Terminology Experiment: A research method in which the investigator actively manipulates one or more factors to observe their effect upon some behavior, while controlling other relevant factors. EXAMPLE: This spring and summer I am growing tomato plants in my backyard. I am using seed starter boxes for the first time and wasn’t sure how compact to pack the dirt in the seed starter boxes. In one box, I packed the dirt in firmly and in the other box I packed the dirt in loosely and I labeled the boxes so that I would remember which was packed firmly or loosely. Throughout the spring I have been monitoring the growth of the plants in centimeters to see which was the best way to pack the dirt and in the summer, I measured the plants as well as counted the number of tomatoes each plant produced to see if there was a difference between the way in which the dirt was packed. The results will influence which way I plant my tomato seeds next year IV= A factor or condition that is deliberately manipulated to determine if it causes change in behaviors or conditions. DV= A factor or condition that is measured at the end of an experiment and is presumed to vary as a result of the independent variable. Hypothesis: The first step in a scientific investigation is to translate a theory or an intuitive idea into a testable hypothesis. Scientists make and test predictions called hypotheses. A hypothesis is a tentative statement about the relationship between two or more variables. Variables are any measurable conditions, events, characteristics, or behaviors that are controlled or observed in a study. If we predicted that putting people under time pressure would lower the accuracy of their time perception, the variables in our study would be time pressure and accuracy of time perception. To be testable, scientific hypothesis must be formulated precisely, and the variables under study must be clearly define. For the above example, I would need to make a hypothesis, which is a testable statement made about what I expect to find regarding the relationship between the IV and DV. For example, for the above study I may make the hypothesis that seeds planted in firmly packed dirt will demonstrate better growth. At the end of the study I will be able to see whether my hypothesis was correct or not. My hypothesis should be based on some prior knowledge or theory and I need to have some sort of comparison group with which I am comparing it. Independent Variable (IV): The experimental factor or factors an investigator manipulates, i.e., the treatment itself. A factor or condition that is deliberately manipulated to determine if it causes a change in behaviors or conditions. A factor may have different levels. For example, in the power point when we reviewed a study of the best way to plant tomatoes the independent variable was compactness of the dirt (firm or loose) and had two levels (Level 1= firm, level 2= loose). There was one IV with 2 levels. 1 Dependent Variable (DV): The behavior you observe, or the outcome that might be affected by the IV. The dependent variable will always involve some measurement. For example, the DV when you measured the best way to plant tomatoes was (1) growth in centimeters and (2) number of tomatoes produced. In this case there were two different ways the outcome was measured (2 different DVs). Operational Definition: A definition that specifies the procedures used to measure some variable or to produce some phenomenon. Experimental Condition: The condition in which participants are exposed to the IV. Control Condition: The condition in which participants are NOT exposed to the IV. This condition provides a baseline for comparison with participants exposed to the independent variable. Confounding variables and Extraneous variables: Experimenters concentrate on making sure that the experimental and control groups are alike on a limited number of variables that could have a bearing on the results of the study. These variables are called extraneous, secondary, or nuisance variables. Extraneous variables are any variables other than the independent variable that seem likely to influence the dependent variable in a specific study. If possible, it is good to identify what variables may be extraneous and control for them. “Does misery love company? This question intrigued social psychologist Stanley Schachter. When people feel anxious, he wondered, do they want to be left alone, or do they prefer to have others around? Schachter’s review of relevant theories suggested that in times of anxiety people would want others around to help them sort out their feelings. Thus, his hypothesis was that increases in anxiety would cause increases in the desire to be with others, which psychologists call the need for affiliation. In Schachter’s study, one extraneous variable would have been the participants’ tendency to be sociable. Why? Because subjects’ sociability could affect their desire to be with others (the dependent variable). If the participants in one group had happened to be more sociable (on the average) than those in the other group, the variables of anxiety and sociability would have been con-founded. A confounding of variables occurs when two variables are linked in a way that makes it difficult to sort out their specific effects. When an extraneous variable is confounded with an independent variable, a researcher cannot tell which is having what effect on the dependent variable. Unanticipated confoundings of variables have wrecked innumerable experiments. That is why so much care, planning, and forethought must go into designing an experiment. A key quality that separates a talented experimenter from a mediocre one is the ability to foresee troublesome extraneous variables and control them to avoid confoundings.” (Weiten, 2016) Weiten (2016). Psychology: Themes and Variations, 10th Edition. Belmont, CA: Thomas Wadsworth. 2 Ethics in research Figure 2.12 Ethics in research - For further review of ethical requirements see textbook pages 53-57 (pdf edition 52-55), and slides 38-39 (ebook section 2-5). - For further information regarding evaluating research see textbook pages 49-53 (pdf edition 47-52) and slides 20 & 37 (ebook section 2-4). 3 Causation: The cause-and-effect relation in which a given event, the cause, produces an observed result, the effect. For example, in the experiment in which a neural impulse was measured, the longer distance from ankle to brain than from shoulder to brain caused people to react more slowly. Experimental design and methodology determines whether or not cause-and-effect statements can be made. • If I observe that people who drive sports cars get more tickets than people who drive family cars, can I say that driving a sports car causes you to get more speeding tickets? • Suppose I randomly choose people and randomly give them a sports car or a family car to drive for 6 months and track the number of tickets they get. At the end of 6 months, the people driving sports cars received more tickets. Can I conclude that driving sports cars results in more tickets? • In which example can I make this causal statement with more confidence? It’s how you design your research study that allows you to be more or less able to make causal statements with any kind of certainty. Observational Studies: Observational studies involve the observation of phenomena of interest as they present themselves to the scientist. Unlike active manipulation in an experiment, observational studies involve examination of the relation between two variables as they naturally occur in the environment. The advantage of observational studies is that the relation between variables that cannot be manipulated experimentally can be studied (e.g., poverty and nutrition, social isolation and language development). The disadvantage is that cause-and effect relations between the variables are much more difficult to establish. For example, let's assume that self-esteem of attractive people is higher than the self-esteem of average-looking people. It is impossible to say that attractiveness causes people to feel good about themselves (have high self-esteem). There is only a relation between the two. The first factor could cause the second factor; the second factor could cause the first factor; or the relation may be caused by some third factor. For example, attractive people may be given more opportunities (a third factor) to experience or learn new things by the people around them and derive their high self esteem from good performance in these situations. 4 Correlation: A correlation is a statistical measure of the relation between two factors that varies from -1 through 0 to +1. As a correlation approaches +1 or -1 the relation becomes stronger. Negative and positive correlations of the same value (e.g., +0.75 and -0.75) are equally strong. Just because a correlation is negative does not mean it reflects a weaker relation than a positive correlation. Correlations of 0 or near 0 indicate that there is no relation between the two factors. Positive Correlation: Both factors increase together (e.g., age and the amount of hair losses in men, amount of time studying and grades). Positive correlations are above 0. Years of education Positive 20 16 12 8 4 0 0 5 10 15 20 25 Annual Income in Thousands Negative Correlation: As one factor increases, the other decreases (e.g., as temperature decreases the number of babies conceived increases, as self-esteem increases anxiety decreases). Negative correlations are below 0. Negative Dental problems requiring care 20 16 12 8 4 0 0 5 10 15 20 25 Annual income in thousands 5 Name Course and section number Assignment 1: Designing an Experiment Your professor has designed a research study to examine the effects of alcohol on reaction time. She has 24 subjects. Eight receive 2 ounces of vodka, eight students receive 4 ounces of alcohol, and eight students receive 4 ounces of alcohol flavored and scented non-alcoholic beverage. Following consumption of the beverage, they play a video driving game in which their responses are timed. They do not know what they are drinking nor what the purpose of the study is, but the person administering the game does know whether and how much alcohol they received. Using the above study, answer the following questions. • What is the hypothesis? ( points) • What is/are the Independent variable(s). How is it/are they operationally defined?(this is where you discuss the levels) (1 points) • What is/are the Dependent variable(s). How is it/are they operationally defined? (1 points) • What is the Experimental group(s)? ( point) • What is the Control group? ( point) • Describe some problems with this study. How well does it meet the ethical requirements? ( points) • What biases (more than one) in research may be a factor in this study and how have these issues been addressed or could researchers address these issues? ( s points) 6
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