Tuesday, September 5 Identify 3-4 characteristics of yourself. Do you think they are more due to NATURE or NURTURE? What is the difference between a representative sample and a random sample? What are some symptoms of ADHD? Today’s topic: Descriptive Methods and Correlation Upcoming Dates: Homework: Read pages 34-37 Work on terms Get onto college board to look over old FRQs Assessments: Test: Tuesday, September 19

Correlational Methods Describing behavior is the first step to predicting it. When observed variables seem to relate to each other, it is said they correlate.

Correlational Research A measure of the extent to which two factors vary together, and thus of how well either factor predicts the other. How are two things related? How strong is this relationship? Can the relationship shape predictions? Correlation coefficient – the mathematical expression of the relationship, ranging from -1 to +1 Measures how well either one predicts the other and how strong that relationship/prediction is (0 = no relationship) r = +0.37 r  correlation coefficient (relationship) +  indicates direction of relationship (positive or negative) 0.37  Indicates strength of relationship (0.00 to 1.00) Scatterplots – a graphed cluster of dots, each of which represents the values of two variables. The amount of scatter suggests the strength of the correlation (little scatter = more correlation)

Positive Correlation Two variables rise or fall together r = +1.00 The taller you are, the more you weigh The more you smoke, your risk of cancer increases As temperature rises, crime rate increases As the ocean level decreases, the fish population decreases r = +1.00

Negative Correlation Two variables relate inversely to each other – as one rises, the other falls. The more you brush your teeth, your risk of cavities decreases The more years spent in jail, the lower the education level The more you hold a baby, the less it cries The more hours spent watching TV, the less time spent doing HW r = -1.00

No Correlation Two variables do not seem to be related r = 0 People born later in the year and intelligence level r = 0

Correlation IS NOT Causation Correlations cannot fully predict future behaviors/attitudes, regardless of how strong the correlation coefficient is. Ex: low self esteem is correlated with depression, however this does not mean that low self-esteem directly causes depression. Knowing that 2 events are associated need not tell us anything about causation Could cause (1) Low self esteem Depression OR Could cause (2) Depression Low self esteem OR (3) Distressing events or biological predisposition Depression Could cause Low self esteem

Human Errors and Correlations Illusory correlation – the perception of a correlation where none exists Being cold and wet causes one to catch a cold Humans tend to perceive order in random events… we want order! We want to make sense and organize events! Random coincidences often don’t look random… Your chances of being dealt either of these hands are precisely the same: 1 in 2,598,960. Patterns and sequences occur naturally in random data, but we tend to interpret these patterns as meaningful data.

Correlational Research Pros: Can measure the extent of a relationship Cons: Correlation is not causation (just because two things are related does not mean one causes the other)

Experimental Methods Because many factors influence everyday behaviors/attitudes, psychologists need to isolate and control variables to establish cause and effect relationships. They do this using experiments.

Experiments a research method in which an investigator manipulates one or more factors (independent variables) to observe the effect on some behavior or mental process (the dependent variable). By random assignment of participants, the experimenter aims to control other relevant factors. Manipulate the factors of influence Hold other variables constant Unlike correlational studies that uncover naturally occurring relationships, an experiment manipulates a factor to determine its effect.

Variables – anything that can vary Independent variable The variable manipulated by the experimenter Dependent variable The outcome being studied as a result of the ind. variable Ex: Hypothesis – Pill X can reduce the effects of heart disease. Ind. - Pill X Dep. – effects of heart disease

Research groups Experimental group Control group Counterbalancing Participants that receive the independent variable Control group Participants NOT exposed to the independent variable Serves as a comparison for evaluating the effect of the ind. variable (sets a base-line) Counterbalancing Using participants as their own control group Ex: Hypothesis – Pill X can reduce the effects of heart disease. Group A receives Pill X, while Group B receives a placebo (fake drug). Exp. – Group A receiving Pill X (ind. variable) Control – Group B receiving placebo

Placebos Placebo – a substance or treatment that has no effect apart from a person’s belief in it. Placebo effect – a person receiving the placebo may report to positive effects due to a belief in the drug/treatment Single blind study – participants do not know if they are in the experimental or control group Double blind study – participants nor researchers know who is in the experimental or control group

Experiments Pros: Cons: Variables can be controlled and manipulated Can determine cause-and-effect Can be replicated Cons: Labs can not always duplicate real-life environments Can be expensive Sometimes not feasible; not ethical to manipulate certain variables

COMPARING RESEARCH METHODS Basic Purpose How Conducted What is Manipulated? Weakness Descriptive To observe and record behavior Case studies, surveys, or naturalistic observations Nothing No control of variables; single cases may be misleading Correlational To detect naturally occurring relationships; to assess how well one variable predicts another Compute statistical association, sometimes among survey responses Does not specify cause and effect Experimental To explore cause and effect Manipulate one or more factors; random assignment to groups Independent variable(s) Sometimes not feasible; results may not generalize to other contexts; not ethical to manipulate certain variables

Descriptive Statistics Once research is gathered, it has to be organized in a meaningful way

Descriptive Statistics Descriptive statistics simply describe a set of data Graphing your findings is usually helpful Lines graphs (frequency polygons) and bar graphs (histograms) are often used Central tendency measures statistical data in an attempt to mark the center of a distribution of data

Describing Data Measures of Central Tendency Mode (occurs the most in a distribution) Mean (arithmetic average of a distribution by adding the scores and then dividing by the number of scores) Median (middle score in a distribution, half the scores are above it and half are below it)

Describing Data Measures of Central Tendency

Describing Data Measures of Variability Range - the difference between the highest and lowest score in a distribution. Standard Deviation - a computed measure of how much scores vary around the mean score.

Statistical significance – if the sample averages are reliable and the difference between the sample averages are large than it is considered statistically significant It means the difference is probably not due to chance

Describing Data Measures of Variability Normal Curve (bell shaped) a symmetrical, bell-shaped curve that describes the distribution of many types of data; most scored fall near the mean (68 percent fall within one standard deviation of it) and fewer and fewer near the extremes.