Research Methods: Thinking Critically with Psychological Science

Hindsight Bias I-knew-it-all-along phenomenon Tendency to falsely report, after the event, that we correctly predicted the outcome of the event.

Overconfidence We tend to think we know more than we actually do

Perceiving Order in Random Events Given random data, we look for order, for meaningful patterns. Coincidence or was the face purposefully sculpted by forces other than nature?

Why the Need for Psychological Science? Science based answers are more valid than those conclusions made from intuition and common sense Enables us to sift reality from illusion A B Which pattern was randomly made?

Scientific Attitude Mental outlook distinguished by an impartial and unbiased method and the application of empirical approaches in the quest for understanding. Includes: Curiosity: passion to explore & understand Skepticism: questioning, taking nothing ofr granted Humility: awareness of your vulnerability to make errors, open-minded to new ideas & perspectives

Critical Thinking A scientific attitude enables to think “smarter”.

Scientific Method

Hypothesis vs. Theory Hypothesis: a testable prediction of how two or more factors are related. Often implied by a theory. Theory: Organized set of principles or concepts that explain specific phenomenon

The Importance of Replication Replication: in order to validate the results of an experiment as reliable, research studies must be repeated, to validate the results and to see if the findings apply to other individuals in varying circumstances.

Operational Definitions

Example: Operational Definitions Proposed Study: Impact of alcohol consumption and driving performance. Question: What are the variables of this study? Alcohol & driving performance Operational Definitions: Alcohol consumption: how many drinks it could take for someone of a certain weight to consume before they become impaired (as measured by say, a breathalyzer) Driving Performance: results of a driving test (ex driving simulation)

Case Study In-depth examination of a person or group of people that usually involves interviews, observations, and test scores. Often conducted by psychologists Provide detailed descriptive data and analyses of new, complex, and/or rare phenomena. Weakness: do not allow generalizations about human behavior

Naturalistic Observation Observing & recording behavior in naturally occurring settings without any interference from the observer(s). Data can then be used for correlational analysis or for generating ideas for new research. Disadvantage: loss of experimental control jane goodall

The Survey Questionnaires or interviews are used to ask a large population of individuals about their behaviors, attitudes, and thoughts. Easy to administer, score, and statistically analyze. Results may be distorted due to sampling errors, poorly phrased questions, and response biases (participants answer what is politically correct instead of how they actually feel) Retrospective or ex post facto studies: look at an effect and search for a possible explanation through surveys of affected groups.

Terms to Know Population: all the cases in a group that is being studied. Random Sample: a sample that is fairly representative of the population because each member of that group had an equal chance of participating in the study The larger the sample size, the greater chance that that the sample is representative of the given population being studied.

Correlational Research Goal: to determine to what extent one variable predicts the other(s). Such research CANNOT imply cause and effect, only that the two variables are related in some way.

Another Example What factors affect violent play among children?

Correlational Research (cont’d) Correlation Coefficient (r): measures the degree of association between two sets of data. --range between -1 and + 1 where -1 = indirect relationship; +1= direct relationship; 0 = no relationship Scatterplots (scattergrams): graphically illustrate the strength and direction of correlations --the slope of the line that best fits the pattern of points suggests the degree and direction of the relationship between two variables.

Scatterplots

Experimental Method The only research method that can establish cause and effect. Carried out in laboratories under controlled conditions with careful measurements of data. Controlled Experiment: the researcher systematically manipulates a variable under controlled conditions and observes the response. Independent Variable (IV): the factor that changes in the experiment. Dependent Variable (DV): the mental process or behavior that is being measured. Essentially, the IV is the cause and the DV is the effect.

Experimental Method (cont’d) If you write your hypothesis in if….., then…. form, --“if part” represents the independent variable --“then part” presents the dependent variable Sample Population: research participants, represents a subgroup of the general population --it is important to have a large sample size to minimize the effects of individual genetic variations on the results.

Experimental Method (cont’d) Experimental group: receives the treatment Control group: does not receive the treatment Random Selection: choosing members of a population so that every individual has a equal chance of being chosen to participate in a study. Random assignment between experimental and control groups minimizes the effects of individual variations between the two groups. Between-subjects design: experimental and control group participants are different individuals.

Confounding Variables Third or “lurking variable that can affect the dependent variable. Limits the confidence of research conclusions.

Confounding Variables People carrying matches are more likely to develop lung cancer. What is the confounding variable?

Eliminating Confounding Variables Experimenter Bias (experimental expectancy effect): researcher’s expectations or preferences influence the obtained results. Demand Characteristics: clues participants discover about the purpose of the study including the rumors they hear suggesting on how they should respond Single-Blind Procedure: participants do not know if they are part of experimental or control group.

Eliminating Confounding Variables Double-Blind Procedure: neither the experimenters directly involved with the participants, nor the participants know which people are part of the experimental and control groups. Placebo: imitation pill, injection, patch, or other treatment Placebo effect: experimental participants change their behavior in the absence of any kind of experimental manipulations placebo effect

Eliminating Confounding Variables Within-Subjects Design: a research design that uses each participant as his/her control. --researchers look at the behavior of each participant pre- and post- treatment. Counterbalancing: If two treatments are used, the order of the treatments may have an effect on the outcome, so ½ of the participants are assigned to one of the treatments first and the second half to the other treatment first.

Quasi-Experimental Research Similar to controlled experiments, however, participants are NOT randomly assigned. Due to confounding variables, results CANNOT establish cause and effect, but they can point researchers in the “right” direction.

Descriptive Statistics Numbers that summarize a set of data (generally in terms of interval or ratio scales) Frequency distribution: orderly arrangement of scores that reflect the frequency of a score or groups of scores usually illustrated in a histogram (bar graph of frequency distributions) or a frequency polygon (line graph) -- with a very large data set, a frequency polygon will approach a smooth curve.

Elementary Statistics Qualitative data are frequently changed to numerical data to make it easier for statistical analysis. Nominal Scale: numbers used to name something --gender: male = 1 female = 2 Ordinal Scale: numbers than can be ranked (1st highest, 2nd highest etc…) Interval Scale: when there is a meaningful difference between each of the numbers Ratio Scale: a meaningful ratio can be made with two numbers (has a real or absolute zero)

Measures of Central Tendency Describes the average or most typical scores for a set of research data or distribution Mode: most frequently occurring score --distribution is bimodal if 2 scores appear most frequently. -- a multimodal distribution occurs when there 3 or more scores appear most frequently Median: the middle score when a set of scores are ordered by size --for odd numbers, the median is the middle score; for even numbers, it lies ½ between the middle two scores.

Measures of Central Tendency Mean: the arithmetic average of a given set of scores. --most preferred measure, because it takes into account information from all data scores. -- most sensitive to extremes and is pulled in the direction of extremes data points. The mean, mode, and median are all the same in symmetrical distributions. Calculate the mode, mean, and median for the following set of quiz scores: 5,6,7,7,7,8,8,9,9,10 Mode: 7 Median: 7.5 Mean: 7.6

Normal and Skewed Distributions Normal distribution: bell-shaped curve that represents how groups of data (characteristics) are dispersed are present in a given population. Skewed distribution: when most scores are squeezed to one end of the distribution -- a few of the scores stretch out away from the group like a tail -- named for direction of the tail (negative or positive) --in very skewed distributions, the median is a better measure of central tendency than the mean

Frequency Distributions

Measures of Variability Range: largest score minus the smallest score. --represents a rough measure of dispersion Variance and Standard Deviation (SD) measure the degree to which scores vary from each other and vary about the mean for the given set of data. --taller and narrower frequency polygons have less variability and a lower SD than ones that are shorter and wider.

Normal Distribution normal distribution. If you add percentages, approximately:  --68% of the distribution lies within one standard deviation (σ)of the mean.  --95% of the distribution lies within 2σ of the mean.   --99.7% of the distribution lies within 3σ of the mean

Inferential Statistics Used to interpret data and draw conclusions Provide researchers with information that findings can/cannot be generalized to the general population of the sample. Statistical significance(p): measure of the likelihood that the difference between two groups results from a real difference rather than just from chance. --results are usually significant when there is a large difference between the means of the two variables and their SDs are small.

Inferential Statistics (cont’d) The lower the p value, the less likely results are due to chance (cut off for statistical significance p< .05). Example: p< .05 – 95% probability that relationship is real and not due to chance. Statistical significance does not imply that the results are important! Meta-analysis: provides a means of statistically combining the results of individual research studies to reach an overall conclusion.

Statistical Scores Standard or z score: standardized score that indicates how many standard deviations a data point is from the mean. --calculated by subtracting the population mean from the individual score and dividing by the SD. -- scores above zero are considered above average and those below 0 are considered below average Percentile Score: indicates the percentage of scores at or below a particular score

Ethical Guidelines Studies highly publicized in the 1960s and 1970s prompted the APA to strengthen their ethical guidelines with respect to experimental design, execution, and practice. -- ie: Harry Harlow: rhesus monkeys separated from mothers Philip Zimbardo: students role-playing prisoners and guards Stanley Milgram: participants believed they were delivering painful shocks to another person

Ethical Guidelines (cont’d) Internal Review Board (IRB): all public and most private institutions have a review board that must approve all research conducted within their “walls” Signed written informed consents must be obtained from participants that outlines procedures, risks, benefits, and the right not to participate or to withdraw from the study without fear of reprisal. Experimentation with animals must also meet the approval of review boards and must be conducted humanely, with minimal stress, pain, and discomfort. Animals must be obtained legally and meet stringent standards for care and testing.