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FNH 398 > Midterm Content > Flashcards

Midterm Content Flashcards

1
Q

Ways of knowing [5]

A
  • tradition (passed through generations; often immoral/incorrect)
  • authority (professional tells you; not always trustworthy)
  • trial-and-error (may not always arrive at best solution)
  • logical reasoning (employs power of deduction to reach a conclusion; not always reliable)
  • scientific research (via scientific method)
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2
Q

Scientific method [4]

A
  • systematic (series of steps → identify problem, design study, collect/analyze data, interpret)
  • empirical (observable/measurable)
  • controlled (not all studies can be controlled, however)
  • critical examination (reviewed/repeated by peers)
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3
Q

What is evidence-based medicine (EBM)? [4]

What does EBM take into consideration? [3]

What are barriers to EBM? [2]

A

EBM used to solve clinical problems.

It uses (1) conscientious, (2) explicity, and (3) judicious use of (4) current best evidence.

It considers (1) available resources, (2) client preferences, and (3) practitioners ability.

Barriers to EBM include (1) lack of time, and (2) lack of skill in critically reviewing/applying research.

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4
Q

Describe challenges in nutrition research. [5]

A
  1. optimal intakes differ across body systems
  2. difficult to accurately measure nutrient intakes
  3. length of exposure vs. timing of impact
  4. the nutrient effect may be small
  5. challenge of having no ‘no-intake’ group
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5
Q

Describe the procedure for EBM. [5]

A
  1. Formulate a question (PICO)
    • Patient/problem of interest
    • Intervention of interest
    • Control/alternative treatment
    • Outcome of interest
  2. Search for answers (3 S’s)
    • Systematic (comprehensive resources)
    • Syntheses (systematic reviews)
    • Studies (original research)
  3. Appraise the evidence
    • Appropriate study design?
    • What does the data show?
    • What does the data mean?
  4. Apply results
  5. Assess outcome
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6
Q

What are the stages in the research process? [8]

A
  1. Idea
  2. Literature review
  3. Define research question and hypothesis.
    • Question is important, answerable, feasible, clear
    • Hypothesis is testable NOT provable (either accept or reject it)
  4. Planning and study design
    • Choose study design, define population, sampling method, what/how to measure variable, ethics approval
  5. Data collection
  6. Analysis → statistics
  7. Interpretation
  8. Dissemination
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7
Q

What type of research is hypothesis generating?

A

Descriptive research (of phenomena/populations), which may not have a hypothesis.

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8
Q

What is research?

A

Systematic investigation utilizing the scientific method.

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9
Q

What is a theory? [3]

A

A theory (1) organizes information, (2) helps explain past events, and (3) predict future ones.

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10
Q

Describe features of good quality research. [6]

A
  • relevant, answerable
    • carries meaning
  • theory-driven
    • builds upon current understanding
  • reproducible
  • generalizable
    • should apply to outside situations (outside study setting)
  • ongoing
    • generates new questions
  • without bias/political motivations
    • research is not done in a vacuum however
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11
Q

Compare basic, applied/clinical, and translational research.

A
  • Basic → acquires new knowledge, helps understanding of phenomena
    • Includes cell and animal research
    • ‘Bench’
  • Applied/clinical → directed towards solving a specific problem
    • Done in humans (clinical trials)
    • ‘Bed’
  • Translational → ‘bench to bedside’
    • Starts with basic research, is then applied to a problem.
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12
Q

Describe types of clinical research. [7]

A
  • Observational → cannot test cause and effect
    • Descriptive
      • Qualitative (examines behaviour in natural social/cultural/political contexts; information from open-ended answers)
      • Quantitative (descriptive statistics)
      • Case study/series (description of one/several patients)
    • Correlational/exploratory: looks for relationships between variables
      • Cross-sectional (E+O)
      • Case-control (O→E)
      • Cohort (E→O)
  • True experimental research → investigator controls variable of interest; tries to discover causal relationships
    • The Gold Standard → double-blind, placebo-controlled, randomized controlled trial
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13
Q

What is ‘true’ experimental research? [4]

A
  • Treatment variable (exposure) is controlled by researcher (i.e., not associated with any confounders)
  • Control for comparison
  • Participants are assigned to groups randomly
  • The Gold Standard = double-blind placebo-controlled randomized clinical trial
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14
Q

What is double blind?

What is double-double blind?

Does triple blind exist?

A

Double-blind: neither researchers nor participants know who is receiving a particular treatment (helps to prevent bias)

Double-double blind: two treatment groups and two control groups; neither researchers nor participants know who is receiving a particular treatment (even better at preventing bias)

Yes, triple blind exists, I’m not sure what it is though…

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15
Q

What is outcomes research?

What are the four types?

A

Outcomes research is conducted to measure the effects of services/interventions.

  1. clinical (length of stay, morbidity, mortality)
  2. functional (activities, mental/emotional health)
  3. patient satisfaction (expectations, self-assessed health status)
  4. economic (costs)
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16
Q

How is choice of research method restricted? [3]

A

Depends on the question and how much is already known, as well as available resources (time, staff, money, tech, etc.)

Further research is restricted by ethical considerations.

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17
Q

How is research disseminated? [5]

A
  1. Publication in peer-reviewed journals
  2. Meetings, conferences, proceedings
  3. Books
  4. Guidelines, policies
  5. Media
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18
Q

Describe the peer-review process.

A

Provides more confidence in scientific journals because other experts review/assess quality, suggest revisions, and decide whether to accept or reject.

Also note: there is a heirarchy of journals - American Journal of Clinical Nutrition is highly rated)

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19
Q

What types of research articles are there? [3]

A
  1. Review (2º): tries to reach general conclusions about current status of literature; summary of past and current knowledge on the topic
    • Narrative: no defined/systematic method in choosing papers to include; important to check data sources for author bias
    • Systematic: uses clearly defined criteria to reduce bias; all articles meeting criteria are included; conclusions are based on all included articles; great source
  2. Original research (1º): conducted by author; describes study objective, methods, and findings in detail; usually a single study
  3. Commentaries, editorials, viewpoints: express professional opinions/interpretations
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20
Q

What are the goals of review papers? [4]

A
  1. summarize the state of the topic
  2. clarify unresolved issues
  3. suggest new hypotheses
  4. direct future research
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21
Q

What are the main components of a research paper? [7]

A
  1. Title: informative; describes findings or objectives, target population, and study design
  2. Abstract: summary of article; provides brief rationale, methods, main findings, and conclusion; can be subjective - must read whole article!
  3. Introduction: summary; provides rationale and background, clearly states thesis; only pertinent references included; does not include methods/data/results/conclusions; do not cite the introduction!
  4. Materials/Methods: identifies sufficient detail to allow reproducible results; data collection method; ethics considerations; subject sampling method etc; spend the most time here when critiquing study design.
  5. Results: data in text, tables and figures; does not explain
  6. Discussion: explains importance or novelty of findings in context of existing literature; future research suggested; synthesis and author opinion; subjective/possible bias; find limitations of study here!
  7. References and Acknowledgements
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22
Q

How does one paraphrase a research paper? [3]

A
  1. What did the study do?
    • methods (population, intervention, outcome of interest)
  2. What did the study find?
    • results (main findings including numbers where indicated)
  3. What does it mean? / Why does it matter?
    • discussion (in context of the report; what are your own interpretations?)
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23
Q

Describe characteristics of a good research paper. [5]

A
  • clear, proper use of language
  • current, accurate literature cited
  • sufficient detail provided for reproducibility
  • no bias
  • discussion highlights importance and limitations
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24
Q

What are the Bradford Hill Causal Criteria? [5] + [4 bonus]

What are concerns of the first 5?

A
  1. Consistency of association: relationship observed repeatedly
    • concerns: consistent errors across studies; inability to find consistent relationships due to methodological differences across studies (different tools used to collect information, different populations studied)
  2. Strength of association: effect size
    • concerns: often only weak associations in nutrition studies
  3. Dose response: statistically significant linear trend
    • concerns: threshold effects → nutrient/outcome relationships are not always linear; misclassification → food records vs. food frequency questionnaire
  4. Biological plausibility: theoretical explanation/mechanism
    • concerns: unknown mechanism for effects of nutrients on diseases; foods vs. nutrients (complex systems); what part of the food is responsible for the effect?
  5. Temporality: exposure precedes the outcome
    • concerns: did diet cause disease? Or did disease cause a change in the diet?

Bonus:

  • Specificity: exposure causes a specific outcome
  • Analogy: comparison to a similar biological system can be made
  • Coherence: fits in with existing knowledge
  • Experiment: true experimental study finds the association
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25
Q

What are some search tips when searching a database? [7]

A
  • Use PICO framework
  • Search one concept at a time
  • Appropriate use of MeSH terms
  • Keyword strategies
  • Combining with AND & OR appropriately
  • Applying limits at the end of the search
  • Reverse engineering
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26
Q

What is a background question?

What is a foreground question?

A
  • BG: general knowledge about a topic; can usually be answered by a 2º or 3º literature source (e.g., review article, clinical guidelines, textbook, etc.
  • FG: patient-specific; focused question; PICO is used to formulate a clinical question
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27
Q

What is MEDLINE?

A

Medline is the premier biomedical database with over 26 million journal articles in the life science.

There are several different interfaces to the database (e.g., PubMed, Ovid)

Searching a database involves both a subject heading (MeSH) strategy as well as a keyword strategy.

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28
Q

How do you combine concepts with boolean operators?

A
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29
Q

What is MeSH? [4]

A

Medical subject heading

Controlled vocabulary indexing the article’s content; subject specialists index the article by tagging it with controlled vocabulary from a standardized list.

Organized in a hierarchical structure which allows for searching at various levels of specificity.

Articles are indexed to the most specific MeSH (e.g., articles about breakfast will not necessarily be indexed as well with meals)

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30
Q

Why use keywords?

A
  • It takes a few months for articles to be indexed with MeSH
  • Sometimes an appropriate MeSH is not available
  • The concept is new to literature or is only recently added as a MeSH (e.g., Vegan / (2016))
  • Indexes errors/omissions
  • Completeness of your search
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31
Q

Describe 4 keyword strategies.

A
  1. Synonyms to describe concept
  2. Join synonyms with Boolean operator: OR (e.g., fat OR lipid)
  3. Truncation: * (e.g., diet* = diets, dietary, diet)
  4. Adjacency operator: adj (e.g., restricted adj2 fat* = the word restricted is within 2 words of the word fat)
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32
Q

What are explanations for associations? [5]

A
  • Bias or systematic (spurious)
  • Effect-cause (real)
  • Confounding (real)
  • Chance/random error (spurious)
  • Cause-effect (real)
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33
Q

What is confounding?

A

A confounder is a 3rd factor that is:

  • associated with the exposure and the outcome, and
  • makes the two appear related when they may not be.
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34
Q

How can confounding be minimized? [4]

A
  • Study design
    • Specification: design inclusion criteria that specify a value of a potential confounder and exclude those with that value
    • Randomization: aim to evenly distribute confounders among study
  • During analysis
    • Stratification: group and analayze participants based on their level of confounding variable
    • Statistical adjustment: techniques used to adjust for confounders using available software

Caveat: data on confounding factor must be collected during the study to do stratification analysis/statistical adjustment (importance of good study design).

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35
Q

What is effect modification?

A

A 3rd variable modifies (does not explain) the effect.

The strength of the apparent association varies over different categories of a third variable (e.g., age, gender, genotype)

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36
Q

Discern between confounding and effect modification.

A
  • Confounder → explains association
  • Effect modifier → influences association, does not explain it.
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37
Q

Define populations and samples.

A

Population: entire group of interest (generalized results apply to this population)

Sample: portion of population included in the study (findings from this sample should be generalizable to the population)

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38
Q

What are the main goals of sampling for research design. [3]

A
  1. To choose a sample representative of the population.
  2. So results can be generalized to the population
  3. To reduce the sampling error.
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39
Q

What is sampling bias?

A

Selected individuals over-/under-represent certain population attributes related to the study.

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40
Q

What is sampling error?

A

Sampling error = x̄ - μ

More of a theoretical concept since we can’t know the true mean of the population. However, it can be estimated.

Sampling error is the difference between the sample and population means.

Reducing this error is the main purpose of sampling.

As the sample size increases, sampling error generally decreases (i.e., larger sample sizes are more representative of the population)

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41
Q

What is the problem with a sample size that is too small or too large?

A

Too small → significant differences may not be found; sample is not representative of population.

Too large → very expensive

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42
Q

Describe an adequate sample size for quantitative vs qualitative research.

A

Quantitative sample size → must be large enough to draw inferences from; the larger, the more representative (unless using poor sampling technique); note, a small sample the is representative is better than a large sample that is unrepresentative.

Qualitative sample size → small numbers provide in-depth information; e.g., proceed until saturation (continue until you’re not hearing anything new)

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43
Q

What are the two steps of sample selection?

A
  1. Define the target population (i.e., who you want the findings to generalize to); define the sampling frame (i.e., accessible population from which sampling is drawn); set inclusion/exclusion criteria
  2. Recruit/select subjects via probability or non-probability sampling.
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44
Q

What is the difference between probability and non-probability sampling?

A

Probability → random selection; reduces sampling bias; preferred

Non-probability → non-random selection

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45
Q

What are the four probability sampling techniques, and four non-probability sampling techniques?

A

Probability → Simple random, stratified random, cluster, systematic

Non-probability → convenience, purposive, quota, snowball

NOTE: No matter the method, the final sample = recruited subjects who consent to participate.

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46
Q

What is a simple random sample?

A

A simple random sample is a type of probability sampling where every individual within the sampling frame has an equal and independent chance of selection.

  1. Define population.
  2. List everyone in it.
  3. Choose randomly.
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47
Q

What is a stratified random sample and when is it used?

A

A stratified random sample is a probability sampling method used if people in the population differ systematically along some characteristic relating to the study factor. It may be disproportionate or proportionate.

  1. Sampling frame is divided into strata (e.g., age, sex, ethnicity)
  2. Samples are drawn randomly from each strata.
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48
Q

What is systematic sampling?

A

A type of probability sampling where researchers choose every kth name on a list (more simple than simple random sampling); will produce a random sample unless the subjects are listed in an order that could affect outcomes.

  1. Divide population by the size of the desired sample (e.g., 50 (population)/10(sample size) = 5)
  2. Select a random starting point.
  3. Select every 5th name as a sampling interval.
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49
Q

Differentiate between proportionate and disproportionate stratified random sampling.

A

Disproportionate → population proportions are not preserved; higher proportions selected from some groups compared to others; useful when it may be difficult to reach a sufficient sample size with some groups; data must be weighted during analysis.

Proportionate → population proportions are preserved

  1. Characteristics of interest are identified (e.g., gender)
  2. Individuals in population are listed separately according to classification
  3. Proportional representation of each classification is determined (or not)
  4. A random sample is selected that reflects the determined proportions OR randomly choose the same number from each group regardless of population proportions.
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50
Q

What is cluster sampling and when is it used?

A

Cluster sampling is a type of probability sampling used if natural groupings exist in the population (e.g., school districts, geographic boundaries). It divides the population into feasible samples.

Instead of randomly sampling individuals, entire units/groups are identified and a random subset (cluster) of them is selected, and then finally individuals from each cluster are chosen at random.

e.g., Of all schools in BC → 10 selected randomly → of selected schools → 10 students collected randomly

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51
Q

What is convenience sampling?

A

This is a non-random sampling technique is when researchers recruit whoever they can, or the most easily sampled population. This type of sampling has very weak representativeness, and may show bias due to ‘self-section’.

For example, standing at a mall or a grocery store and asking people to answer questions would be an example of a convenience sample. Or posting an advertisement and recruiting any respondent.

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52
Q

What is purposive sampling?

A

This is a type of non-probability sampling where researchers select subjects specifically for a reason → for example, if they know they need a very specific type of person, they will be sought out.

The sample will be selected by the researcher based on specific (subjective) criteria → more often used in qualitative research (rare in quantitative)

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53
Q

What is quota sampling?

A

This type of non-probability sampling is ‘equivalent’ to stratified random sampling. It is used when stratified random sampling is not possible.

Participants with the characteristic of interest are non-randomly selected until a quota is met.

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54
Q

What is snowball sampling?

A

This is a type of non-probability sampling where existing subjects recruit future subjects, which may be useful for studying ‘rare’ or hard-to-reach groups. This type of sampling may be biased by the social networks of the original subjects.

55
Q

Discern between convenience and purposive non-probability sampling.

A

Convenience → whoever is available based on objective inclusion/exclusion criteria (e.g., poster ad for students age 18 - 25)

Purposive → sample selected by researcher based on some subjective criteria; sample is at discretion of researcher (i.e., ‘hand-picked’ selection of info-rich sources); used almost exclusively in qualitative research

56
Q

What is conceptualization and operationalization?

A

Conceptualization → the process of specifying what we mean by a term; ‘concepts to observations’; helps translate the abstract theory/construct into specific variable(s) (i.e., operationalize).

Operationalize → the process of connecting concepts (i.e., what we want to observe) to observations (i.e., measurable). Operational definitions: How will the variable(s) be measured? What level of measurement will be used?

57
Q

What are the four levels of measurement?

A
  1. Nominal → categories with no ranks
  2. Ordinal → ranked order
  3. Interval → ranks with known intervals between them
  4. Ratio → has an absolute zero and meaningful ratios

(think: NOIR)

58
Q

Describe the nominal scale of measurement.

A

Assignment of labels; used for categorical variables.

The categories vary in quality but not amount (i.e., CANNOT say one is more/less than another)

e.g., Did you walk/cycle/transit/drive to UBC on Friday?

59
Q

Describe the ordinal scale of measurement.

A

Assignment of values along some underlying dimension; one observation is ranked above/below another; variables are ordered with no defined interval (i.e., CANNOT say the amount of one variable is more/less than another)

e.g., underweight/health weight/overweight

60
Q

Describe the interval scale of measurement.

A

Assignment of values with equal distances between points; one score differs from another on some measure that has equally appearing intervals.

The zero is ARBITRARY, so no meaningful ratios can be used (e.g., 1980-81 has the same interval as 2010-11 BUT 0 does not imply the beginning of time).

e.g., temperature (i.e., 0°C does not mean absence of temperature)

61
Q

Describe the ratio scale of measurement.

A

Values have ranks and equal distances between points (i.e., meaningful ratios can be computed); CAN say that values differ, by how much, and what this means due to an absolute zero point.

This is the highest level of measurement.

e.g., age, weight, height, caloric intake

62
Q

Give examples of all four levels of measurement for Vitamin D intake.

A

Nominal → sourced from the sun or from the diet

Ordinal → does not meet / meets/ exceeds DRI

Interval → set RDA as 0 and report scores as above or below (e.g., RDA 20, intake 15 → report -5)

Ratio → Vitamin D intake reported in mcg/day

63
Q

What is the importance of levels of measurement? [2]

A
  • How a variable is measured can determine the amount of information obtained (e.g., BMI vs. under/normal/overweight).
  • How a variable also determines the type of statistical tests that can be used.
64
Q

What are 5 challenges is measuring change?

A
  1. The level of original measurement matters.
  2. There may be a minimal detectable difference (i.e., can only detect differences greater than the measurement error of the instrument used)
  3. Starting points matter → floor/ceiling (i.e., already as high/low as it can get, outcome won’t differ) effects; regression to the mean)
  4. Variables change naturally over time (e.g., blood pressure)
  5. Tools of measurement must be both reliable and valid.
65
Q

What are reliability and validity? Why are they important?

A

Reliability → tool is consistent

Validity → tool measures ‘what it should’

These are important for knowing if we are measuring what we want to measure, and if we are doing so correctly.

66
Q

What is reliability?

A

Reproducibility of a measurement (i.e., the extent to which a measurement is consistent and free from error).

67
Q

Describe the two main categories of error.

A

Systematic → ‘predictable’ errors of measurement (e.g., consistent under-/over-estimation); major concern for VALIDITY of measure.

Random → due to chance; major concern for RELIABILITY of measure (i.e., there is no consistency)

68
Q

Describe four factors that influence error.

A
  1. Tester or rater → errors in taking measurements, recording behaviour or data entry
  2. Measurement instrument → equipment malfunction; uncalibrated equipment; unclear questionnaire
  3. Variability in characteristic being measured → transient states of participants (e.g., mood, health, fatigue-level, blood pressure)
  4. Situational factors → room temperature, lighting, crowing, etc.
69
Q

Describe four ways to reduce factors that influence error.

A
  1. Maintain consistent scoring procedures; ensure testers are trained uniformly
  2. Increase the number of items/observations; standardize instructions; eliminate unclear questions
  3. Standardize testing conditions
  4. Minimize the effects of external events
70
Q

What are four ways reliability may be evaluated?

A
  1. Inter-/intra-rater
  2. Test-retest
  3. Parallel forms
  4. Internal consistency
71
Q

Describe reliability evaluation by inter-/intra-rater(s).

A

Inter-rater → have two raters judge the same event/behaviour and assess agreement

Intra-rater → assess the stability of measures by the same person

72
Q

Describe reliability evaluation by test-retest reliability.

A

Give the test to the same people at two time points and see if the results are the same.

It is necessary to be aware of testing-effects (i.e., change just by being tested), and be sure that measured characteristic does not change over time.

73
Q

Describe reliability evaluation by parallel/alternate forms.

A

Give two different but equivalent forms of the test to the same group and compare the results.

e.g., survey by paper vs. online; survey in 1st and 2nd language of participant

74
Q

How may reliability be measured when using parallel/alternate forms to evaluate reliability?

A

The correlation coefficient, which indicates how scores on one test change relative to scores on a second test. +1 = perfect reliability | 0 = no reliability | -1 = inverse

Also the intraclass correlation coefficient may be used. The results from the two tests must be related (correlated) and agree.

75
Q

Describe reliability evaluation by internal consistency and two ways it may be measured.

A

Extent to which items in the tool measure the same characteristic (and nothing else); degree of consistency/correlation among items.

Internal consistency may be measured by:

  1. Split-half reliability → randomly divide items into 2 subsets and examine the consistency in total scores (e.g., results from 100 question questionnaire must agree when split into Q1-50 and Q51-100)
  2. Cronbach’s Alpha → conceptually, the average consistency across all possible split-half reliabilities; 0.7 is considered acceptable
76
Q

What are the four types of validity?

A
  1. Face
  2. Content
  3. Criterion
  4. Construct

(think: FCCC)

77
Q

Describe validity evaluation by face validity?

A

‘face-value’

Does the measuring instrument appear to test what it is supposed to?

Does it appear to be valid to those completing it? Answers may not be honest/accurate if not.

78
Q

Describe validity evaluation by content validity.

A

How well do the items represent all relevant items?

Ask an expert: Does this instrument measure everything it’s supposed to? Is anything missing? Is my question comprehensive?

79
Q

Describe validity evaluation based on criterion validity.

A

Ability of the tool to predict results obtained on a valid external criterion or reference standard; criterion/standard should be a valid indicator of variable of interest.

Two types:

  1. Concurrent (present) → e.g., FFQ validated with 24 hour recalls or diet records collected for the same time period
  2. Predictive (future) → e.g., using HbA1C to predict diabetes risk
80
Q

Describe validity evaluation by construct validity.

A

Construct validity cannot be directly measured due to abstract concepts like healthy eating and happiness.

The extent to which test results are related to underlying abstract concept (difficult to establish; often used in combination with other validity evaluation methods).

Strategies [2]:

  • Show that people with and without certain traits score differently (i.e., they can be differentiated).
    • DISCRIMINANT → two unrelated measures should not be correlated
  • Compare the measure with other related measures of similar or differing constructs.
    • CONVERGENT → two measures of a similar construct should be correlated
81
Q

Describe reliability and validity in the laboratory.

A

Reliability → ensure experiments can be repeated and give the same results; often run duplicate or triplicate analysis

Validity → positive control (a known amount of something you are trying to measure or detect is run with assay to ensure you can detect it and measure it accurately); negative control (ensures you only detect what you want and not something else)

82
Q

Define a parameter.

A

Parameter → measure of a population characteristic

83
Q

Define a statistic.

A

Statistic → measure of a sample characteristic

84
Q

Define descriptive statistics.

A

Summarizes the data and provides basic information about the distribution of scores in a sample using the mean, min, max, range, and standard deviation.

85
Q

Define inferential statistics.

A

Inferences from the sample to a larger population.

86
Q

What is an independent variable?

A

Treatments or conditions under control of the researcher; the researcher may not have ‘direct’ control, but might assign groups based on this feature.

87
Q

What is a dependent variable?

A

Outcomes of the research study; sensitive to changes in the independent variable

88
Q

What are numerical variables?

A

Numerical variables have measurable quantity; two types:

  1. Discrete → variable can take on a finite number of values (e.g., # of students in FNH398)
  2. Continuous → values can range along a continuum (e.g., height)

Level of measurement: ratio or interval

89
Q

What are categorical variables?

A

Categories/groups; measures of proportions (e.g., gender, ethnicity)

Level of measurement: ordinal, nominal

90
Q

What is descriptive statistics?

A

Includes figures/graphs, measures of central tendency and variability as pivotal first steps for visualizing and understanding data and their distribution.

Central tendency → mean (normal distributions), median (skewed distributions), mode (categorical variables)

Spread/variability → range (crude), standard deviation (normal distributions), percentiles and inter-quartile ranges (skewed distributions)

91
Q

Describe the normal curve. [3]

A
  • Symmetrical
  • Asymptotic tail
  • Bell shaped histogram
92
Q

Differentiate between mean, median, and mode.

A

Mean → arithmetic average; normal distributions

Median → midpoint of distribution; skewed distributions

Mode → most frequently occurring score; categorical data

93
Q

What is a normal distribution, positive skew, and negative skew in relation to mean and median?

A

Normal → mean = median

Positive skew → long right-hand tail; mean > median

Negative skew → long left-hand tail; mean < median

94
Q

What is variability in data?

A

The degree of spread/dispersion in a set of scores; central tendency doesn’t tell us anything about spread (i.e., two curves with the same mean may have differing degrees of variability)

95
Q

What are measures of variability? [5]

A
  1. Range = max - min; crude indication of spread; does not delineate shape of distribution or how much scores vary from the mean
  2. Standard deviation = average difference of scores from the mean; sensitive to outliers; not the same as standard error
  3. Standard error = uncertainty in measurement of the mean; portrays uncertainty around the mean; gives a sense of where the true population mean lies; measure of variation of the mean
  4. Percentiles = value below which a certain percent of the data lie (e.g., 25th percentile = 25% of the data lies below this number)
  5. Quartiles = divide the data into four equal parts; inter-quartile range = difference between 3rd and 1st quartiles; Q2 = median; Q1 = 25th percentile; Q2 = 50th percentile; Q3 = 75th percentile
96
Q

Describe a box plot.

A
97
Q

What is a Z-score?

A

The # of standard deviations a score is away from the mean.

98
Q

How is the normal distribution divided based on Z-scores?

A

99.7% of the data lies between -3 and 3 (i.e., 99.7% of the data lies within 3 standard deviations from the mean)

95% of the data lies between -2 and 2

68% of the data lies between -1 and 1

The following is also mirrored on the positive half of the distribution:

0 to -1 → 34%

  • 1 to -2 → 13.5%
  • 2 to -3 → 2.35%
  • 3 to -∞ → 0.15%
99
Q

What is a confidence interval?

A

An interval in which the true population is likely to exist.

e.g., For a 95% confidence interval implies that there is a 95% probability that the true population mean exists within this interval.

100
Q

When may an outlier be removed?

A

If it lies above the 95th percentile or below the 5th percentile.

101
Q

What is inferential statistics?

A

When we find a difference between group means in our sample we want to know:

Does this represent a real difference between target groups?

Is this likely just a chance/random finding in our sample?

Inferential statistics is used to determine the likelihood of the finding happening by chance.

102
Q

Discern between the null and the alternative hypotheses.

A

Null → states that there is no difference between groups (i.e., Ho: μ1 = μ2); always stated using population parameters; accepted as true in the absence of other information.

Alternative → states that there is a difference between groups or relationship between variables (i.e., HA: μ1 ≠ μ2)

103
Q

What is hypothesis testing?

A

Statistical tests to either:

Reject the null (statistically significant difference between groups) or,

Fail to reject the null (no statistical significance between groups)

104
Q

What is a Type I error?

A

‘False positive’

The null hypothesis is rejected BUT there is no real difference between groups.

α = probability of making a Type I error (set to 0.05 usually; 5% chance we reject the null when it is actually true

105
Q

What is a Type II error?

A

‘false negative’

The null hypothesis is NOT rejected but there IS a real difference between groups or association between variables.

β = probability of making a Type II error; usually set at 0.2 (20% chance of failing to reject the null when it is false)

106
Q

What is power?

A

The ability of a test to reject the null hypothesis (i.e., the likelihood of finding a difference if one exists)

power = 1 - β

For β = 0.2, power = 0.8; 80% power to detect a difference if one exists.

107
Q

List the steps in completing a test of statistical significance. [6]

A
  1. State the null and alternate hypothesis.
  2. Establish a significance level (i.e., α)
  3. Choose the most appropriate test.
  4. Collect the data.
  5. Analyze data using statistical test.
  6. Reject or fail to reject the the null
108
Q

How is a statistical test chosen?

A

Independent → dependent → statistical test

Categorical → Continuous → t-test (2 categories) or ANOVA (> 2 categories)

Categorical → Categorical → Chi-squared

Continuous → Continuous → Correlation

109
Q

Large differences between means are […] to occur by chance alone than small differences between means.

The greater the variability in distributions, the […] the difference between the means is due to chance.

A

Large differences between means are [less likely] to occur by chance alone than small differences between means.

The greater the variability in distributions, the [more likely] the difference between the means is due to chance.

110
Q

What three pieces of information are required for a t-test?

A
  1. Mean for each group
  2. Standard deviation for each group
  3. Sample size
111
Q

What is a P-value?

A

Probability of obtaining our result (by chance) if the null is assumed to be true; calculated by probability distribution

112
Q

What are specific probability distributions for each statistical test?

A

T-test → t-value compared to t-distribution

ANOVA → f-value compared to f-distribution

113
Q

How do we reject or fail to reject the null?

A

Based on α (i.e., level of significance)

P-value > α (fail to reject; probability that results are due to chance is greater than we are willing to accept)

P-value < α (reject; unlikely results are due to chance alone; likely represents a real difference; statistical significance)

114
Q

What is the difference between parametric and non-parametric statistical tests?

A

Parametric → representative of population; data approximately normally distributed; variances of groups being compared are approximately equal; interval or ratio scales of measurement

Non-parametric → assumptions of parametric tests are not met; any distribution including skewed

115
Q

Independent variable = categorical

Dependent variable = continuous

Relevant statistical test?

A

T-test → 2 group means

ANOVA → > 2 group means; only tells us if significant differences occur anywhere among groups, but not which specific groups differ → Post-hoc analysis used to see ‘which’ groups differ (i.e., Fischer’s least significance test, Tukey’s Honestly significant difference test)

116
Q

Describe the different types of T-tests. [4]

A
  1. Two-tailed → used for non-directional hypotheses; mean of one group may be larger or smaller than the mean of the second group
  2. One-tailed → stringent; harder to find statistical significance; less Type I error chance; used when direction of change is known (e.g., mean 1 > mean 2); used for directional hypotheses
  3. Independent means → used when two independent, unrelated groups are compared
  4. Dependent means → a.k.a. paired t-test; used when groups are related (e.g., difference in F+V intake on Monday and Saturday → same individuals both times)
117
Q

Why is ANOVA used for > 2 group means and not multiple T-tests?

A

more comparisons = greater chance of error in t-test

118
Q

What are three types of ANOVA?

A
  1. One-way → one independent variable (e.g., measuring BP across groups of sodium intake)
  2. Two-way → two independent variables (e.g., measuring BP across groups of sodium intake and also groups of magnesium intake)
  3. Repeated measures → e.g., measuring blood pressure in the same person after three doses of sodium
119
Q

How are two continuous variables statistically analyzed?

A

x-y scatterplot; correlation coefficient (for linear relationships)

Pearson correlation varies between -1 and 1. Direction of relationship indicated by +/-. Strength of relationship indicated by value.

If X… → If Y… → The correlation is…

⇡ → ⇡ → + (direct)

⇣ → ⇣ → + (direct)

⇡ → ⇣ → - (inverse)

⇣ → ⇡ → - (inverse)

120
Q

How is the correlation coefficient interpreted?

A
  1. 8 - 1.0 → very strong correlation
  2. 6 - 0.8 → strong correlation
  3. 4 - 0.6 → moderate correlation
  4. 2 - 0.4 → weak correlation
  5. 0 - 0.2 → no/very weak correlation
121
Q

What are three cautions about using correlation/regression?

A
  1. Cannot extrapolate outside the range of the data.
  2. Need a range of values to show a correlation.
  3. Correlation coefficient does not tell us if relationship is statistically significant (additional tests needed to determine P-value)
122
Q

Discern between correlation and regression.

A

Correlation → direction (+/-) and strength (strong/weak)

Regression → magnitude of change; how much does y vary when x varies?

123
Q

What is linear regression?

A

Line of ‘best fit’ (y = mx + b)

Can be used to predict a value for the dependent variable (y) for a given value of the independent variable (x).

124
Q

What is the coefficient of determination?

A

R2

The proportion of variability in a data set that is accounted for by the statistical model → ranges from 0 to 1.

If 0 → none of the variation in the outcome is explained; x has no impact on y

If 1 → the regression line ‘perfectly fits’ the data; all variation in outcome is explained

Example → R2 = 0.8 → 80% of the variation in the dependent variable (y) is explained by the independent variable (x).

125
Q

What is multiple linear regression?

A

Used to describe the association between several independent variables simultaneously, and a single dependent variable; predictor variables can be categorical (using dummy variables like 0 = yes, 1 = no) or continuous

126
Q

What is logistic regression?

A

Dependent/outcome variable is dichotomous (yes/no).

Independent variable may be continuous or categorical.

127
Q

When is linear (or multiple linear) regression used?

A

Independent/predictor variable → continuous OR categorical

Dependent/outcome variable → continuous

128
Q

When is logistic regression used?

A

Independent/predictor variable → continuous OR categorical

Dependent/outcome variable → categorical

129
Q

List 5 parametric statistical tests.

A
  1. Independent (unpaired) t-test
  2. Dependent (paired) t-test
  3. One-way/Two-way ANOVA
  4. Repeated measures ANOVA
  5. Pearson correlation
130
Q

List 5 non-parametric statistical tests.

A
  1. Mann-Whitney U-test
  2. Wilcoxon signed ranks
  3. Kruskal-Wallis
  4. Friedman
  5. Spearman rho
131
Q

What is a Chi-squared (X2) test?

A

Used when all variables are categorical.

Compares the frequency with which we would expect certain observations to occur with the frequency that it actually occurred.

132
Q

What is multivariate analysis?

A

Considers multiple variables; may be used to ‘adjust’ or ‘control’ for the influence of several factors.

133
Q

Discern between significance and meaning.

A

Statistical significance → the probability that the observed result occurred by chance is low.

Meaning → clinical significance; requires good judgement to determine whether results represent a meaningful difference.

FNH 398 (4 decks)

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