Chapter 2 The Research Enterprise in Psychology Flashcards
(91 cards)
1
Q
Research methods in psychology
A
science is similar to detective work
- promote the idea of curiosity
- skepticism
- open minded
2
Q
The Scientific Process/Method:
A
- identify a question
- what do we want to learn about?
- ex/ why am I scared of clowns?
- Form a hypothesis
- what is our specific prediction?
- ex/ because of something that happened in my past. I.e. trauma
- Gather information
- ex/ sending emails, anything to gather information
- Analyze the data
- what can we conclude?
- conclusion is the answer to the question
- what can we conclude?
3
Q
theory
A
a plausible or scientifically acceptable general principle or body of principles offer to explain phenomena
- ex/ why are people scared of this? (general principle)
- ex/ why do I see the sky as blue? (could be theory because it applies to multiple people/things)
4
Q
What is a good theory?
A
- organize information in a meaningful way
- is testable (i.e., is ‘disprovable’)
- In science you cannot prove anything correct. You do not prove in science, but you can disprove. Instead, you support ideas, and provide evidence towards ideas.
- predications are supported by research
- conforms to Law of Parsimony
- Occam’s Razor: if there is multiple explanations possible, then pick the simplest one
5
Q
Trouble with Humans:
A
studying unobservable mental processes
6
Q
studying humans
A
- Complexity - 500 million neurons in the brain
- Variability - every person is different
- Reactivity - reactions differ when observed vs not observed
7
Q
Hindsight understading
A
after viewing a behaviour, propose an explanation that makes sense in that context
- what we think is going on
8
Q
hypothesis-testing
A
test possible explanations through scientific method
- verifying if we’re right or not
9
Q
operation definition:
A
- a description of a property in concrete, measurable terms
- basic procedure of how one can measure something
- ex/ how many licks does it take to reach the tootsie of a tootsie pop
- make sure it is replicable
- essentially, making sure you are measuring what you want to measure
- i.e., define what you are studying
10
Q
descriptive research
A
describe behaviour in nature
2. e.g., case studies, surveys, naturalistic observation (*multiple choice)
11
Q
case studie
A
- a method of gathering scientific knowledge by studying a single individual (n=1)
2. Not generalizable
3. Pro: important for testing particular theories and/or exceptional cases: e.g., H.M., Phineas Gauge
12
Q
Sample survey research
A
a subject of individuals form the population
13
Q
Representative sample
A
posses the important characteristics of the population in the same proportions. Data from representative sample are more likely to generalize to the larger population than data from a unrepresented sample.
14
Q
Naturalistic Observation
A
observing people/animals in their natural environment, when they do NOT know they are being observed
1. ex/ it is NOT naturalistic observation to view animals in a zoo
15
Q
One of the best ways to avoid demand characteristic
A
demand characteristics: someone behaves a certain way because they think that’s what you want
16
Q
limits
A
experiment cannot inform a person that they are being observed.
1. cannot gain consent, ethical problem
17
Q
things you CAN do
A
observing how many people at the mall has white shoes. Observe in a environment where people know they are being watched (cctvs) but does not know what is being recorded
18
Q
things you CAN’T do
A
observing thoughts or emotions
19
Q
Descriptive Research
A
describe bahaviour in nature
1. E.g., case studies, surveys, naturalistic observation
20
Q
Correlational Research
A
ook at the relationships between two variables
1. are two variables related?
2. cannot tell if variable A causes variable B
21
Q
correlation does not equal
A
casuation
22
Q
Correlation
A
compare the pattern of variation in a series of measurements between variables
* E.g., Correlation between insults and favors
23
Q
Types of Correlation:
A
postivie, perfect. negatvie
24
Q
Perfect correlations
A
(r = +1 or -1). rare
25
Positive correlations
(0 < r -<- +1)
an increase in one variable relates to an increase in the other
* OR decrease = decrease!
* E.g., the more times you laugh at my jokes, the happier I feel OR age and height
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Negative correlations
(-1 -<- r < 0)
an increase in one variable relates to a decrease in the other
* E.g., the faster you drive, the less time it takes to get somewhere
* No correlations between variables (r = 0)
27
Strength of Correlations
Denoted by the letter 'r'
* indicates how related two variables are
* -1 < r < +1
<>(less than or)
ex/ which one is stronger? +.5 or =-.8?
-0.8 is closer to -1, than +0.5 to 1
Thus, -0.8 has a stronger correlation
28
the number closest to 1 is
the stronger correlation. you cannot go beyond 1
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less variations =
strong correlation
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average of all dots across the graph is
how you determine
1. use r values to calculate as graphs are difficult to read by itself
4. slope does not matter!
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Benefits of Correlations
gvie idea which viariable to sue in research experiments. starting point.
32
if you cannot manipulate it
you cannot determine causality
33
the third variable problem
two variables may be related to be one another (are correlated) only because they are both causally related to a third variable
* ex/ x (exposure to media violence) <--- z (lack of adult supervision) ---> y (aggressiveness)
34
accounting for the third variable problem:
matched samples
* participants in two groups match
* has the same z score (i.e., exact same amount of adult supervision)
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matched pairs
each participant matches another
* each participants matches another participant from another group, but the amount of score is the same
* i.e. Group 1: child A 20%, child B 91%, child C 40%
* Group 2: child A 20%, child B 91%, child C 40%
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manipulation does not equal to
correlation. but is = causality
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Experimental Research
the only way to truly infer causality is to develop an experiment
1. in other words only way we can determine causality
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experiment
a technique for establishing the casual relationship between variables
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Relationships between variables:
experiments allow researchers to determine the casual
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variable
a property whose value can change across individuals and over time
- age, income, height, GPA... something we can measure
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independent variable (IV)
the variable that is *manipulate* in an experiment
- ex/ manipulate amount of study and measure GPA
42
Dependent Variable (DV)
The variable that is *measured* in a study
43
Manipulating variables:
the point of an experiment is to manipulate variables in a controlled enviroment
- controlled group: do not manipulate
- experimental group: manipulate
- measure both groups and look for a difference
44
Steps of manipulation:
determine what you want to study
1. identify your independent variable(s) (IV), dependent variable(s) (DV), and measure
2. between-subjects or within-subjects design
45
Between-subjects:
two groups of different subjects
- control group, treatment (experimental) group
46
Within-subjects:
one group where the participants serve as both the control and experimental groups
- ex/ everyone get weed brownie for midterm 1 and normal brownies for midterm 2. Everyone in the group is both the control and experimental group.
47
placebo
harmless substance that looks like the treatment drug; used to counter expectation effects.
- e.g., sugar pill; saline injection
48
reliability
produce the same measurement when measuring the same thing
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validity
must be conceptually related to the property of study
50
power
ability of a measure to detect the conditions specified in operational definition
51
Demand characteristics:
aspects of an observational setting that make people behave as they think they should
- a problem for psychological research
- both clinical and scientific settings
- ex/ downplaying feelings in fear of being judged
52
Avoidivng demand characteristics:
reducing demand characteristics by ensuring participants anonymity and confidentiality
- ensuring anonymity by giving them numbers instead of using names, and confidentiality by not connecting the responses by their numbers
- use measures that are not susceptible to demand characteristics (fMRI, galvanic skin response tests (polygraph))
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use deception
e.g., tell participants research is on how much they smile but its actually on eye blinks. Give debriefing in the end (tell the truth in the end)
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include filler items
harder to make connections on a video they watched and a sudoku they solved to their emotions
55
Experimenter effects/observer bias
scientists can sometimes unconsciously influence participants responses/behavior
1. expectation can influence measurements
1. "see what you want to see"
2. expectations can influence reality
1. unconsciously cue people to respond how you want them to respond
56
double-bind studies
. neither the researcher nor the participant knows which is treatment, and which is a control
2. almost always do a double blind study
57
2. using automated measurement devices
1. remove the human element to eliminate human bias
2. computers
58
Random Sampling:
"technique for choosing participant to ensure every member of population has an equal chance of being included in the sample"
- does not always produce a representative sample
- ex/ drew 10 participants from the entire class and they're all male
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generaliability
multiple experiment can be conducted on different samples. If the results are similar, it increases the generaliability of the finding.
60
Random Assignment:
- a procedure that uses a **random event** to assign people to the experimental or control group
61
Descriptive statistics:
methods for describing and summarizing data
62
central tendency:
. the value of measurements near the center or midpoint of a distribution
2. only thing well have to calculate in this class
63
Mode
value of the most frequently observed measurement
64
mean
average of all measurements
65
median
value in the middle of the distribution
66
Describing Data
graphical representation of data is quick and efficient
- frequency distributions
- histograms
- groups data by score (x-axis)
- height of bar is "proportion" or "frequency" (y-axis)
- box plots (not tested)
67
variability
how much measurements differ from one another
68
Range
value of the largest measurement in a frequency distributions minus the smallest
69
Normal distribution
gaussian distributions or "bell-curve"
- symmetrical
- central peak
- trails for to both ends
- 50% scores above and 50% below
70
skewed distributions:
normal
- positively skewed
- draw the mean above the mode. Raise the tail of the graph to the positive because of certain outliers (ex/ two people scored high on test while everyone else got 70)
71
negatively skewed
draw the mean below the mode. (ex/ certain people go 10 when everyone else got 80s)
72
mode
peak distribution is always the mode
average and median will shift towards the skew
73
significant results
significant results
74
inferential statics
inferential statics
75
- can also calculate significant results using correlationx
strong enough correlation because of something and not by chance
- significant correlation -> do an experiment (cannot be something impossible to manipulate)
76
Significance
p < 0.05 means the probability that random assignment failed and the at the results can be attributed to some other variable (chance) is less than 5%
77
p =
probability
78
Null Hypothesis
states that an observed difference between the samples are due to chance
- assume this is true until awe can reject it with statistically significant results
- not the opposite to hypothesis but that there are no difference
- goal of the experiment is to prove the null hypothesis false!
- only reject null hypothesis if the p value is lower than 0.05
79
internal validity
results of tour experiment worked out, and p < 0.05
1. placebo effect
2. or characteristic demands or experimental bias
80
external validity
experimental property where variables have been operationally defined in a normal, typical, or realistic wat
81
82
replicability
someone else does the same study and get the same results
83
meta-analyses
combine and look at results of multiple papers and see if they all agree on causal agents, etc. (How generalized is it?)
84
Ethics
strict ethical laws for preforming their experiments on both people and animals
- scientists must show respect for people, animals, and the truth
- must not fudge numbers
85
1979 Belmont report
respect people's right to make decisions for and about themselves; no influence or coercion
- minimize risks and maximize benefits
- must distribute the benefits and risks equally to participants without prejudice towards particular individuals/groups
86
Informed consent
agree to risks and benefits
87
Freedom from coercion
cannnot be forced to partipate
88
Protection from harm
protec ttheir participants from pshycial or psychological harm
89
Debriefing
if a participant is deceived in any way during or before an experiment, at the end they must be told the purpose of the study and informed of the deception
90
Confidentiality
private and personal information key confidential
91
Ethics with animals
must be trained in research methods and experienced in the care of laboratory animals
- laboratory animals and experienced
- must minimize the discomfort, infection, illness, and pain of animals
- animals cannot be subjected to discomfort and pain unless alternative procedures are unavailable
- the study must show significant benefit to society for these works to be approved
- must perform all surgical procedures under appropriate anesthesia and must minimize animals pain after surgery and during recovery
