false

Study Guides
(248,447)

Canada
(121,541)

University of Guelph
(7,161)

Psychology
(952)

PSYC 2360
(37)

Mark Fenske
(1)

Midterm

Unlock Document

Psychology

PSYC 2360

Mark Fenske

Winter

Description

Naturalistic Observation
-designed to describe and measure the behavior of people and animals as it occurs in
their everyday lives
-focus on multiple behaviours in a particular setting and qualitative description and
interpretation
Advantages: useful for providing insight into real-world behavior, can examine behaviors
that cannot be manipulated by researcher
Disadvantages: time consuming, potential for subjective interpretation
Janie Goodall’s Research
-observed and recorded behavior of chimps in Africa, one of first to record tool use in
non-humans
Types of Observational Research Designs
Unacknowledged participant – Roys observations in the raincoat factory: chance to get
intimate info from workers, but researcher may change the situation; poses ethical
questions
Acknowledged participant – Whyte’s study of “street corner society”: ethnically
appropriate, but might have been biased by friendships, potential for reactivity
Unacknowledged observer – recording the behaviors of people in a small town: limits
reactivity problems, but poses ethical questions
Acknowledged observer – Pomerantz’s study of children’s social comparison,
researchers able to spend entire session coding behaviors, but potential for reactivity
since children knew they were being watched
Case Studies
-individual cases, sometimes studied in naturalistic settings, often brought into clinical
setting for in-depth assessment
-descriptive records of one or more individual’s experiences and behaviors, often in-
depth analysis of a single case
-case is interesting because it is unusual, qualitative interpretation of the case
Phineas Gage
-brain damage left him with change in personality and deficits in reasoning, key
indication that specific parts of the brain are associated with specific functions
Systematic Observation
-observational research and case studies can provide a detailed look at ongoing
behavior, but the qualitative nature of the data often not very objective
-these concerns can be over using systematic observation -focus on specific behaviors in a narrow context, quantitative
-coding system to measure behaviors, categories defined before project begins, based
on theoretical predictions
Stop Sign Study
-McKelvie & Schamer, observed whether males and females made complete stop while
driving at night
-men equally stopped 40% of time, women stopped 10% of time and 60% of time
Sampling Strategies
-event sampling: focuses on specific behaviors that are theoretically related to social
comparison
-individual sampling: randomly selects one person to be the focus of all the observers
for an observational period
-time sampling: involves each observer focusing on a single participant for a time period
before moving on to another participant
Archival Research
-based on an analysis of any type of existing data sources: statistical records (daily
temps, sports records, and crime data), survey archives, and written and mass
communications like the newspaper
Content Analysis
-essentially the same as systematic coding of observational data
-includes the specification of coding categories, uses more than one rater
Hypothesis Testing – new week
Samples and Population
-research findings are based on samples drawn from populations
-inferential stats allow us to infer what the population is like, based on sample data
Two Group Means
-research q: are there sex-related differences in alcohol consumption?
-ask samples of males and females about number of drinks consumed during last week
-results: avg number of drinks consumed males – 2.5 females – 1.3
-is the mean different for girls and boys? If we assume sample means are different does
the difference generalize to the entire population and do the population means also
differ?
Measure of Central Location
mean: add all numbers / by amount of categories
deviations from the mean: the numbers d1= x1-x, d2=x2-x, etc. are called deviations from the mean
sum of squares: the sum is called the sum of squares of deviations from the mean –
(equation on page 6 of slides)
median: line up numbers from least to greatest – pick middle
Measure of Variability (Dispersion, Spread)
-range (R = max-min)
-variance, standard deviation
-sample variance – (equation on page 7 of slides)
-the sample SD (equation on page 7) is the square root of the sample variance
Interpretations of s
-in normal distributions: sd – approx. distance from mean to inflection point on
histogram
-approximately 68% observations will lie within one SD of the mean, 95% of
observations lie within two SDs of the mean
Hypothesis Testing (Difference of two means)
Sampling Distribution
-distribution of all possible values of stats, i.e. mean
-sampling distribution of the mean collects samples from the population, calculates the
mean for each samples, plot the means – distribution of sample means
*as a sample size gets larger shape of the distribution approaches normal distribution
-central limit theorem: even if the population is not distributed normally, the sampling
distribution will be normal
Null Hypothesis
-assumes observed data do not differ from what would be expected on the basis of
change, h0
Testing: Statistical Significance
-to reject null, observed data must deviate more than what would normally be expected
under the sampling distribution
Probability Value
-we assume that all observations come from the same parent population
-p is the probability that: an observed difference could have occurred simply by change
Judging the plausibility of the null
-the sample mean should be plausible, under the sampling distribution of the mean
-plausibility of the null is judged by computing the probability p of observing a sample
mean that is at least as deviant from the population mean as the value we have
observed Alpha (a)
-arbitrary threshold at what level of the investigator is willing to discount the role of
change as an explanation for an observed group difference
Statistical Decisions
-when p < alpha, then asset chance could not be the explanation of the observed group
difference
-this leaves systematic error and the independent variables as the remaining
explanations – statistically significant!
-when p > alpha, then assert chance could not be ruled out as the explanation of the
observed group difference – this is NOT statistically significant!
P-Values
-two-sided p values: used to test research hypothesis and take into consideration that
unusual outcomes may occur more than one way
-one-sided p values: can be used in some special cases
One-tailed vs. Two-tailed
-use a one-tailed test when you have specific reason to believe that the effect will be in
a particular direction, and you do not acre if the effect is I the opposite direction, they
will always result in smaller p values and hence a greater chance of reaching the
significance for your directional hypothesis
-decision whether to use one-tailed or two-tailed must be paid prior to data collection
Type 1 Error
-occurs when we reject the null when it’s true, likelihood is set to alpha .05, 5% is a
reasonably low probability of being wrong, but could set lower
-saying there is a differenc

More
Less
Related notes for PSYC 2360

Join OneClass

Access over 10 million pages of study

documents for 1.3 million courses.

Sign up

Join to view

Continue

Continue
OR

By registering, I agree to the
Terms
and
Privacy Policies

Already have an account?
Log in

Just a few more details

So we can recommend you notes for your school.

Reset Password

Please enter below the email address you registered with and we will send you a link to reset your password.