Presentation on theme: "Fig 2.1 - Theory construction. A good theory will generate a host of testable hypotheses. In a typical study, only one or a few of these hypotheses can."— Presentation transcript:
Fig 2.1 - Theory construction. A good theory will generate a host of testable hypotheses. In a typical study, only one or a few of these hypotheses can be evaluated. If the evidence supports the hypotheses, our confidence in the theory they were derived from generally grows. If the hypotheses are not supported, confidence in the theory decreases and revisions to the theory may be made to accommodate the new findings. If the hypotheses generated by a theory consistently fail to garner empirical support, the theory may be discarded altogether. Thus, theory construction and testing is a gradual process.
Fig 2.2 - Flowchart of steps in a scientific investigation. As illustrated in a study by Cole et al. (1996), a scientific investigation consists of a sequence of carefully planned steps, beginning with the formulation of a testable hypothesis and ending with the publication of the study, if its results are worthy of examination by other researchers.
A theory is a system of interrelated ideas used to explain a set of observations. A hypothesis is a tentative statement about the relationship between two or more variables. Participants or subjects are the organisms whose behavior is systematically observed in a study Data collection techniques allow for empirical observation and measurement Operational definitions are used to clarify precisely what is meant by each variable Statistics are used to analyze data and decide whether hypotheses were supported Findings are shared through reports at scientific meetings and in scientific journals – periodicals that publish technical and scholarly material ◦ Advantages of the scientific method: clarity of communication and relative intolerance of error
Quantitative Research ◦ Assume variables can be identified and the relationships between them measured using statistics, with the aim of inferring a cause- effect relationship (except for in correlational studies) ◦ Data typically in the form of “numbers” that are easy to summarize and submit to statistical analysis ◦ Require operational definitions and the measurement of the dependent variable must be reliable and objective, with as few extraneous variables as possible ◦ Often laboratory experiments or correlational studies ◦ Focuses on validity and reliability or research design and are meant for generalization beyond the sample from which data are drawn Qualitative Research ◦ Gathered through direct interaction with participants or by observations in the field ◦ Data consists of text (from transcripts or field notes) ◦ Textual data is open ended, flexible, and more open to interpretation (“rich data”)
Self-fulfilling prophecy→ an expectation that is fulfilled because of the tendency of the person to act in ways that bring it about Population→ all cases in a group from which samples may be drawn for a study Representative sample→ one in which the characteristics of a group reflect the characteristics of the population as a whole ◦ In order to have a representative sample: Have a random sample→ each member of the population has an equal chance of inclusion The larger the random sample, the more representative it will be Generizability→ extent to which the results of a study can be applied to the population as a whole
Experiment = manipulation of one variable under controlled conditions so that resulting changes in another variable can be observed ◦ Detection of cause-and-effect relationships Independent variable (IV) = variable manipulated Dependent variable (DV) = variable affected by manipulation ◦ How does X affect Y? ◦ X= Independent Variable, and Y= Dependent Variable Remember: ◦ The Dependent Variable depends on the independent variable ◦ When applying to an experiment fill in the blanks ______________ depends on ________________
Experimental hypothesis→ predicts exact result of the manipulation of the IV on the DV Null hypothesis→ predicts that there will be no results or that the result will be due to chance
Experimental group – subjects who receive some special treatment in regard to the independent variable Control group – similar subjects who do not receive the special treatment ◦ Logic: Two groups alike in all respects (random assignment) Manipulate independent variable for one group only Resulting differences in the two groups must be due to the independent variable Extraneous and confounding variables ◦ Extraneous variables are any variables other than the independent variable that seem likely to influence the dependent variable in a specific study ◦ Confounding of variables occurs when two variables are linked together in a way that makes it difficult to sort out their specific effects
Fig 2.5 - The basic elements of an experiment. As illustrated by the Schachter study, the logic of experimental design rests on treating the experimental and control groups exactly alike (to control for extraneous variables) except for the manipulation of the independent variable. In this way, the experimenter attempts to isolate the effects of the independent variable on the dependent variable.
Independent samples (independent measures) ◦ Uses different subjects in each condition of the experiment Repeated measures ◦ Involves using the same subjects in each condition of an experiment ◦ Reduces extraneous variables Manipulate more than one independent variable ◦ Allows for study of interactions between variables Use more than one dependent variable ◦ Obtains a more complete picture of effect of the independent variable
Fig 2.6 - Manipulation of two independent variables in an experiment. As this example shows, when two independent variables are manipulated in a single experiment, the researcher has to compare four groups of subjects (or conditions) instead of the usual two. The main advantage of this procedure is that it allows an experimenter to see whether two variables interact.
Strengths: ◦ conclusions about cause-and-effect can be drawn Weaknesses: ◦ artificial nature of experiments may produce unnatural behavior (poor ecological validity) ◦ ethical and practical issues