Research Flashcards
1
Q
Normative analysis
A
prescriptive, based in reason and logic
2
Q
Empirical analysis
A
descriptive / explanatory, based on observation and measurement
3
Q
Scientific approach
A
an understanding of knowledge (epistemology) and way of obtaining knowledge (methodology)
4
Q
Positivism
A
there is an objective reality
5
Q
Interpretivism
A
reality changes with perspective and is decided by the individual
6
Q
Core beliefs of scientific approach
A
- Empiricism: knowledge is derived from real world observation, not theoretical deduction
- Determinism: everything has a cause that we can find
- Objectivity: science should accurately represent reality
- Replication: science is cumulative, so we need to repeat research to make sure it’s correct
7
Q
Intersubjectivity
A
multiple studies should demonstrate similar findings
8
Q
Components of a research report
A
- Abstract/executive summary
- Introduction
- Research design
- Presentation of findings
- Discussion
- Conclusions
- References
9
Q
Null findings
A
research that results in no proven connection between 2 concpets
10
Q
Replicable
A
showing you research so others can replicate it to prove your hypothesis
11
Q
Transmissible
A
using research that is easy to understand
12
Q
Informed consent
A
research participants need to fully understand the extent to which they will participation and the nature of the project
13
Q
Systematic errors
A
getting the same wrong answer after multiple attempts
14
Q
Random errors
A
getting different answers each time you attempt
15
Q
Levels of measurement (NOIR)
A
- Nominal: only names and categories
- Ordinal: ordered information
- Interval / Ratio: exact numbers
16
Q
Applied research
A
research done to solve a real world problem
17
Q
Basic research
A
research done for the sake of understanding new ideas
18
Q
Measurement errors
A
gap in between you expected result and the actual result
19
Q
Positive correlation
A
both variables move in the same direction (up or down)
20
Q
Negative correlation
A
variables move in opposite directions
21
Q
Causation
A
one concept happens because of another concept
22
Q
Correlation
A
2 concepts move similarly, but this may not be because of each other
23
Q
Independent variable
A
the variable that isn’t changed by the other (cause)
24
Q
Dependent variable
A
the variable that changes based on the other one (effect)
25
Types of random sampling
- Simple: draw from a group
- Systemic: creating an algorithm to draw from a group
- Proportionate: random selection based on the percentages in the population
- Disproportionate: weighted random selection to over-represent certain groups
26
Types of non-random sampling
- Convenience / Accidental: sampling the first people you can find
- Volunteer: people select to be a part of the sample
- Purposive / Judgemental: manually creating a sample from personal judgement and knowledge
- Snowball: starting with a small group who then ask others like them
- Quota: selecting people to fill a requested list
27
Sampling error
difference in sample statistic and population parameter
28
Margin of error / confidence interval
how close the sample is to the population (as represented by a % range)
29
Document analysis
gathering key facts and basic level information from documents (names, dates, times, numbers)
30
Text analysis
systematic study of content, form, and substance of a text
31
Discourse analysis
what the text says about society, meaning, and interactions
32
Content analysis
message of the text, frequency of terms, length of text
33
Structural content analysis
physical measurements of the content (pictures, titles)
34
Substantive content analysis
what is being said or written (idealogical leanings, coverage bias)
35
Manifest content
visible surface content
36
Latent content
underlying meaning of the content
37
Codebook
rules of the analysis (what words you are looking for, what part of the content)
38
Intercoder reliabilty
multiple people reading or watching the same content and getting the same result
39
Interviewer effect
the impact the interviewer's presence has on the interviewee and the information shared
40
Observation research
observing behaviour in it's natural setting as it occurs
41
Participant observation
being a part of the group you are studying
42
Obtrusive participant observation
the group knows they are being studied
43
Unobtrusive participant observation
the group does not know they are being studied
44
Covert participant observation
undercover observation of a group (illegal)
45
True participant observation
researching a group you already belong to
46
Participant observer
identifying yourself to the group you will be studying
47
Complete observer
observing the behaviour of a group from outside the group
48
Reactivity
the researcher's impact upon the group they are studying and the information shared
49
Data saturation
no new information is gained from new interviews and studies
50
Interview framework
guidelines of what information you want to ask in the interview
51
Hawthorne effect
people will change their answers when they know they are being studied (social desirability)
52
Focus group
small group of similar people with the purpose of gaining new and specialized information
53
Secondary data
data collected by another researcher that you are allowed to use
54
Closed-ended questions
participants have to choose from a preset group of answers
55
Open-ended questions
participants can answer in any way they want
56
Exhaustive
everyone taking a survey has an option for them (or "other")
57
Aggregate data
all the data collected combined to create an average
58
Microdata
the individual data collected from each case
59
Metadata
technical information about how the information was collected
60
Omnibus survey
multiple groups run one survey and get relevant information for them from it (to save money)
61
Cross-sectional study
one-time question asked at one moment in time
62
Longitudinal / Panel study
long-term study that asks multiple questions about a topic over time
63
Non-response bias
people who agree to take the study may not accurately represent the population
64
Research design
imposed controlled restrictions with the purpose of observation
65
Experimental / Treatment group
the group given treatment before the post-test
66
Control group
the group not given treatment (or given placebo) before the post-test
67
Between-subjects design
doing the experiment without giving a pre-test
68
Within-subjects design
giving both groups a pre-test to better contextualize the results of the post-test
69
Internal validity
controlled space where you can be sure the outcome is a direct result of the treatment
70
External validity
real world where the outside factors may influence the outcome
71
Quasi-experiment
looking at statistics from the past to conduct an experiment as if it was happening now
72
Factorial design
testing multiple factors, the sub categories within those factors, and how they relate to each other
73
Double-blind design
both the participant and the administrator do not know if they are in the treatment group or the control group
74
Single-blind design
only the participant does not know if they are in the treatment group or the control group
75
Small-N research
qualitative approach, usually less than 30 cases
76
Large-N research
quantitative approach, large number of cases
77
Case study
in-depth investigation of a single individual, group, or event
78
Descriptive case study
great detail of everything that happens in a case
79
Theory-testing case study
cases that confirm of dispute current theories
80
Failed most-likely case
case outcome is expected to confirm a theory, but it disproves it
81
Successful least-likely case
case outcome is expected to disprove a theory, but it confirms it
82
Process tracing
explaining each step of a case development to demonstrate the causation between 2 concepts
83
Comparative research
small-N, contrasts cases to strengthen generalizations
84
Most-similar-systems design
cases with the same factors that get different outcomes, so causation can not be proven
85
Most-different-systems design
cases with different factors that get the same outcome, so causation can not be proven
86
Galton's problem
2 different things under observation may influence each other and lose their independence
87
Audit trail
detailed description of the research steps taken
88
Cross-tabulations
table used to compare 2 nominal or ordinal variables
- IV: row (across), DV: column (down)
89
Descriptive statistics
statistics used to quantitatively describe information
90
Inferential statistics
statics used to infer from a random probability sample to a population
91
Univariate statistics
describes or infers a relationship between the value of 1 variable
92
Bivariate statistics
describes or infers a relationship between the values of 2 variables
93
Multivariate statistics
describes or infers a relationship between the value of 3 or more variables
94
Frequency distribution
how many cases take each value (raw and relative)
95
Raw frequency distribution
exact number of cases in each value
96
Relative frequency distribution
proportion of cases in each value represented by a %
97
Measure of central tendency
the most typical number (one number that represents the entire distribution)
98
Measure of dispersion
how much the values vary
99
Measures for nominal variables
- Central tendency: mode (most) (value with greatest number of cases or highest %)
- Dispersion: variation ratio (% of everything not in the modal category)
- Association: Lambda (PRE-based), Cramer's V (not PRE-based)
100
Measures for ordinal variables
- Central tendency: median (middle) (value of the middle case)
- Dispersion: range (value of the lowest category to value of the highest category)
- Association: Gamma (PRE-based), Tau-B (symmetrical table), Tau-C (asymmetrical table)
101
Measures for Interval Ratio variables
- Central tendency: mean (average) (average of all values, add values then divide by # of cases)
- Dispersion: Standard deviation (average of all dispersions from the mean)
- Association: Pearson's R (linear data), Spearman's RHO (non-linear data)
102
Standardized scores
exact number of standard deviation units a case is above or below the mean
103
Proportional Reduction in Error (PRE)
how much knowing the value of the cases in the IV helps you predict the values of the DV
104
Basic linear regerssion
the regression line crosses the graph at the closest value to every point
105
Intercept line
what number the regression line starts on the graph
106
Type 1 error
assume that2 variables are related, but they are not
107
Type 2 error
assume that 2 variables are not related, but they are
108
Chi-Square
gives the likelihood of each possible degree of relationship occurring in a sample if there was no relationship in the population (nominal and ordinal only)
109
Control variable
the variable that is unchanged to better understand the relationship between variables
110
Statistical significance
how likely it is that a relationship between 2 variables in a sample might have occurred by chance and may not exist in the population
111
Difference of means
- T-Test: compares the means of 2 cases
- ANOVA: compares the means of multiple cases
