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PSYC 101 > Ch.2: research methods > Flashcards

Ch.2: research methods Flashcards

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1
Q

empiricism

A

The belief that accurate knowledge can be acquired through observation

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2
Q

theories

A

hypothetical explanations of natural phenomena; ideas about how something works

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3
Q

hypothesis

A

a falsifiable prediction based on a theory

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4
Q

what are the methods (2) used to study human behaviour?

A
  1. methods of observation (determine what people do)

2. methods of explanation (why people do what they do)

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5
Q

operational defintions

A

a description of a property/variable in measurable terms

ie: for example, we might operationally define happiness as “a person’s self-assessment” or “the amount of dopamine in a person’s brain” or “the number of times a person smiles in an hour.”

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6
Q

construct validity

A

the extent to which one adequately characterizes the property/variable; how well a study conceptualizes it variables

ie: measuring smiles per hour to measure emotions per day over measuring their IQ

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7
Q

power

A

the ability to detect the presence of differences or changes in the magnitude of a property

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8
Q

empirical method

A

set of rules and techniques for observation

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9
Q

the scientific method

A

a procedure for using empirical evidence to establish facts.

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10
Q

naturalistic observation

A

gathering data by observing behaviour in an ordinary setting, without researcher interference

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11
Q

what are a case study/method and some of its disadvantages

A
  • the deep study of an individual or small group in hopes of revealing universal principles
  • research usually on unusual phenomena
  • DESCRIBES behaviour (experimentation explains it) what cant always be studied in a lab

disadvantage: large time commitment, the small number group is NOT representative of the population as a whole (experimentation can be used to apply what learned to the larger population(

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12
Q

correlational research

A

requires at least two variables to be measured so that one variable can be used to predict the other
- NO variable manipulation by researcher

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13
Q

disadvantages of experimentation

A 
some variables cant be manipulated without violating ethical standards:
- informed consent
safety
- privacy of data
benefit to society
- a benefit to participants
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14
Q

demand characteristics

A

settings that cause subjects to behave as they think someone else (eg. experimenter) wants or expects

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15
Q

how to avoid demand characteristics?

A
  1. naturalistic observation
  2. if you cant do naturalistic observation:

privacy and control

  • gathering info privately or anonymously
  • measuring behaviour that people are unable or unlikely to control (such as dilation of pupils to tell whether or not you’re lying)

unawareness
- make sure ppl are unaware of the true purpose of the observation

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16
Q

how can you avoid observer/experimenter bias

A

double-blind design: neither the researcher not the participant knows how the participants are expected to behave
- avoids the placebo effect

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17
Q

normal distribution

A

where the most measurements of a frequency distribution are concentrated in the middle
mean = median =mode

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18
Q

what are the descriptive stats of central tendency?

A

mode: the value of the most frequently observed measurement.
mean: the avg value of all the measurements.
median: the value in the middle

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19
Q

descriptive stats:

A

s calculated from a distribution of scores, indicating the central tendency (avg) and the variability; brief summary statements aBOUT ESSENTIAL INFORMATION FROM A frequency DISTRIBUTION

  • indicated whether or not experimental treatment changed performance
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20
Q

third-variable (confounding variable)?

A

correlation between two variables cannot be taken as evidence of a causal relationship between them because a third variable might be causing them both

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21
Q

the third-variable (confounding variable) problem?

A

correlation between two variables cannot be taken as evidence of a causal relationship between them because a third variable might be causing them both

***why correlation DOES NOT = causation

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22
Q

what’s a solution to the third-variable problem?

A
  1. experimentation: eliminating or controlling other possible causes, thus creating a a set of conditions that differ in ONLY ONE WAY. Experimentation finds causal relationships
    - via manipulation: a technique for determining the causal power of a variable by actively changing its value
  2. randomization
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23
Q

a good detector has ____ and _____

A

power and reliability

If a person smiles a bit more often on Tuesday than on Wednesday, a powerful smile-detector will detect different amounts of smiling on those two days. If a person smiles exactly as much on Wednesday as she did on Tuesday, then a reliable smile-detector will detect identical amounts of smiling on those two days. A good detector detects differences or changes in the magnitude of a property when they do exist (power), but not when they don’t (reliability).

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24
Q

negatively skewed data leans to the….

A

right

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25
Q

positively skewed data leans to the

A

left

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26
Q

standard deviation

A

how each of the measurements in a frequency distribution differs from the mean; involves finding the distance between each individual score and the mean, and then computing the average of these distances.

  • indicate how many exceptions there are.
  • when the standard deviation gets larger = the more the two groups overlap and the smaller (weaker) the effect size and vice versa (smaller SD = less overlap = stronger effect size)
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27
Q

if the distribution has extreme scores that pull the mean in their direction, researchers prefer to use the:

A

median

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28
Q

What’s a limitation of naturalistic observation?

A

** describes behaviours but cannot explain why they occur which is why we need experimentation

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29
Q

frequency distribution

A

a graphic representation showing the numner of times in which the measurement of a property takes on each of its possible values

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30
Q

sample

A

a partial collection of people or animals or things drawn from a population (n); representative of the population

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31
Q

population

A

a complete collection of people (N)

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32
Q

correlation

A

when variations in the values of one variable are synchronized with variations in the value of the other

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33
Q

what is direction (r) in a correlation?

A

the direction of the correlation = positive or negative;

  • positive = “more is more”; ppl have a lot of one variable also have a lot of the other; r=1
    ie: more health is associsated with more wealth
  • negative = “more is less”; r= -1
    ie: more health is associated with less poverty

*the sign associated wit r

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34
Q

variability

A

the extent to which the measurements differ from each other (range, standard deviation, etc)

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35
Q

correlation coefficient (r) + scale

A

mathematical measure that shows both strength and direction of a correlation (how much of an impact the variables have on one another ie: 0.75 or -0.75 = strong correlation vs -.05 or -0.10)

scale:
weak = 0.10, medium = 0.30, strong > or = 0.50

36
Q

what is strength (r) in a correlation?

A

the absolute value of r, thus tells you the amount of exceptions to the correlation/rule

37
Q

r= 1

A

perfect positive correlation (positive direction)
every time the value of a variable increases (decreases) by a certain amount, the value of the second variable (decreases) increases by a certain amount.

38
Q

r= - 1

A

perfect negative correlation
every time the value of a variable increases by a certain amount, the value of the second variable decreases by a certain amount.

39
Q

r = 0

A

no correlation
every time the value of a variable increases by a certain amount, the value of the second variable NEITHER increases nor decreases by a certain amount.

40
Q

imperfect positive correlation

A

most common in the real world.
there are always exceptions which means r will be somewhere between 0-1 and 0- -1

  • few exceptions=r is closer to 1, more exceptions = r is closer to -1
41
Q

imperfect positive correlation

A

most common in the real world.
there are always exceptions which means r will be somewhere between 0-1 and 0- -1

  • few exceptions=r is closer to 1 (stronger correlation), more exceptions = r is closer to -1 (weaker correlation)
42
Q

natural correlations

A

correlations we observe in the world around us

ie: height and weight, sleep and memory, etc

43
Q

techniques used in experimentation?

A

manipulation: a technique for determining the causal power of a variable by actively changing its value

random assignment: avoids bias towards a specific type of person

44
Q

steps of the experimentation technique

A
  1. MANIPULATE: manipulate the independant variable
    - creates at least 2 conditions
  2. MEASURE: measure the dependant variable (“depends “on the participants)
  3. COMPARE: compare the variable of the variable in one condition and the value of the variable in the other condition
    - if values differ on avg, then the independent CAUSED changes to the DEPENDANT
45
Q

self-selection

A

problem that occurs when anything about a participant determines whether they will be included in the experiment or control group;
a type of bias that can arise when study participants choose their own treatment conditions, rather than being randomly assigned

46
Q

random assignment

A

a procedure that allows chance (ie coin flipping) to assign participants to the experimental/control group
**used in experimental/causation studies

47
Q

can random assignment fail? is so, how?

A

yes.
assortment of participants into group appears biased/not 50/50 split of each “type” of participant in each condition.

  • calculate the STATISTICAL SIGNIFICANCE of the data. if p>5%, the results are NOT statistically significant and random assignment failed, p<5%, then results are statistically significant
48
Q

statistical significane

A

calculates how likely is it that the sample’s result came from a population in which there is no relationship (if the null hypothesis were true); determined by calculating the probabilities that random assignment has failed, there inferential statistics
- assess the probability that the effect size calculated from one just one sample came from a larger population with certain characteristics

  • statistically significant results: p<0.05
  • ** a result can be statistically significant but not necessarily important
49
Q

internal validity

A

an attribute of an experiment that allows it to establish causal relationships; everything INSIDE the experiment is working exactly as it should

  • how well it can rule out alternative explanations
  • *conclusions drawn are restricted to the
50
Q

external validity

A

when the variables in an experiment have been defined in a normal, typical, or realistic way (variables are representative of the real world); how well the results can generalize to a population of interest.

51
Q

type I error

A

error that occurs when researchers conclude that there is a causal relationship between two variables when in fact there is not (a false positive conclusion)

52
Q

type II error

A

error that occurs when researchers conclude that there is not a causal relationship between variables when in fact there is (a false negative conclusion)

53
Q

cross-sectional study

A

a study in which people of different ages are compared with one another at the same time

  • get a snapshot of people at the same time
  • more feasible alternative to longitudinal study
54
Q

longitudinal study

A

research in which the same people are restudied and retested over a long period of time

  • useful for developmental psychology (how behaviours, personality, etc change over time)
55
Q

disadvantages to longitudinal research

A

takes alot of money and time

difficult to retain research participants (some move away, pass away, etc)

56
Q

disadvantages of the cross-sectional studies

A

cohort effect: the way ppl are affected by their coming from a particular time in history (50year old in 1950s vs 2010)

57
Q

central tendency

A

the typical, or representative score in a distribution, often referred to as the “average”

58
Q

inferential statistics

A

use sample results to infer what is true about the broader population; #s that are calculated from a distribution of scores to provide evidence supporting or opposing a hypothesis
(indicates the level of confidence in results)

59
Q

experimental group

A

participants in an experiment who receive the treatment level of the independent variable

60
Q

control group

A

participants in an experiment who do not receive the treatment

61
Q

null hypothesis significance testing (NHST)

A

an approach to evaluating research results that compared the observed outcome to what would be expected if the null hypothesis is true

= p>0.05 or p<0.05
when the sample’s result (or one more extreme) would happen less than 5 percent of the time if the null hypothesis is true = reject null and say the research is statistically significant

62
Q

null hypothesis

A

no difference between the variables in a population

ie, no difference between the experimental group and the control group

63
Q

effect size (“d”)

A

the magnitude of the relationship between manipulated or measured variables.
- the absolute value of the size of r captures the effect size = r of 0.30 = medium effect size, r of 0.80 = strong effect size.
a calculated number that indicates the size of a difference between two values; not affected by the sample size

“d” is used to represent the statistical calculation of the effect size

64
Q

confidence interval

A

a range of scores calculated such that there is a specific probability (usually 0.95) that the value of interest (such as the estimated mean of the population) actually falls within that range

  • if the confidence intervals don’t overlap = indicates that differences between the two groups are reliably different
65
Q

p-level

A

probability of finding a difference that is equal to or greater than what was actually measured, assuming the null hypothesis is true
the probability/odds that the results would have occurred if the random assignment had failed

66
Q

reliability

A

ability to detect the absence of differences or changes in the magnitude of a property
- findings can be consistently repeated

67
Q

3 criteria for causation (making casual claims)

A
  1. must show correlation between the variables
  2. establish the temporal precedence of the causal variable (one variable must come before the other)
  3. rule out alternative explanations (third-variable)

temporal precedence: establishing that the cause (i.e., independent variable) occurs before the effect (i.e., outcome)

68
Q

convenience vs random sampling

A

Random:
ie. random sampling because it used a random-digit dialer to select who was going to be in the study sample.
Convenience:
- there was no manipulated variable in the study, so there was no random assignment.
- a convenient group/choice given the study’s circumstances ( ie. psyc class in uni vs random ample of another population)
- whoever is there for observers to observe at that time

69
Q

note

A

Each method has its own strengths and weaknesses, so each one is better for answering some questions than others.

70
Q

experimental research

A
  • testing whether one variable causes another; establishes causal relationship between variables
  • independent (manipulated); dependant (control group)
  • ONE OF THE VARIABLES MUST BE MANIPULATED
  • uses random assignment
  • determines causation
71
Q

descriptive research

A

about a single variable and not about a relationship

  • Most descriptive studies try to provide concise summaries of one variable at a time.
  • often based on self reports
  • usually expressed as percentages or frequencies
72
Q

what happens when you add a data point lower than the mean

A

, the mean decreases but the standard deviation increases.

73
Q

what happens when you add a data point higher than the mean

A

both the mean and the standard deviation increase

74
Q

how are mean and standard deviation similar?

A

both examples of descriptive statistics; Both means and standard deviations summarize participants’ different responses.

75
Q

how are the r and d statistic similar?

A

Both r (strength) and d (effect size) indicate the strength of the relationship between two variables.

76
Q

TRUE OR FALSE: a low p-value means the hypothesis is true

A

FALSE
it just means the data are unlikely to occur under the null hypothesis
- significant result

77
Q

TRUE OR FALSE: a high p-value means that the hypothesis is false/should be abandoned

A

FALSE
it just means that the data are likely to occur under the null hypothesis
- non-significant result

78
Q

meta-analysis

A
  • provides a quantitative estimate of the effect size in the previous body of research
  • demonstrate research replication
79
Q

Saying that a finding is “statistically significant” means that it

A

is unlikely to have happened by chance if the null hypothesis were true

80
Q

TRUE or FALSE: every study must have a strong external validity, or the results will not be considered reliable

A

Many studies sacrifice external validity for internal validity; studies that make this sacrifice are sure not to make extensive claims about their generalizability.

81
Q

TRUE or FALSE: every study has to reflect the global population

A

Not every study has to reflect the global population; rather, the participants used in a study should be parallel to the specific types of people that the study aims to analyze.

82
Q

idea

A

unorganized principles or thoughts about social behaviour

83
Q

what’s a variable?

A

event, situation, behaviour, or characteristics that take on more than one value

84
Q

conceptual variable definition:

A

concept or meaning of a variable (what is it)?

ie:
age is conceptual, operationalized to be days since birth (for example)

exercise (conceptual), operationalized (ie: miles run per month)

85
Q

predictions

A
  • more specific than the hypothesis

- – >uses operational definitions of variable

86
Q

placebo

A

A placebo is a sham substance or treatment which is designed to have no therapeutic value.

PSYC 101 (3 decks)

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