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