematic relationships between variables. The better the research design, the less random error measured and the less uncertainty you will have attributing cause and effect The experimental design is the strongest of these designs due to its control features Review: Independent and Dependent Variables Independent variable (IV) Manipulated variable (cause) Some are participant variables that are not experimentally manipulated Dependent variable (DV) Measured variable (effect) Many are abstract constructs (stress, mood, cognitive performance) Review: Independent and Dependent Variables Remember that in the experimental method. One variable is manipulated (IV) and the other variable measured (DV) Review: Causality The inferences of cause and effect require three elements 1. Temporal order 2. Covariation between the two variables IV = cause; DV=effect 3. Need to eliminate plausible alternative explanations Operational Definitions of Variables Variable is an abstract concept that must be translated into concrete forms of observation or manipulation. A variable must be defined in terms of the specific method used to measure or manipulate it. This is called the operational definition. How do you define STRESS? Developing IV s & DV s Create a operational definition for your independent variable(s) and dependent variable(s) Choose the range of your independent variable Find a realistic range Select a range that shows effect A good idea is to do a pilot experiment (not required for class experiment) 4

5 Developing IV s & DV s (con t) The dependent variable needs to be reliable and valid Reliable = getting the same results when the measurement is repeated multiple times Validity = measuring what we intend to measure The closer you come to directly observing the behavior you are interested in, the less controversy (in terms of reliability and validity) you will have Developing IV s & DV s (con t) Suppose you are interested in memory and want to compare two ways of presenting material to be remembered. After one week you wish to measure how much your subjects remember. What should you measure? Ask them what they remember? What if they can t remember anything? Does that mean they remember nothing? Developing IV s & DV s (con t) Could you use a recognition test and determine the accuracy at picking previously presented material? Could you have them relearn the material and measure the percent of time saved by having learned it before? What type of results do you think each of these methods might give you? Consideration: Dependent variables, even those that appear to be directly observable, may be linked only indirectly to the behavior you are interested in Example: Writing About Emotional Experiences Pennebaker and Francis (1996) had several research questions: Does writing about emotional experiences cause people to experience better outcomes? [Note: Better than what? We need a comparison. That s why we have control groups.] Are cognitive changes that occur with writing about emotional experiences related to these outcomes? Research Example (continued) Pennebaker and Francis manipulated an independent variable, type of writing, using two levels (or conditions): emotional writing operationally defined as writing about your deepest thoughts and feelings about coming to college superficial writing operationally defined as describing in writing any particular object or event of your choosing as objectively and as dispassionately as you can Research Example (continued) They measured several dependent variables to operationally define outcome and cognitive changes : Health outcome was measured by the number of physician visits. Academic outcome was measured using students Grade Point Average (GPA). Cognitive change was measured using language frequency counts (e.g., number of insight and understand words). 5

6 Research Example (continued) Pennebaker and Francis hypotheses: Students in the emotional writing condition will have better health and academic outcomes than students in the superficial writing condition. Students in the emotional writing condition, compared to the superficial writing condition, will demonstrate cognitive change (i.e., more insight and understand words). Research Example: Causal Inferences Three conditions must be met before we can make a causal inference: Covariation of cause and effect: We must observe a relationship between the independent and dependent variables. For example, participants who write about emotional events have better health and academic outcomes than participants who write about superficial events. Thus, the two types of writing covary with the different outcomes. Is this enough???? As we ve learned, covariation is not enough for making a causal inference ( correlation does not imply causation ). More is needed. Causal Inferences (continued) Temporal precedence: The presumed cause precedes the effect. For example, writing about emotional events (the cause) comes before the beneficial health and academic outcomes (the effect). Establishing a time-order relationship can be tricky. An important question to ask is: How can we be sure that participants in the emotional writing condition were not healthier and more academically successful (e.g., higher GPA) than the superficial writing participants before they even wrote one word? (i.e., the effect precedes the cause)? Causal Inferences (continued) Elimination of alternative explanations: Using control techniques, we rule out other possible causes for the outcome. If the two groups differ in ways other than the emotional and superficial writing, these differences become alternative explanations for the study s findings. To eliminate alternative explanations, researchers use two main control techniques: holding conditions constant, and balancing. With proper use of control techniques, an experiment has internal validity (we will discuss this much later in class) Choosing A Method: Advantages of Multiple Methods Research findings based on a single method for making observations may be biased because of characteristics of the measurement process. A multimethod approach means that researchers use a variety of measures to examine a research question: Direct observation Surveys Unobtrusive measures Because direct observation and survey methods can be reactive (i.e., people react to being observed), unobtrusive measures provide an important alternative for gaining information about people. Choosing A Method: Advantages of Multiple Methods Experimental versus nonexperimental methods Considerations 1. Artificiality 2. Ethical and practical issues 3. Participant variables 4. Description of behavior 5. Successful predictions for the future 6. Advantages of multiple methods 6

7 Evaluating Research: Three Validities Construct validity the adequacy of the operational definitions Internal validity the ability to draw conclusions about causal relationships External validity the extent to which the results can be generalized to other populations and settings The End 7