Psych – Unit 1
History and Research Methods
What is Psych?
 Psychology is a science that seeks to answer questions about
ourselves – how we think, feel, are motivated, etc.
 It is the science of BEHAVIORS – anything an organism does
(observable) and….
 MENTAL PROCESSES – thoughts, beliefs, ideas, values,
emotions (subjective)
Psych’s Roots
 Modern Psychology – December 1879 – Germany
Wilhelm Wundt – first psych experiment – measured sensation
responses
Psych = evolved from
PHILOSOPHY &
BIOLOGY
Early Schools of Thought
 STRUCTURALISTS: (Wundt) wants to explore the
structure of the mind. Looks inward (introspective) – very
subjective
VERSUS
 FUNCTIONALISTS: (William James) interested in how
mental and behavior processes actually function and how they
enable us to adapt, survive and flourish
Psych’s Big Debates
 1.) Nature V. Nurture
 Are we are the way we are due to nature (genes, chemical
imbalances, genetic predispositions) or nurture (our
environment, culture).
 Twin and adoption studies are critical….why?
 2.) Stability V. Change
Psychology’s Perspectives – in class
 1.) Behaviorists
 2.) Psychoanalysis
 3.) Humanists
 4.) Biopsychology
 5.) Cognitive
 6.) Social-Cultural
 7.) Evolutionary
Psych’s Subfields
 Basic Research: builds the knowledge base – answers content
questions for the sake of knowledge
 Applied Research: hands on research that tackles very
specific, practical problems (work place consultants)
 Professional Services – counseling
 PhD – Psychologist - doctorate in psychology – writes,
researches, teaches at university
 Clinical Psychologist treats mentally ill
 MD – Psychiatrist - medical doctor specializing in psychiatry –
works in hospital or psych ward or out-patient counseling
Psych as a SCIENCE
 Psychologists use the scientific method to conduct research
and produce measurable, empirical evidence/data
 Psych must pursue the scientific method due to the
limitations of human intuition
 Hindsight Bias – “Monday morning QB,” I knew it all along
– the tendency to believe after learning an outcome that one
would have seen it coming
 Overconfidence – we tend to think we know more than we
do (all freshmen thought they’d graduate in 4 years)
1.) Descriptive Research Methods
(only describes – doesn’t explain)
 CASE STUDY: the study of one unit (i.e. one person)
 Pro – you can get detailed, in depth information
 Pro – great method to study rare phenomenon
 Con –You cannot generalize – your case study may be atypical
and you cannot make assumptions about the larger population
 Ex: I want to write a book on the life of the American teenager
and I select one 16 year old and spend a year studying that
individual in detail – shadowing them, interviewing them, their
friends, etc.
Descriptive Research Methods
 2.) SURVEY DATA – uses a questionnaire to gather
information from many people quickly – often asks people to
report behaviors and opinions about a given topic
 Pros: quick, cheap, and efficient – I can get a lot of data quickly
 Cons : surface level – doesn’t go in depth
 Cons: people lie or may misunderstand the questionnaire – or
wording effect (censorship v. restrictions)
 Make sure your sample represents your population in order to
generalize
 Example: I want to research the life of the American teen and
have 3,000 teenagers at the local high school fill out a detailed
questionnaire on their behaviors
Descriptive Research Methods
 3.) NATURALISTIC OBSERVATION: watching and
recording the behaviors of organisms in their own, natural
environment
 Pros – get organisms in natural setting – avoid lab effect
 Cons – Observer bias – two people watching the same scene
may come to different conclusions (subjective)
 Ex: I want to research the life of an American teen, so I go
undercover and enroll in the local high school. I go to class,
parties, etc with the teenagers and observe their behaviors
CORRELATIONAL RESEARCH
 Correlation studies go beyond describing behavior to
predicting it
 Based on a known relationship we can predict phenomenon
 Ex: If I know a correlation exists between smoking and lung
damage and I know Johnny smokes two packs a day for twenty
years….I can predict that he will have significant lung damage
 CORRELATION DOES NOT = CAUSATION!!!!!
(only a controlled experiment can show cause)
How to carry out a correlation study…
 1.) Identify your variables
 2.) Gather data
 3.) Create a scatter plot
 4.) Draw a line of best fit
 5.) Calculate a correlation Coefficient
 6.) Analyze and make conclusions about the relationship
Correlation Scatter plots
Correlation Coefficients
Correlation Coefficients
 r=
 + relationship: +.4 to +1.0
goes in same direction
 Example – height and weight
 - relationship: -.4 to -1.0
inverse; goes in opposite
direction
 Example – exercise and weight
 No relationship: -.3 to +.3
No visible relationship
 Example – eye color and GPA
EXPERIMENTS
 Experiments are the most scientifically stringent research
method as you control and manipulate variables to
illustrate a true cause and effect relationship
 IV: the variable manipulated – the treatment
 DV: the variable measured
 Operational Definitions: exactly how the IV was
administered and the DV was measured
 Example – DV is hyperactivity. The Operational definition is
a survey to be filled out by the parent rating hyperactivity on
a scale of 1-10
Steps of an experiment
 1.) Identify your population – the group your are interested in studying
 2.) Take a large, random sample to guarantee it is representative of the
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population
3.) Randomly assign your sample to either the experimental condition (gets
the treatment) or the control condition (gets a placebo – nothing special)
4.) Run your experiment
5.) Measure your DV
6.) Run inferential stat tests (MANOVA, t-test, ANOVA) to see if the
difference between the experimental and control groups is statistically
significant) – did we meet our p-value = .05?
7.) Replicate - RELIABILITY
8.) If yes, conclude that the IV caused the DV
9.) Generalize your findings back to your population
Possible Confounding Variables and
Critical Controls
 Large Random Sample
 Random Assignment
 Placebo (blind)
 Guarantees sample is
representative and allows
you to generalize
 Guarantees your groups are
the same (=) prior to
treatment
 Controls for the
Hawthorne Effect
 Double Blind
 Controls for observer bias
Confounding Variables…..BAD
 Confounding variables – unexpected variables that distort
the results. They destroy your conclusions, so you want to
anticipate possible confounding variables and control them.
 Controlling confounding variables helps to guarantee that
your experiment is VALID – that it is truly measuring what
you claim to be measuring.
DESCRIPTIVE STATISTICS
 Descriptive stats describe/summarize a large set of data
 Measures of central tendency – summarized the middle of
the data
 Mean – average
 Mean – middle score, 50th percentile
 Mode – most frequently occurring score
 Measures of variation – summarizes the spread of the data
 Range – high score minus low score
 Standard Deviation – average distance of each data point from the mean
SD = square root of variance
Distributions
 Normal Distributions form
a bell curve. The mean,
median, and mode are all
the exact same point
Distributions
 Positively skewed
distributions have positive
outliers – extreme scores
to the right of the bulk of
the data that pull the tail in
the positive direction.
 The median is slightly
affected (pulled positive)
 The mean is the most
effected (pulled positive)
Distributions
 Negatively skewed
distributions have negative
outliers – extreme scores
to the left of the bulk of the
data that pull the tail in the
negative direction.
 The median is slightly
affected (pulled negative)
 The mean is the most
effected (pulled negative)
Inferential Statistics
 Inferential Stats are more sophisticated statistical tests (t-
tests, ANOVAs and MANOVAs) used at the end of an
experiment to infer significance
 P-value = .05
 If we meet the p-value we are 95% confident that the difference
between the experimental and control groups is caused by the
IV and is not due to chance (i.e. only 5% chance of error)