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Test 1 Flashcards

1
Q

Where does most of our understanding of the social world come from?

A
  • Authority
  • Tradition
  • Common sense
  • (Social) media
  • Personal experience (experience, observation, interpretation, intuition)
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2
Q

What are sources of bias?

A

Systemic distortions during interpretation.

  • Overgeneralization
  • Selective observation
  • Expectations
  • Premature closure
  • Halo effect
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3
Q

What are the goals of research?

A
  • Description
  • Explanation
  • Prediction
  • Influence
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4
Q

What are the types of research?

A
  • Basic

- Applied

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5
Q

What is basic research?

A

Advances knowledge without necessarily having any obvious practical applications.

  • Description
  • Explanation
  • Prediction
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6
Q

What is applied research?

A

Provides solutions for specific practical problems and advancing quality of life.

  • Influence
  • Solve problem
  • Money
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7
Q

What is science?

A
  • Critical approach to asking questions about how a system works: General methodology independent of subject matter.
  • Process of making structured observation, forming theories, and adapting theories in response to new empirical evidence (data).
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8
Q

What is quantitative research?

A

Systematic empirical study of observable phenomena via statistics and mathematics (describe data using numbers, measurements).

  • Experiments
  • Correlation
  • Surveys
  • Observation
  • Content analysis
  • Existing statistics
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9
Q

What is qualitative research?

A

In depth inquiry into specific experiences by describing and exploring meaning via narrative (describe data using words, images, sounds).

  • Qualitative interviews
  • Focus groups
  • Field research
  • Content analysis
  • Historical-comparitive
  • Alternative production
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10
Q

What is the scientific process?

A
  1. Observation - data collection
  2. Theory - current explanation
  3. Hypothesis - specific prediction
  4. Observation - test prediction
  5. Evaluate/Modify theory
  6. …
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11
Q

What are theories?

A

Integrated systems of assumptions and principles that attempt to organize and predict all currently known observations.

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12
Q

What are the characteristics that scientific theories are evaluated with?

A
  • Falsifiability: Must be testable and rejected (or adjusted) if predictions are not confirmed.
  • Parsimony: Must reflect the simplest possible explanation for the current body of knowledge.
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13
Q

What are hypotheses?

A
  • Testable predictions that are derived logically from theory - falsifiability
  • Describe the specific relationships between two or more variables
  • Acceptance or rejection of allows for evaluation/modification of theories
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14
Q

What are variables?

A

Any characteristic that can have a range of different values (anything that can vary).

  • Data point: individual piece of information
  • Data set: collection/group of data points
  • Data distribution: pattern of the data points
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15
Q

What are descriptive methods?

A

Methods that observe and describe phenomena as they exist and with minimal interference.

  • Naturalistic observation
  • Laboratory observation
  • Case studies
  • Survey research
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16
Q

What is naturalistic observation?

A

Observing behaviour as it occurs in its natural setting and without interference.

  • Natural and spontaneous behaviour
  • Impossible/unethical to manipulate
  • Covert observation of participants
  • Experimenter bias/effects/influence
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17
Q

What is laboratory observation?

A

Observing behaviour without interference in more controlled conditions of the laboratory.

  • Reduction of random variables
  • Allows use of precision equipment
  • Artificial elements become factors
  • Awareness of potential observation
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18
Q

What are case studies?

A

In depth observation of a single or small number of rare or extraordinary cases.

  • Rare phenomena and unique cases
  • Use multiple and varied approaches
  • Gain insight/formulate hypotheses
  • Hard to generalize/draw conclusions
  • Weak basis for normal behaviour
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19
Q

What is survey research?

A

Susceptible to a number of potential weaknesses that may distort results:

  • Sampling bias
  • Wording effects
  • Self-report bias
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20
Q

What is sampling bias?

A

Failure to question/survey sample that is representative of the larger population.

  • Population: group of interest
  • Sample: subset being measured
  • Random/representative sampling
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21
Q

What are wording effects?

A

Subtle changes in wording of a question can lead to dramatically different results.

  • Censorship vs restrictions
  • Welfare vs. aiding the needy
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22
Q

What is self-report bias?

A

Inability to report accurately or honestly on individual’s own behaviours/attitudes.

  • Social desirability
  • Sexual behaviour
  • Racism and sexism
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23
Q

What is correlation?

A

Statistical measure of the strength/direction of the relationship between multiple variables.

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24
Q

What is the correlation coefficient?

A

Numerical representation of the strength and direction of the relationship between variables.

  • Statistical term ‘r’
  • Complete range (-1 to 1)
  • Strength (number 0 to 1)
  • Direction (valence in +/-)
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25
Q

What is positive correlation?

A

Variables increase/decrease together: ranging from 0 (weak) to +1 (perfect).

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26
Q

What is negative correlation?

A

One variables increases/one decreases: ranging from 0 (weak) to -1 (perfect).

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27
Q

What is the experimental method?

A
  • Allows the experimenter to manipulate factors of interest while holding all others constant (controls other factors/influences).
  • Increased control allows the experimenters to determine casual direction: cause and effect.
  • Advantage over all previous methods.
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28
Q

What are experimental variables?

A
  • One variable changes in response to another.

- Dependent changes in response to changes in independent.

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29
Q

What is an independent variable?

A

Represents the specific intervention or the variable that is being manipulated.

  • Varied by the experimenter
  • Multiple treatment/levels
  • Subject/treatment/etc
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30
Q

What is a dependent variable?

A

Outcome of interest that should change in response to the level of the treatment.

  • Any measurable response
  • Stable/reliable/accurate
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31
Q

What are experimental groups?

A

Group of participants that are exposed to the independent variable/treatment.

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32
Q

What are control groups?

A

Group of participants exposed to the same conditions as the experimental group, but not the independent variable.

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33
Q

What criteria does the data require before being adopted?

A
  • Generalizability: apply to other groups
  • Replicability: able to repeat effects
  • Reliability: test yields consistent results
  • Validity: are measuring what you intend
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34
Q

What are the potential issues that might make someone question the validity of experimental findings?

A
  • Confounding and uncontrolled variables
  • Who is studied - sampling/selection
  • Expectation - placebos/blind study
  • Researcher bias - double-blind studies
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35
Q

Why act unethically?

A
  • Publish or perish: career and funding
  • Growing knowledge and certainty
  • Prestige (personal and financial gain)
  • Shortcuts: deadlines, budgets, etc
  • Ignorance, misconceptions, bias, etc
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36
Q

What is scientific misconduct?

A

Violating accepted ethical norms and standards.

  • Research fraud: invent, falsify, distort data or lying about how a study was conducted
  • Misrepresenting findings: p-hacking
  • Plagiarism: presenting another’s ideas, words or work as your own or without proper credit
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37
Q

What are the formal ethics provided by?

A
  • Governments, institutions, professional organizations.
  • Tri-Council Agencies
  • Research Ethics Board
  • Professional code of ethics
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38
Q

What are the ethical guidelines?

A
  • Voluntary/informed consent
  • Freedom to withdraw
  • Protection from harm
  • Confidentiality/anonymity
  • Avoid coercion/deception
  • Debriefing/results
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39
Q

What are levels of measurement?

A

How variables are measured will affect the amount of information you have about them. Level of measurement will influence the types of analysis that we can perform a data set.

  • Nominal
  • Ordinal
  • Interval
  • Ratio
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40
Q

What is nominal?

A

Named categories with no implied hierarchy or ordering among them.

  • Mutually exclusive: only belong to one
  • Collectively exhaustive: all possibilities
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41
Q

What is ordinal?

A

Ordered categories where the distances between them cannot be considered equal. Compare between categories but cannot know the exact difference between them.

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42
Q

What is interval?

A

Equal distances (intervals) between values with an arbitrary zero point. (IQ, rating scales, celsius)

43
Q

What is ratio?

A

Equal distances between values with a meaningful/absolute zero point. (weight, height, siblings)

44
Q

What is interval/ratio?

A

Meaningful/absolute zero of ratio variables allows performance of math. Difference between interval and ratio is not an issue for statistical analysis.

45
Q

What is operationalization?

A

Defining a fuzzy concept to make it distinguishable, measurable and understandable in terms of empirical observations.

  • Define concepts by measurement
  • Attraction/popularity/happiness
  • Nominal/ordinal/interval/ratio
46
Q

What are descriptive statistics?

A

Present, organize and summarize larger sets of numbers using fewer numbers.

  • Central tendency
  • Variability/spread
47
Q

What are inferential statistics?

A

Methods that compare groups, or to draw conclusion about population from samples.

  • Statistic (sample)
  • Parameter (population)
48
Q

What is a population?

A
  • The entire group that researchers want the results to apply (entire group of interest).
49
Q

What is a sample?

A

Subgroup being studied.

50
Q

Why limit research to samples when you are ultimately interested in complete population?

A
  • Population potentially massive
  • Inefficient to study everyone
  • Population changes over time
51
Q

What are the challenges with using sample over population?

A

Main difficulty is that any sample will differ from the population due to random factors.
- Random error: sample does not equal population
Consequently any difference or association between samples may be due to chance.
- Inferential stats: account for chance

52
Q

What are representative samples?

A

Minimize random error by collecting data from samples that are representative of populations. This means that the make-up of your sample should approximate that of entire population.

53
Q

What is a parameter in inferential statistics?

A

Population - summary characteristic of entire population.

54
Q

What is a statistic in inferential statistics?

A

Sample - summary characteristic of a particular sample from the population.

55
Q

What is a unit of analysis?

A

Who or what you are measuring - level of unit that provides the data points for your research.

  • Individual
  • Group
  • Company
  • City/county
  • Province/state
  • Country
56
Q

What are frequency distributions?

A

Summarize distribution of variables by reporting number of responses contained in each category.

  • Variable name/categories of analysis
  • Frequency - number of responses
  • Total number of responses (N)
57
Q

What is relative frequency?

A

Compares number of responses in specific category to the total number of responses. (proportion) Should add to 1.
rf = f/N

58
Q

What is percentage?

A

Simple transformation of relative frequency: estimates frequency response/100 cases. Should add to 100.
% = f/N X 100

59
Q

What is a cumulative frequency?

A

Number of cases with values above/below.

f: 5 19 10
cf: 5 24 34

60
Q

What is a cumulative percentage?

A

Simple transformation of cumulative frequency: estimates frequency of responses/100 cases.
c% = cf/N X 100

61
Q

What is grouped frequency?

A

Interval data sometimes spread over larger range of scores making frequency distribution unclear. Condense scores into groups/categories (class intervals) containing more than one score value.

  • Calculate range:highest - lowest
  • Divide range/number of categories
  • Round value up = class interval
62
Q

What are flexible intervals?

A

Class intervals do not always have to be of the same size: will depend on distribution of data.

  • Expand upper/lower intervals
  • Class intervals of different sizes
  • Relative significance of intervals: depending on variable, differences between intervals can have vastly different meanings (set interval to normalize meaning)
63
Q

What is a proportion?

A

p = f/N

64
Q

What is a percentage?

A

% = f/N X 100

65
Q

What is a ratio?

A

f1/f2 X Base

66
Q

What is a cross-tabulation?

A

Table that presents distribution of one variable (usually dependent) across categories of one or more variables (usually independent).

Can express the frequencies of each cell as percentages:

  • Column totals
  • Row totals
  • Sample total
67
Q

What are measures of central tendency?

A

Express groups of scores in terms of their middle most value. Effectively summarize groups of scores and facilitate individual and group comparisons.

  • Mean
  • Median
  • Mode
68
Q

What is the mean?

A

Arithmetic mean is the primary index of central tendency for interval and ratio data. Allows for the comparison of groups of different sizes- score value per subject.
mean = sum of all scores/number of scores
X(bar) = ∑X/n

69
Q

What is the mean formula for a population?

A 
u = ∑Xi/N
u: mean of population (mu)
∑: stigma - sum of
Xi: particular score/observation
N: number of scores (population)
X: individual score/observation
70
Q

What is the mean formula for a sample?

A 
X(bar) = ∑Xi/n
X(bar): mean of sample
∑: stigma - sum of
Xi: particular score/observation
n: number of scores (sample)
X: individual score/observation
71
Q

What are the deviation scores?

A

Total distance of all scores above the mean equals the total distance of all scores below the mean (sum always zero).
Subtract each point from the mean, add them all to get 0.

72
Q

What are outliers?

A

Extreme values that pull the mean away from the central tendency of distribution.

73
Q

What is the median?

A

Value where half of all scores fall above and half fall below - rank order scores and count to the middle value.
- Odd: count to middle most value
- Even: Average of both middle values
median = N+1/2 (not the median but where the median is placed)

74
Q

How does the median help?

A

Inappropriate for most inferential statistics, sometimes better for descriptive statistics.

  • Minimzes extreme scores
  • Floor and ceiling effects
  • Only option for ordinal data
75
Q

What is the mode?

A

Most frequently occurring score in distribution.

  • Only option for nominal
  • Unimodal (one mode - one bump)
  • Bimodal (two modes - two bumps)
  • Multimodal (many modes)
76
Q

What is the normal distribution?

A

Theoretical bell-shaped frequency distribution that is unimodal and symmetrical: approximated in nature and research.

  • Mean = median = mode
  • Symmetric/zero skew
  • Mosokurtic
  • Asymptotic tails (doesn’t touch x-axis)
77
Q

What is skewness?

A

Refers to the symmetry, or lack of symmetry, of the distribution.

  • Positive = tail skewed right (flat part of curve on right)
  • Negative = tail skewed left
78
Q

What is kurtosis?

A

Refers to how flat or curved the shape of the distribution is.

  • Leptokurtic: peaked (low variability)
  • Mesokurtic: normal
  • Platykurtic: flat (high variability)
79
Q

What is the five-number summary?

A

Descriptive statistics that provide information about dataset - five most important percentiles.

  • Sample minimum: 0% below/100% above (lowest point)
  • First/lower quartile: 25% below/75% above (median of the lower half)
  • Median sample: 50% below/50% above (median)
  • Third/upper quartile: 75% below/25% above (median of the upper half)
  • Sample maximum: 100% below/0% above (highest point)
80
Q

What are pie charts?

A

Circular chart using slices to represent frequencies or percentages - all slices add up to 100%.

  • Nominal variables only (ordinal possible/weird)
  • Explode slice of interest (one slice pokes out more)
  • Approximately five categories
81
Q

What are graphs?

A

Represent data as a series of bars of different lengths: unlimited variables and all levels of measurement. Bars generally (not always) arranged so category is along x-axis and value is along y-axis.

  • Bar graph
  • Histogram
82
Q

What is a bar graph?

A

Space out bars to represent discontinuity between (nominal) variables.

83
Q

What is a histogram?

A

Bars touch to represent continuity between (interval) variables.

84
Q

What are frequency polygons?

A

Plot frequencies or midpoints of intervals then connect the dots.

  • Ordinal/interval data since it highlights continuity
  • Connect lines to estimate values
85
Q

What are measures of variability?

A

Express the pattern or spread of individual scores around the mean.

  • Pattern of scores around the mean
  • Distance of scores from other scores
86
Q

What is the range?

A
  • Difference between the highest and lowest scores in a distribution - simplest measure.
  • Expressed as interval containing all scores in the distribution from lowest to highest.
87
Q

What are deviations from the mean?

A

Distance (deviation) of individual scores in a distribution from the mean of the distribution.
deviation score = Xi - X(bar)
∑(Xi - X(bar)) = 0 always

88
Q

What is the mean deviation?

A

Average deviation of a score from the mean: average absolute values of difference scores.
mean deviation = ∑|Xi - X(bar)| / n
- Would be great measure of variability, except for absolute values

89
Q

What is variance?

A

Average of the squared deviations from the mean - spread of scores around the mean. Square the deviation scores instead of using absolute values - solves problem.

  • Sum of squares (SS) ∑ (Xi - X(bar))^2
  • Divide by N or n-1

Does reasonable job representing variability in the data but expressed in units squared. Squaring results in large values makes relating to mean difficult.

90
Q

What is the formula for variance in population?

A

o^2 = ∑(X-u)^2 / N

91
Q

What is the formula for variance in sample?

A

s^2 = ∑(X - X(bar))^2 / n-1

Divide by n-1 because sample variance underestimates population variance.

92
Q

What is standard deviation?

A

Calculated by taking the square root of the variance - average variability around mean. Comparison easy, same units as mean.

93
Q

What are Z scores?

A

Number of standard deviations that a particular score is away from the mean of its distribution. Using standard deviation as the unit of reference allows for immediate understanding of where the score fits into the distribution.

  • Compare scores on different distributions
  • Percent of scores above/below the score
  • Exact placement of the normal distribution
94
Q

How to calculate z scores (raw score to z score)?

A
  1. Calculate deviation score
  2. Divide by standard deviation
    Z = X-X(bar) / s
    - Negative: below mean
    - Positive: above mean
95
Q

How to calculate raw score (z score to raw score)?

A
  1. Multiply Z score by standard deviation
  2. Add deviation score to the mean
    X = X(bar) + (Z)(SD)
96
Q

What is standard normal distribution?

A

Shape of the distribution of standard scores will always match distribution of raw scores.

  • Mean of Z scores = 0
  • Standard deviation = 1
  • Area under curve = 1
97
Q

What are standard scores?

A

Converting to Z scores allows you to compare scores that come from different distributions.

  • Different means/standard deviations
  • Normalizes to 0/1
98
Q

What does knowing the Z score allow you to determine?

A
  • Percentage of scores above/below that score
  • Percentage of scores between any two scores
  • Z score value for any particular percentage
99
Q

What is probability?

A

Relative likelihood that one particular outcome will or will not occur relative to some other outcome.
- Outcome: result unknown in advance
- Frequency: times something happens
Expected relative frequency
probability = successful outcomes / all possible outcomes

100
Q

What is the range of probability?

A

Proportion of possible successful outcomes to the total number of possible outcomes: never less than 0 or greater than 1.

  • p = 1: absolute certainty (100%)
  • p = 0: complete impossibility (0%)
101
Q

What is the probability addition rule?

A

Probability of mutually exclusive events occurring is sum of individual probabilities. Mutually exclusive means that one outcome excludes the possibility of all others occurring.
- Sum of all outcomes: p = 1

102
Q

What is the probability multiplication rule?

A

Probability of multiple independent outcomes occurring is product of individual probabilities. Independent outcomes mean that the outcome of one event has no influence on another event.

103
Q

What is the gambler’s fallacy?

A

Belief that if/when deviations from expected results are observed, future deviations in the opposite direction are more likely.

  • Repeated losses does not mean you are due to win
  • Outcome independent of previous outcomes
  • Probabilities even out only after infinite trials
104
Q

What does probability have to d with everything else?

A

Any distribution can be defined in terms of its mean and standard deviation.

  1. Assume normal distribution
  2. Normalize data - convert to Z
  3. Exact probability of any score

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