thinking critically with psychological science (research methods) chapter 1
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THINKING CRITICALLY WITH PSYCHOLOGICAL SCIENCE
(RESEARCH METHODS)
Chapter 1
Hindsight bias
“Knew it all along phenomenon” Paul Slovic & Baruch Fischoff The tendency to believe, after learning
the outcome, that one would have foreseen it.
Overconfidence
Thinking that we know more than we do More confident than correct
WREAT=WATER ETRYN=ENTRY
Point to Remember…
Hindsight bias and overconfidence often lead us to overestimate our intuition. Through scientific inquirery we can sift through what is reality and what is illusion.
Scientific Attitude
Requires one to ask two questions: 1.)What do you mean? 2.) How do you know?
Requires humility May have to reject your own ideas
Being skeptical but not cynical, open but not gullible
Use critical thinking Examines assumptions, discerns hidden values,
evaluates evidence, and assess conclusions
Case Study
One individual (or small group) studied over an extended period of time in depth
Sometimes over generalizes Must answer questions with other
methods
Survey
Looking at many cases in less depth Wording effects can give you different results
“aide to the needy” vs. “welfare” “free and reduced lunch” vs. “economically
disadvantaged” Questionnaire or interview Random sampling
A sample that fairly represents a population because each member has an equal chance to be included.
Naturalistic Observation
Observing and recording behavior in naturally occurring situations without trying to manipulate and control the situation.
Behaviors may be overlooked or if the one being observed notices that they are being watched, behaviors may change
Point to Remember..
A case study, survey, or naturalistic observation does not explain behaviors, it just describes it!
Correlation
One trait or behavior accompanies another
One predicts the other Scatterplots
Positive Negative (one score goes up and the other
goes down) No Relationship
Illusory Correlations
A perceived nonexistent correlation between two things
Help explain superstitious beliefs Being outside in the cold and wet causes
one to get sick (not true)
Causation
One variable may or may not lead to an outcome Low self-esteem could cause Depression Depression could cause low self-esteem
Point to Remember…
Correlation indicates the possibility of a cause and effect relationship but it does not prove causation.
Double-Blind Procedure
Both the researchers and participants do not know if they have received the actual treatment or the placebo
Reduce bias behaviors
Placebo Effect
Placebo: Latin meaning “I shall please” Just believing you are receiving
treatment can cause your mind to boost your spirits, relax your body, or relieve your symptoms
Pill with no medical ingredients
Experimental Condition vs. Control Condition
Experimental:
Exposed to the treatment
Control:Without the treatment Used as the
comparison
Independent vs. Dependent Variables
Independent(cause):
The factor that is manipulated
Variable whose effect is being studied Breast
milk(experimental)/Formula (control)
Dependent(effect):
Outcome factor The one that is
being changed due to the manipulations of the independent variable Intelligence score
Describing Data
Mean Average Preferred measure of tendency but very sensitive to extremes
Median Middle Less senstiive but doesn’t take into account all the information
in the data points Mode
Most frequently occurring Least common, but quick if data is not in order
Range Difference between high and low
Standard Deviation Determines if scores are packed together or dispersed
Statistical Significance
When sample averages are reliable and the difference between them is relatively large
The difference we observe is probably not due to chance variation between the samples
Experiment Design
Hypothesis: Prediction of how two or more factors are related “If (IV)…then (DV)…” statement
Cofounding variables: Differences between the experimental and control group other than those resulting from the independent variable
Limit confidence in research conclusions Operational definition: A description of the
specific procedure used to determine the presence of a variable.
Eliminating Cofounding Variables
Experimental bias/experimenter expectancy effect: When a researcher’s expectations/preferences about the outcome of a study influence the results gathered Simple smile, nodding, treating the
experimental group differently Demand characteristics: Clues participants
discover about the purpose of the study Single-blind procedure used
Within-subjects Design
Each participant is used as his/her own control Before treatment and after treatment is
compared Counterbalancing is used to reduce an
effect if two treatments are being tested ½ of the group is assigned one treatment
first and vise a versa
Quasi-Experimental Research
Participants are not randomly assigned males vs. females young vs. old Caucasians vs. Latinos
Do not establish cause and effect relationships due to cofounding variables
Test Method
Consistency Repeatability Same
scores/results each time
The extent to which an instrument measures/predicts what it is supposed to Example: solving
algebra problems would not measure your understanding of Psychology
Reliability Validity
Statistics: A field that involves the analysis of numerical data about representative samples of populations
Nominal Scale: Numbers used to simply name something and can be used to count the number of cases Girls=1, boys=2…no meaning
Ordinal Scale: Used for ranking and numbers cannot be averaged Highest score= 1, second highest=2. etc.
Interval Scale: Meaningful differences between each of the numbers Difference between 32 and 42 is 10
Ratio Scale: Meaningful ratio can be made with two numbers * Ratio scale have a absolute zero point (weight, volume,
and distance, zero has meaning)
Descriptive Statistics: Numbers that summarize a set of research data obtained from a sample Describe sets of interval or ratio data Frequency Distribution: Orderly
arrangement of scores indicating the frequency of each score/group of scores. Histogram (bar graph) Frequency Polygon: Line graph that
replaces the bars with single points and then the points are connected with lines (Bell curve)
Measures of Central Tendency
Describe the average or most typical scores for a set of data (mean, median, mode) Bimodal: two scores appear most frequently Multimodal: 3+ scores appear more than once
Normal Distribution: Mirror images, symmetrical, bell curve
Skewed: Data is squeezed into one end Negatively skewed: to the left Positively skewed: to the right
Measures of Variability
Describes the spread/dispersion of scores (range, variance, standard deviation)
Variance computation: Difference between each value and the mean, squaring
the difference between each value and the mean (eliminates negatives), summing the squared differences and then taking the average of the sum of squared differences
Standard Deviation Computation: The square root of the variance Must fall between 0 and half the value of the range *Wont be required to find actual calculations of
variance or SD
Inferential Statistics
Used to interpret data and draw conclusions Allows researches to either generalize the chosen sample
to the entire population or not as long as the sample represents the population.
Statistical Significance (p) is used Results are statistically significant when:
Large difference between means of the two frequency distributions
SD are small Samples are large
Statistically Significant if: 1 in 20 probability p < .05 less than 1 in 100 probability p < .01 The lower the p value the less likely the results were due to
chance
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