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Project Prep > Project Prep Benchtest > Flashcards

Project Prep Benchtest Flashcards

1
Q

Research question characteristics

A
  • Focused on a single problem
  • Researchable using primary/secondary sources
  • Feasible to answer within the timeframe and practical constraints
  • Specific enough to answer thouroughly
  • Complex enough to devlop the answer over a space of a paper or thesis
  • Relevant to your field of study/or society more broadly
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2
Q

Types of Research questions

A
  • Descriptive research
  • Comparative research
  • Correlational research
  • Exploratory research
  • Explanatory research
  • Evaluation research
  • Action research
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3
Q

What is in a problem statement?

A
  • Context
  • Specific issue being investigated
  • Why this problem? Why now? Currency?
  • Set objectives (project goals)
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4
Q

Descriptive research

A

What are the characteristics of X?

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5
Q

Comparative research

A

What are the differences and similarities between X and Y?

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6
Q

Correlational research

A

What is the relationship between variable X and variable Y?

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7
Q

Exploratory research

A

What are the main factors in X? What is the role of Y in Z?

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8
Q

Explanatory research

A

Does X have an effect on Y? What is the impact of Y on Z? What are the causes of X?

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9
Q

Evaluation research

A

What are the advntages and disadvantages of X? How well does Y work? How effective or desirable is Z?

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10
Q

Action research

A

How can X be acheived? What are the most effective strategies to improve Y?

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11
Q

S.M.A.R.T

A

Specific, Measurable, Attainable, Realistic, Timely

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12
Q

Inductive vs Deductive research

A

Developing a theory vs testing a theory

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13
Q

Exploratory vs Explanatory research

A

Exploring the main aspects of problem vs explaining causes and consequences of a well defined problem

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14
Q

Academic critique

A
  • Deep dive into a single body of work
  • Should be a counter argument - need to use external evidence and give counter points
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15
Q

Positivist

A
  • Objective study
  • Reductionist (break down complexities into simpler units of study)
  • Verifying theories
  • Can be studied in isolation
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16
Q

Critical Theorist

A
  • Knowledge used to empower people
  • Participatory
  • Seeks to bring about change
  • Focus on empowering groups
  • Studied within that context
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17
Q

Constructivist

A
  • Truth is relative to context
  • Theory is open to interpretation
  • Generates theories in a given context
  • Cannot be studied in isolation
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18
Q

Pragmatist

A
  • All research is biased
  • No objective ‘truth’
  • Works towards pratical solutions to problems
  • Multiple answers
  • Seek the best one(s)
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19
Q

Reliability

A
  • How consistent are repeated measurements
  • How close together are the measurements
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20
Q

Validity

A

Results correspond to the real thing

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21
Q

Types of reliability assessments

A
  • Test-retest
  • Inter-rater
  • Internal
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22
Q

Types of validity assessments

A
  • Construct
  • Face
  • Concurrent
  • Predictive
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23
Q

Test-retest

A

-Determines reliability of the test and results over time
- Good indicator of reliability is strong correlation (r > 0.8) between same test given to same subjects over time
- Only works on consistent attributes

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24
Q

Inter-rater

A
  • Determines reliability of test measurements and results gathered by different researchers
  • Different people should give strongly correlated results
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25
Q

Internal

A
  • Do you get same results if you use different tests to measure the same thing
  • Strong correlation supports reliability
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26
Q

Construct (Validity assessment)

A

Does the test relate to high level theories

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27
Q

Face (Validity assessment)

A

Does test appear to test what it aims to test

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28
Q

Concurrent (Validity assessment)

A
  • Does the test relate to an existing similar validated test
  • Work is built on findings of another test and matches their work
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29
Q

Predictive (Validity assessment)

A

Does the test predict performance in a later developed test

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30
Q

Research Ethics

A

Concerns the responsibility of researchers to be honest and respectful to all individuals who are affected by their research studies or their reports of the studies’ results

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31
Q

Research integrity

A

Conducting research in a way that allows others to have trust and confidence in the methods used and findings that result from this

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32
Q

Bias

A

Conscious or unconscious influencing of the study and its results

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33
Q

Types of Bias

A
  • Recall bias
  • Selection bias
  • Observation bias
  • Confirmation bias
  • Publishing bias
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34
Q

Recall Bias

A
  • Survey respondents asked to recall events
  • different types of events more likely to be remembered than others
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35
Q

Selections bias

A

Samples can sometimes under-represent certain people and over represent others

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36
Q

Observation bias

A
  • Hawthorne Effect
  • When participants are aware that they’re being observed they, either consciously or unconsciously, alter the way they act or the answers they give
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37
Q

Confirmation bias

A
  • Occurs during interpretation of study data
  • Researchers consciously or unconsciously look for information or patterns that confirm the ideas or opinions that they already hold
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38
Q

Publishing bias

A
  • Studies with negative findings (nothing found) are less likely to be submitted by scientists or published by journals
  • Perceived as less interesting
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39
Q

Avoiding bias

A
  • Bias in per-course survey (unbalanced data) - automatic profiling
  • Bias in learning about user instead of type of user (stereotyping) - different users in training and test sets
  • Bias in future data predicting past - train on past, test on future
  • Bias in unbalanced data sample - stratified sampling
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40
Q

Literature review

A
  • A survey of scholarly sources on a specific topic(s)
  • Provides an overview of current knowledge allowing you to identify relevant theories methods and gaps in existing research
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41
Q

Review article

A
  • Summarises current state of understanding on a topic
  • Surveys and summarises previously published studies - rather than report on new facts or analysis
  • Gives roadmap on future research
  • Can be used to back up the validity of your question
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42
Q

Surveys

A

Any method focused on asking Participants for responses

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43
Q

Purpose of Surveys

A
  • Gather information not available from other sources
  • Ubiased representation of population interest
  • Collect information from many individuals to understand them as a whole
  • Allows massive information gathering
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44
Q

Type of data collected by surveys

A

Mainly quantitative but qualitative methods can be used too

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45
Q

Pros of Surveys

A
  • Can get info from large samples
  • Can have different types and numbers of variables
  • Gets info that’s hard to observe
  • Easy and cheap
  • Standardised stimulus - no observer subjectivity
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46
Q

Cons of surveys

A
  • Intentional misreporting to hide inappropriate behaviour
  • Poor recall
  • Response rates are critical
  • Can introduce bias from wording of questions
  • Inflexible - can’t be changed during data gathering
  • Not ideal for controversial issues
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47
Q

Survey Types by purpose

A
  • Exploratory - form general ideas about research questions
  • Descriptive - collect more specific descriptions of the variables of interest
  • Explanatory - develop understanding of relationships among variables of interest
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48
Q

How can you validate surveys?

A
  • Need to validate bias in question design
  • Ask positive and negative questions - should be given opposite answers
  • Validity of survey comes from the representativeness of the sample and the precision of the questions
  • Face validity - Do questions appear reasonable and acquire data you want
  • Content validity - Are questions all about issue and other subjects related to it
  • Internal validity - Do questions imply the outcome you want to achieve
  • External validity - Do questions elicit answers that are generalizable
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49
Q

Survey - Research questions

A
  • Correlational questions
  • Less technical questions - usability
  • Exploratory questions
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50
Q

Types of sampling

A
  • Random sampling- each member has equal chance of being picked
  • Stratified sampling- use subsets of the population to sample - lower sampling error
  • Systematic sampling- every Nth name is selected
  • Quota sampling- researcher chooses necessary number of participants per stratum
  • Purposive sampling- researcher selects participants according to criteria
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51
Q

Purpose of Observation

A

To understand how people naturally interact with products and people and the challenges they face

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52
Q

Pros of Observation

A
  • Can get more subtle data
  • Allows richly detailed description
  • Viewing or participating in unscheduled events
  • Improves quality of data collection
  • Can see things you weren’t expecting
  • Useful for formulating hypothesis
  • Doesn’t depend on information provided by respondents
  • Can deal infants/animals
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53
Q

Cons of Observation

A
  • Less structured responses
  • Get huge amount of data - analysing and not including bias is hard
  • Difficult to replicate - lots of variables you don’t have control of
  • Different researchers gain different understanding of what they observe
  • Male/female researchers have access to different information
  • Many events are uncertain in nature - difficult for researcher to determine time and place
  • Can’t generalise
  • Long and expensive
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54
Q

Observation - Research questions

A
  • Exploratory
  • Explanatory
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55
Q

Type of data collected by Observation

A

Typically qualitative but can be quantitative

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56
Q

Types of Observation

A
  • Complete observer
  • Observer as Participant
  • Participant as Observer
  • Complete Participant
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57
Q

Complete observer

A
  • Detached observer
  • Researcher is neither seen nor noticed by participants
  • Minimises Hawthorne effect - participants more likely to act natural
  • Most likely to raise ethical questions
58
Q

Observer as participant

A
  • Researcher is known and recognised by participants
  • Participants know research goals of the observer
  • Some interaction with participants but limited
  • Researchers aim is to play a neutral role
59
Q

Participant as observer

A
  • Researcher is fully engaged with the participants
  • More of a friend or colleague than neutral third party
  • Full interaction with participants but they still know its a researcher
60
Q

Complete participant

A
  • Fully embedded researcher
  • Observer fully engages with the participants and partakes in their activities
  • Participants aren’t aware that observation and research is being conducted
61
Q

How do you validate an observational study?

A

Use multiple independent researchers to observe

62
Q

Direct Observation

A
  • Quantitative technique
  • Explicitly counting the frequency and/or intensity of specific behaviours
  • Most direct observation data collection done by actual observers
  • Don’t require human data collector - audio/video can be used
  • Ordinal data/ purely factual description
  • Structured form of data collection
63
Q

Participant observation

A
  • Process enabling researchers to learn about the activities of the people under study in the natural setting through observing and participating in those activities
  • Qualitative, interactive and unstructured
  • Information collected is unique to the individual collecting the data
64
Q

Purpose of Interviews

A

Explore the views, experiences, beliefs and/or motivations of individuals on specific matters

65
Q

Purpose of Focus Groups

A
  • Group of respondents are interviewed together
  • Obtain data from purposely selected group of individuals rather than representative sample
66
Q

Pros of Interviews

A
  • Can get qualitative data
  • Preferable when researcher wants subjective perspective rather than generalisable understandings
67
Q

Cons of Interviews

A
  • Time consuming
  • Not the best for researching sensitive topics
68
Q

Pros of Focus Groups

A
  • Better at drawing people out of their shells - increased validity
  • Allows for discovery
  • Can build on each others comments for richer contextual data
69
Q

Cons of Focus Groups

A
  • Time consuming
  • Anonymity is hard
  • Less reliable
  • Participants can be influenced by other group members - conformity, social desirability, oppositional behaviours
  • Need skilled interviewer to prevent these problems
70
Q

Interviews and Focus Groups - Research questions

A
  • Exploratory questions
  • Theory testing/creation questions
  • Confirmatory research questions
71
Q

Type of data collected by Focus Groups and Interviews

A
  • Almost always qualitative
72
Q

Structured vs Unstructured questions

A

Structured:
- Quantitative method
- Closed-ended questions
- List of questions
- Everyone asked same questions in the same order
- Easy to replicate
- Easy to test for reliability
- Quick to conduct
- Not flexible
Unstructured:
- Do not use any set questions
- Guided discussion
- Most useful for qualitative research
- Rarely provide valid basis for generalisation
- More flexible
- Increased validity - can probe for deeper understanding
- Time consuming to conduct and analyse the data
- Employing and training interviewers is expensive
Semi-Structured:
- Set questions but can investigate answers more
- Gets qualitative and quantitative data
- Can explore around answers
- Gathers useful info but respondents can answer more on their own terms
- More flexible
- More time-consuming

73
Q

Types of Focus Groups

A
  • Dual moderator - Two moderators
  • Two-way - Two seperate groups having discussions at the same time - second group listens to the firs tbefore having teh discussion
  • Mini- 4-5 participants instead of 6-10
  • Client-involvement - clients ask for focus group and invite those who ask
  • Participant-moderated- one or more participants are moderators
  • Online
74
Q

Purpose of experiments

A

Allows researchers to look at cause-and-effect relationship
Used when:
- There is time priority in a causal relationship
- There is consistency in a causal relationship
- The magnitude of the correlation is great

75
Q

Pros of experiments

A
  • Allows for reproducibility
  • Generalisation is easier
  • Can take bias into account using statistics
76
Q

Cons of experiments

A
  • Equipment might be more expensive
  • Highly prone to human error
  • Errors can reduce validity
  • Eliminating real-life variables can result in inaccurate conclusions
  • Time-consuming process
  • Researchers can control variables to suit personal preferences
  • Results are not descriptive
77
Q

Experiments - Research questions

A

Correlational questions

78
Q

Type of data collected by experiments

A

Quantitative

79
Q

True experiment

A

Researcher manipulates one variable and controls the rest of the variables

80
Q

Ad hoc analysis

A

Hypothesis invented after testing is done to try and explain contrary evidence

81
Q

Independent variable

A

variable manipulated

82
Q

Dependent variable

A

variable measured

83
Q

Control variables

A

not changed

84
Q

Purpose of Secondary data analysis

A
  • Take data from previous research and examine it for new question
  • Look for datasets that other people have created
85
Q

Pros of secondary data analysis

A
  • Discover new things from old data
  • Can use data that you wouldn’t have the resources to gather
  • Access to historical data
  • Ease of Access
  • Inexpensive
  • Time-saving
86
Q

Cons of secondary data analysis

A
  • May be issues with the data e.g bias
  • Might twist yourself to fit the data you’ve got
  • If you don’t know how the data is collected - don’t know the validity
  • Because data is hugely heterogeneous in many cases - have to make decisions to remove, ignore or add sections - can lead to confirmation bias
  • Many critical decisions in processing the data
  • Irrelevant Data - have to find the relevant data from the irrelevant data
87
Q

Secondary data analysis - research questions

A
  • Often explorational
  • Every question can be asked
88
Q

What can go wrong in data cleaning

A
  • Because data is hugely heterogeneous in many cases - have to make decisions to remove, ignore or add sections - can lead to confirmation bias
  • Need to know a lot about the data to prove that any changes in adding or ignoring have valid assumptions and rationale
89
Q

How can you validate secondary data analysis

A

To validate secondary data, find the:
- Purpose for which the material was collected/created
- Specific methods used to collect it
- Population studied and validity of the sample
- Ccredibility of the collector
- Limits
- Historic and/or political circumstances
- And consider how the data is coded/categorised
- Consider whether data must be adapted/adjusted

90
Q

Quantitative data - Research questions

A
  • Correlational
  • Causation
  • The how questions
91
Q

Qualitative data - Research questions

A
  • The why questions
92
Q

Mixed approach

A
  • Mix of qualitative and quantitative data
  • Usually use different methods to collect them
  • When you have a small sample size - want to do quantitative but don’t have enough people
  • Qualitative used to underpin quantitative
  • For exploration
93
Q

Quantitative data

A
  • Expressed in numbers and graphs
  • Used to test or confirm theories and assumptions
  • Can be used to establish generalisable facts about a topic
  • Methods include experiments, observations recorded as numbers and surveys with closed-ended questions
  • At risk for research biases icl. Information bias, omitted variable bias, sampling bias or selection bias
94
Q

Qualitative research

A
  • Expressed in words
  • Used to understand concepts through experiences
  • Gather in-depth insights on topics
  • Methods include interviews with open-ended questions, observations described in words, focus groups, Ethnographies and literature reviews
  • At risk of research biases incl. Hawthorne effect, observer bias, recall bias and social desirability bias
95
Q

Qualitative data limitations

A
  • Don’t draw samples from large-scale data sets due to time and costs involved
  • Problem of adequate validity or reliability is major concern due to subjective nature
  • Contexts, situations, events, conditions and interactions cannot be replicated
  • Generalisations can’t be made to a wider context than the one studied
  • Lengthy time required
  • Expert knowledge of an area is required to interpret the data
96
Q

Qualitative data advantages

A
  • Researcher gains an insider’s view of the field - can find issues that are often missed
  • Can be important in suggesting possible relationships, causes, effects and dynamic processes
  • Allows for ambiguities/contradictions in the data which reflect social reality
  • Uses a descriptive, narrative style
97
Q

Quantitative data limitations

A
  • Do not take place in natural setting
  • Do not allow participants to explain their choices
  • Poor knowledge of the application of the statistical analysis may negatively affect analysis and subsequent interpretation
  • Large sample sizes needed for more accurate analysis
  • Confirmation bias - researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on theory/hypothesis generation
98
Q

Quantitative data advantages

A
  • Scientific objectivity - data can be interpreted with statistical analysis
  • Useful for testing and validating already constructed theories
  • Data analysis and collection can be performed quickly
  • Data can be checked by others and replicated
  • Hypotheses can be tested
99
Q

Hypothesis testing

A

Collect data to determine if a claim about the population is true

100
Q

Hypothesis

A

-Testable statement that you want to accept or reject
- You never “prove” a hypothesis

101
Q

Validity of a hypothesis

A
  • Needs to be testable
  • Need to be able to prove it false
  • Be specific - don’t use ambiguous words e.g “athlete” or “better”
  • Don’t be too specific - overlap with methodology
    “If (one variable) ‘is related to’/’is affected by’/’causes’ (other variables) then (comment on relationship)”
102
Q

Alternative hypothesis tails

A
  • Two tailed test - doesn’t state direction
  • One-tailed test - states direction
103
Q

Type 1 error

A

Null Hypothesis is true but is rejected - false positive

104
Q

Type 2 error

A

Null hypothesis is false but is not rejected - false negative

105
Q

P-value

A
  • Compare p-value to a threshold value (significance level/alpha) to reject null hypothesis
  • P > alpha - fail to reject
  • P <=alpha - reject
106
Q

Critical value

A
  • Some tests return a list of critical values and their associated significance levels and a test statistic
  • Test statistic < critical value - fail to reject
  • Test statistic >= critical value - reject
107
Q

Types of data

A
  • Observational data
  • Experimental data
  • Simulation data
  • Dervived/Compiled data
108
Q

Observational data

A

Open surveys, observational studies, focus groups etc. …

109
Q

Experimental data

A

Collected via experimentation - easier to reproduce

110
Q

Simulation data

A

Scenario simulation allows for generation of predictive data

111
Q

Derived/Compiled data

A

Utilises existing data to generate new data - secondary data analysis

112
Q

Descriptive analysis

A
  • Basic analysis of the data giving a general overview
  • Only describes what the data is or what it shows
  • Allows for simple analyses
  • No extrapolation of inference
  • Measures of frequency
  • Measures of central tendency
  • Measures of dispersion or variation
  • Measures of position
113
Q

Measures of frequency

A

Count, percent, frequency

114
Q

Measures of central tendency

A
  • Mean, median, mode
  • Used to show an average or most commonly indicated response
115
Q

Measures of dispersion or variation

A
  • Range, variance, standard deviation
  • Variance/standard deviation - difference between observed score and mean
  • When you want to show how spread out the data is
116
Q

Measures of position

A
  • Percentile ranks, Quartile ranks
  • Describes how scores fall in relation to one another
  • Relies on standardised scores
  • Use when you need to compare scores to a normalised score
117
Q

Exploratory Analysis

A
  • Examine or explore data and find relationships between variables which were previously unknown
  • Does not describe the cause
  • Useful for discovering new connections
118
Q

Inferential Analysis

A
  • Use statistics to look beyond the collected data to identify new conclusions
  • Using a small sample of data to infer about a larger population
  • Based on laws of probability and confidence intervals
  • Central Limit Theorem
  • T-test
119
Q

Central Limit Theorem

A
  • Distribution sample means approximates a normal distribution and the sample size gets larger, regardless of populations distribution
  • Average of sample means and standard deviations will equal the population mean and standard deviation
120
Q

T-test

A
  • Tells how likely the difference between two groups is a real difference rather than sampling artefact
  • ‘P-value’ - probability that the data collected occurs by random chance
121
Q

Predictive Analysis

A
  • Using historical or current data to find patterns to make predictions about the future
  • Simulations can both generate data for prediction as well as using existing data
  • Accuracy of predictions depends on input variables/data
  • Accuracy depends on types of models - linear model generally works well
  • Using variable to predict another doesn’t denote a causal relationships
122
Q

Causal Analysis

A
  • Step beyond inferential analysis
  • Examines the cause and effect relationships between variables focused on finding the cause of a correlation
  • Generally large, complex and expensive studies
  • Four important components
    1. Correlation
    2. Temporal sequence - cause must occur before effect
    3. Concomitant variation - variation must be systematic between the two variables
    4. Nonspurious association - Any covariation between a cause and an effect must be true and not due to another variable
123
Q

Mechanistic Analysis

A
  • Similar to predictive but instead of general data driven predictions - utilise highly specific changes in variables that lead to changes in linked variables
  • Generally used in high precision disciplines e.g engineering and physics
  • Often used in high precision computer models
124
Q

5 characteristics of quality data

A
  1. Validity - degree to which data conforms to defined business rules or constraints
  2. Accuracy - ensure data is close to true values
    - E.g put in positive and negative questions in questionnaire - person should answer 1 to the negative if they answered 5 to the positive
  3. Completeness - degree to which all required data is known
  4. Consistency - ensure data is consistent within the same dataset/ across multiple datasets
  5. Uniformity - degree to which data is specified using the same unit of measure
125
Q

Qualitative data scales

A
  • Nominal (categories, no ordering) e.g male, female
  • Ordinal (categories, ordered) e.g small, medium, large
126
Q

Quantitative data scales

A
  • Discrete (countable, integers)
  • Continuous (measurable) e.g Age, temperature - can subdivide it
127
Q

Paired or match variables

A

Two variables in the individuals of a population that are linked together in order to determine the correlation

128
Q

Choice of statistical test from paired or matched observation

A
  • Nominal variable - McNemar’s Test
  • Ordinal (Ordered categories) - Wilcoxon
  • Quantitative (Discrete or Non-Normal) - Wilcoxon
  • Quantitative (Normal) - Paired t test
129
Q

Parametric test

A
  • Make assumptions about the parameters of the population distribution from which the sample is drawn
  • Often that the population data are normally distributed
  • Can only apply parametric tests (e.g T-test) if you have a sample big enough (in regards to population) to assume that the central limit theorem applies
130
Q

Non parametric tests

A
  • “distribution-free”
  • Can be used for non-Normal variables
131
Q

Reducing Type 1 and Type 2 errors

A
  • Reducing the chances of a type I error increases the chances of a type II error and vice versa
  • In science it is better to miss something than draw incorrect conclusions - reduce type I errors
  • Bonferroni correction - Reduces instances of type I errors but increases type II errors
  • Types II error reduction not as easy as Bonferroni:
  • Increase sample size
  • Change alternative value in the alternate hypothesis
132
Q

ANOVA (analysis of variance)

A
  • test looking at 3 or more groups
  • reduces type I errors
  • Used for comparing the means of three or more groups or variables
133
Q

Monte Carlo simulation

A
  • In uncertain scenario - allows for exploration of the problem/solution space
  • One of the most popular techniques for calculating effect of unpredictable variables on a specific output variable
  • Ideal for risk analysis
134
Q

Factor analysis

A
  • Large well-structured questionnaire
  • Trying to address multiple things
  • Many questions may investigate the same ‘factor’
  • Method allows for grouping variables into set of underlying factors
  • Confirmatory factor analysis - know what the factors are and have set them
  • Exploratory Factor analysis - assume there are factors but not setting them
135
Q

Cohort analysis

A
  • Form of behavioural analytics
  • Ideal for examining user behaviour
  • Allow for exploration between cohorts
  • Group of people who share common characteristics over a given time frame
136
Q

Cluster Analysis

A
  • Works by organising items into groups or clusters on how associated they are
  • K-means clustering - n data points in k clusters
  • Setting different number of clusters gives different results
  • Works at a data-set level - every point is assessed relative to the others - data must be as complete as possible
  • Intracluster distance - distance between clusters
  • Intercluster distance - distance within clusters
137
Q

Time series analysis

A
  • Useful to see how variable changes over time
  • Forecasting via trends
138
Q

Sentiment Analysis

A
  • Natural language processing technique to determine whether data is positive, negative or neutral
  • Not terribly refined - can’t figure out sarcasm
139
Q

Basic vs applied research

A

Research for curiosity vs research to answer a specific question

140
Q
A

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