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CHAPTER 6 Psychological Research and Statistics

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Objectives Describe the process of psychological research Name the different types of psychological research and some of the methodological hazards of doing research Describe descriptive and inferential statistics Name specific research methods used to organize data

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Gathering Data How do psychologists collect information about the topic they’ve chosen to study?

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Gathering Data Validity – extent to which an instrument measures or predicts what it is supposed to Algebra questions would not be a valid measure of what you learned in Psych class

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Gathering Data Sample – relatively small group out of the total population Population – an entire group as a whole Sample must be representative of the population If a sample is not representative, then it is biased How can researchers avoid bias?

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Gathering Data What does correlation mean? The degree of relatedness between two sets of data Two types - positive correlation & negative correlation

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Gathering Data IQ scores and academic success – positive correlation (direct relationship) The higher your IQ, the higher your grades Car speed and time it takes to travel somewhere – negative correlation (inverse relationship) - as car speed increases, time it takes to reach your destination decreases

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Correlations Your turn! Hours in the sun and chance of sunburn Positive correlation Amount of exercise and % body fat Negative correlation Mrs. Bird’s high school GPA and your high school GPA no correlation

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Correlations A researcher uses statistics to compute their research findings Statistics = field of mathematics that involves the analysis of numerical data about representative sample of population Correlation coefficient = needs to be near 1 (-/+), the closer results are to -1 or +1 the better the relatability between the two variables

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Experiments Why do researchers choose experimentation over other research methods? Researchers can control the situation. Can establish cause and effect, only research method in which you can The goal of research is to prove or disprove a... Hypothesis

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Experiments Variables – conditions and behaviors that are subject to variation/change Two types of variables – independent and dependent IV – manipulated variable in order to view its effects DV – dependent upon the IV – affected by it, the one the researcher measures

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Experiments Experimental group – consists of subjects who undergo the experimental treatment – variables are applied to this group Control group – consists of subjects who do not receive experimental treatment Why is this group necessary?

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Experiments in Psych Avoid Researcher Bias: researcher’s desire to prove hypothesis affects results Avoid Self-fulfilling prophecy: researcher’s desire to prove hypothesis affects results Could be very subtle or unconscious, but researcher will treat one group slightly differenty (body language, tone of voice)

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Avoiding Researcher Bias Use double-blind = neither researcher nor subjects know what group they are in, helps reduce researcher influencing results Confounding variables = factors that cause changes in the dependent variable that aren’t the independent variables

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Quasi-Experiment (“sort of” experiment) For example, imagine that we wanted to do a study to compare student performance. Imagine further that we scheduled two sections of the course, let students sign up for which one they wanted, and then taught one using cooperative learning and the other using standard lecture. Note that this study includes a manipulated independent variable, but it lacks random assignment of participants to conditions. The problem with this approach, of course, is that there might be differences between the two groups of students other than the style of teaching to which they were exposed. Perhaps the students who signed up for the earlier section are more “gung ho.” Or perhaps the students who signed up for the evening section are more likely to be working adults. Or perhaps the students in the 1:00 p.m. section tend to be drowsy after lunch. It is possible that differences in the dependent variable could have been caused by these differences rather than differences in teaching style.

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So what’s the problem with quasi-experiments?

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No random assignment! = a sort of experiment! Other examples: boys vs girls, old vs. young

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Naturalistic Observations Naturalistic observation – viewing the subjects of an experiment in their natural habitat IMPORTANT: Subjects CANNOT know they are being watched! Why is this important??

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CASE STUDY Case study – a scientific biography of a group or person, very in depth look at a phenomena Most use long-term research to gather tons of data in order to generate new hypotheses Utilize lots of different tests to collect data ex) facial agnosia, split brain patients

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Surveys Surveys – an interview/questionnaire that gathers data on the attitudes, beliefs, and experiences of large numbers of people

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Longitudinal Studies Longitudinal studies – covers a long period of time, same subjects followed for long time and questioned at different intervals in time (ex. Age 20, 25, 30 35) Psychologists study subjects over regular intervals for a period of years Allows for examination of consistencies and inconsistencies as development occurs

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Cross-sectional Cross-sectional studies – individuals are organized/studied on the basis of age Question different groups of people that represent different stages of development

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Avoiding Errors How can researchers avoid errors while doing research? self-fulfilling prophecy - Researchers finding what they want to find, while overlooking contrary evidence Example experiment – testing a new medicine Single Blind – subjects do not know if they have a are in control group (placebo) or in the experimental group ( get real IV) Double Blind – subjects AND experimenter have no knowledge of who in is experimental or control group = best option if possible to design

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Smile Break

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Statistics A branch of mathematics that enables researchers to organize and evaluate the data they collect

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Statistics Descriptive statistics – listing and summarizing data in a practical and efficient way Examples – graphs, averages

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Statistics Frequency distribution – table that arranges data in a way that allows us to see how often a particular score occurs Histogram – similar to bar graphs – always vertical & the bars always touch Frequency polygon – no bars just lines to visually display data

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Frequency Distribution

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Histogram

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Frequency Polygon

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Central Tendency Central tendency – a number that describes something about the “average” score Used to summarize information into statistics Measures of CT: mean, median mode

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Central Tendency Mean – an “average” score Most commonly used measure of CT To find the mean, you add all scores and divide by the number of scores

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Central Tendency Median – the middle score The midpoint of a set of scores, so it divides the frequency distribution into two halves Mode – the most frequent score

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Central Tendency 0, 3, 4, 4, 5, 5, 6, 7, 8, 8, 8, 9, 9, 10, 10 Mean – 6.4 Median – 7 Mode - 8

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Measures of Variance Distributions show us not only the “average” score, but also how “spread out” these scores are. Variance – provides an index of how spread out the scores of a distribution are

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Measures of Variance Range – subtract the lowest score from the highest score Standard deviation – a measure of distance, describing an “average” distance of every score to the mean The larger the standard deviation, the more spread out the scores are

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Standard Deviation

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Inferential Statistics Used to determine whether or not the data that researchers collect supports their hypotheses, or whether their results are merely due to chance outcomes, draw conclusions & interpret data probability & chance If probability that results are due to chance is less than 5% (.05), researchers can be confident in their findings (less than 1 in 20 chance)

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Ethical Guidelines Read page 59

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Define for Homework, Chapter 6 PopulationPlacebo effect SampleDemand characteristics Experimental groupCounterbalancing Control groupReliability Between subject design Ex post facto Within subject designBimodal Confounding variablesMultimodal Operational definitionSkewed distribution Random AssignmentsMeta-analysis Experimenter Bias

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