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Quantitative Research in Criminology > Exam 1 > Flashcards

Exam 1 Flashcards

1
Q

Quantitative characteristics

A
  • N = population size

- correlation doesn’t equal causation, correlation is an association

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2
Q

Quantitative strengths

A
  • good at finding correlation
  • yields many responses (more representative)
  • easier to chart
  • gives a general outlook on a social situation
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3
Q

Quantitative weaknesses

A
  • not good at finding causation
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4
Q

Qualitative characteristics

A
  • n = sample size
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5
Q

Qualitative strengths

A
  • easier to establish causation

- in-depth

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6
Q

Qualitative weaknessess

A
  • generalization more difficult to establish

- not applicable to the general population

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7
Q

Population

A
  • total set of subjects of interest in a study
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8
Q

Sample

A
  • subset of the population on which the study collects data
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9
Q

Parameter

A
  • numerical summary of the population
  • the value you are trying to uncover, cannot often do it precisely
  • we do not always have access to the entire population
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10
Q

Statistic

A
  • numerical summary of the sample data

- to get a better sense of what the perimeter value might be

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11
Q

Descriptive statistics

A
  • statistics summarizing (outlining) sample or population data
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12
Q

Inferential statistics

A
  • statistics making predictions about population parameters based on sample data
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13
Q

Qualitative variable

A
  • variable that is placed on a measurement scale that has numerical values
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14
Q

Quantitative variable

A
  • variable that is placed on a measurement scale that has a set of categories
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15
Q

Discrete variable

A
  • variable taking the form of a set of separate numbers, such as 0, 1, 2, 3
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16
Q

Continuous variable

A
  • variable that can take an infinite continuum of real number values
17
Q

Measurement scales: interval

A
  • quantitative, scale with specific numerical distances between levels
  • Nominal: qualitative, scale with categories that are in no specific order
  • Ordinal: qualitative, scale with categories that are in a specific order
18
Q

Sampling methods

A
  • Random sampling: drawing a sample of n subjects who each have the same probability of being drawn, ability to better make inferences / draw conclusions about the population
19
Q

Sampling errors

A
  • Sample bias: sample is not representative
  • Response bias: under and over reporting
  • Non-response bias: large sample, few participants
20
Q

Distribution shapes

A
  • normal (bell-shaped)
  • U-shaped
  • skewed to the right
  • skewed to the left
21
Q

Frequency

A
  • relative frequency: proportion of observations falling into a category
22
Q

Frequency distributions

A
  • bar graph (typically categorical data)
  • pie chart (typically categorical data)
  • histogram (typically quantitative data)
23
Q

Central tendency: mean

A
  • an average

- sum of the observations divided by total number of the observations

24
Q

Central tendency: median

A
  • observation that falls in the middle of the ordered sample
  • what is the most typical observation you can come across
  • mean and median are usually close in a normal distribution
25
Q

Central tendency: mode

A
  • value that occurs most frequently in the distribution
  • unimodal: for one mode
  • bimodal: for two modes
  • multimodal: for more than two modes
26
Q

Variability: range

A
  • difference between the largest and smallest observations
27
Q

Variability: deviation

A
  • difference between an observation and the mean (i.e. how far an observation ‘falls’ from the mean of the population or the sample)
28
Q

Variability: standard deviation

A
  • typical (average) deviation from the mean for an observation in the set
  • will always have a positive value, but can go either way
29
Q

Variance

A
  • approximate average of squared deviations in a distribution
30
Q

Percentile

A
  • measure of data dispersion breaking down the distribution in percentage points.
  • an observation’s percentile indicates the percentage of observations that are of equal or lesser value in the distribution.
  • conversely, an observation’s percentile also allows us to calculate the percentage of observations that fall above it in the distribution.
  • impossible to have a 100 percentile (implies that 100% of the population has the same value or be below your value)
31
Q

Quartile

A
  • measure of data dispersion breaking down the distribution in four ordered segments.
  • when ordered in ascendance, the first 25% of the data distribution comprise the lower quartile, whereas the first 75% of the distribution comprise the upper quartile
32
Q

Quartile order

A
  • Min: 0% of the data
  • Q1: First 25% of the data
  • Med: First 50% of the data
  • Q3: First 75% of the data
  • Max: Fully 100% of the data
33
Q

Empirical Rule

A
  • ~ 68% of observations fall between the mean and one standard deviation on either side
  • about 2/3 will fall on both side of the middle
    ~ 95% of observations fall between the mean and two standard deviations on either side
  • two standard deviations (20-7-7 — 13, 6)(95% will be greater than 6 and lower than 13)
  • over 99% of observations fall between the mean and three standard deviations on either side.
  • data will be cluster around the middle
  • rare to find observations that fall off of the distribution
34
Q

Z-score

A
  • the number of standard deviations that any given observation in a distribution falls away from the mean of that distribution
  • tells you the right tail probability associated with that z-score, can also use it to find the LTP
  • RTP is the probability of encountering another observation that is further away removed from the mean than the observation in question
35
Q

Sampling distribution of a statistic

A
  • probability distribution that specifies probabilities for the possible values the statistic can take
  • every sample a mean; can draw a distribution form the mean
36
Q

Sampling distribution of sample means

A
  • the probabilities of specific values the mean of a sample would take if we repeatedly drew random samples from the population
  • the sample distribution of y-bars (distribution of the means of the samples that we have collected)
  • whereas a ‘regular’ probability distribution has a standard deviation (σ), a sampling distribution of sample means has a standard error (σȳ).
  • same concept of a standard deviation
37
Q

Central Limit Theorem

A
  • for random sampling with a large sample size n, the sampling distribution of the sample mean ȳ is approximately a normal distribution (n=30 is sufficient)
38
Q

Measurement scales: nominal

A
  • qualitative, scale with categories that are in no specific order
39
Q

Measurement scales: ordinal

A
  • qualitative, scale with categories that are in a specific order

Quantitative Research in Criminology (1 decks)

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