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exam review Flashcards

1
Q

name three different types of populations

A

target, sample, accessible

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2
Q

Define target, accessible and sample pops

A

The target population is the overall group you wish to study.
○
The accessible population is the group available to you, the researcher, within your study.
○
The sample population is the refined population selected for the study from the accessible population and should accurately represent the target population.

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3
Q

what are the different types of sampling

A

probabilistic and nonprobabilistic

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4
Q

when is probabalistic sampling used

A

Probabilistic sampling (used in quantitative studies with the aim to generalize results)

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5
Q

when is nonprobabalistic sampling used

A

Non-probabilistic sampling (used in qualitative studies with the aim to represent phenomena)

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6
Q

subtypes of probabilistic sampling

A

simple random, systematic, stratified (proportional and non proportional), cluster

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7
Q

subtypes of nonprobablistic sampling

A

convenience, snowball, quota, reasoned chocie, theoretical

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8
Q

define simple random sampling

A

Simple random sampling: Each element in a population has an equal chance of being selected.

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9
Q

define systematic sampling

A

Systematic sampling: Elements are selected at regular intervals from a randomly determined starting point.

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10
Q

define stratified sampling, name and define its two subtypes

A

Stratified sampling: Divides the population into subgroups (strata) with common characteristics and samples are selected from each strata.

subtypes:
proportional: The sample size for each strata reflects its proportion in the total population.
nonproportional: Over-represents certain strata.

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11
Q

define cluster sampling

A

Randomly selects heterogeneous groups (clusters) instead of individuals.

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12
Q

define convenience sampling

A

Selection based on easy accessibility.

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13
Q

define snowball sampling

A

Participants recruit others they know who meet study criteria.

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14
Q

define quota sampling

A

Selection based on pre-determined proportions of subgroups in the population.

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15
Q

define reasoned choice

A

Combination of quota and convenience sampling, considering availability and knowledge about the topic.

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16
Q

define theoretical sampling

A

Iterative selection based on emerging themes from data analysis in grounded theory.

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17
Q

what determines sample size in quantitative contexts? what factors influence it?

A

the number of variables needed for statistical significance

factors: statistical power, homogeneity vs. heterogeneity, magnitude of effect, error types, power level, purpose of study

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18
Q

explain the factors that influence sample size in quantitative studies

A

Statistical power: probability of correctly finding an effect
Homo/heterogeneity: population=uniform or diverse
Magnitude of effect: is the measured difference/relationship small or large
Error types: type one (rejecting null hypothesis when true) or two (accepting null hypothesis when false)
Power level: 80% in most studies
Purpose: is the goal exploring, describing or predicting relationships?

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19
Q

what determines sample size in qualitative contexts? what factors influence it?

A

Not predetermined, based on data saturation - the point at which no new themes are emerging from the data.

Factors: scope, nature, data quality

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20
Q

explain the factors that influence sample size in qualitative studies

A

scope: whether the research question is broad or narrow
nature: is this study complex or sensitive?
data quality: level of ease in collecting complete and rich data

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21
Q

name the principles underlying measurement

A

operationalization and levels of measurement (nominal, ordinal, interval, ratio)

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22
Q

define measurement

A

measurement involves assigning numbers or symbols to events, objects or characteristics according to rules.

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23
Q

types of data collection instruments

A

questionnaires, structured observations, physiological measurements

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24
Q

define each data collection instrument and possible advantages

A

Questionnaires: Written sets of questions used to collect information from respondents. Advantages: low cost, efficient, allows anonymity, no interviewer bias.
○
Structured observations: Collecting data by systematically watching and recording behavior or events.
○
Physiological measurements: Objective measurements of bodily functions (e.g., BMI, blood pressure).

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25
Q

Define and name the two subtypes of measurement error

A

Measurement Error: Discrepancy between observed scores and true scores. Subtypes: random and systematic

26
Q

What causes random error vs systematic error?

A

Random Error: Due to unpredictable factors, subjective influences, or instrument issues.
○
Systematic Error: Due to predictable and consistent factors.

27
Q

Define relibaility versus validity and how they differ

A

Reliability: Consistency of measurements.

Validity: Accuracy of measurements, whether the instrument measures what it intends to measure.

28
Q

name and define the constituents of relibaility

A

Temporal stability: Consistency over time.
■
Equivalence: Consistency between different versions of the instrument.
■
Inter-rater reliability: Consistency between different observers.
■
Internal consistency: Consistency of items within the instrument.

29
Q

Name and define the constituents of validity

A

Content Validity: Whether the instrument covers all aspects of the concept being measured.
■
Construct Validity: Whether the instrument accurately measures the underlying theoretical concept.
■
Face Validity: Whether the instrument appears to measure what it claims to measure

30
Q

How do you assess reliability and validity using statistical methods? List and explain the two methods

A

Correlation Coefficient: A statistical measure of the strength and direction of association between two variables.
○
Reliability Coefficient: A statistical measure of the consistency of scores from an instrument.

31
Q

name and define the subtypes of qualitative data analysis

A

Non-statistical: Focuses on interpretation, description, and exploration of themes and patterns.
○
Iterative: Involves a cyclical process of data collection, analysis, and interpretation.

32
Q

what are the key steps involved in qualitative data analysis? explain them

A

Data Cleaning: Review data, remove irrelevant information, and refine the research question.
○
Data Reduction: Organize and categorize data into meaningful units, identifying recurring themes and patterns.
○
Data Analysis/Interpretation: Coding data, developing and refining themes, and connecting them to the research question and theoretical framework.
○
Data Display: Presenting the findings in a clear and compelling way using text, tables, figures, or diagrams.

33
Q

how do you differentiate between descriptive and inferential statistics?

A

Descriptive Statistics: Used to summarize and describe data.

Inferential Statistics: Used to make inferences and generalizations about a population based on sample data.

34
Q

explain measurement tools involved in assessing descriptive statistics

A

Frequency Analysis: How often different values occur.
■
Central Tendency: Measures of the average or typical value. (mean, median, mode)
■
Dispersion: Measures of the spread or variability of data. (range: difference between the highest and lowest values.,

variance: average squared deviation from the mean.,

standard deviation: square root of the variance.)

35
Q

explain the measurement tools involved in assessing inferrential statistics

A

Hypothesis Testing: Evaluating whether there is enough evidence to reject a null hypothesis.
■
Relationships Among Variables: Exploring associations and correlations between variables.

36
Q

How do you select appropriate statistical tests based on variable types and the research question?

A

according to OUTCOME X predictor. consult the following.

DICHOTOMOUS x dichotomous: chi^2 or fisher-exact
DICHOTOMOUS x categorical: chi^2 or fisher-exact
DICHOTOMOUS x ordinal: wilcoxon
DICHOTOMOUS x continuous: t-test, wilcoxon, logistic regression

CATEGORICAL x dichotomous: chi^2
CATEGORICAL x categorical: chi^2
CATEGORICAL x ordinal: kruskal-wallis
CATEGORICAL x continuous: anova, class prediction

ORDINAL x dichotomous: wilcoxon
ORDINAL x categorical: kruskal wallis
ORDINAL x ordinal: spearman correlation
ORDINAL x continuous: anova, class prediction

CONTINUOUS x dichotomous: t-test, wilcoxon
CONTINUOUS x categorical: anova
CONTINUOUS x ordinal: anova
CONTINUOUS x continuous: correlation, linear regression

37
Q

Define statistical and clinical significance

A

Statistical Significance: The probability that observed results are not due to chance.
○
Clinical Significance: The practical or meaningful importance of the results.

38
Q

How do you interpret results of statistical analyses?

A

Hypothesis Testing: Determine whether the null hypothesis can be rejected.
○
Effect Size: Determine the magnitude of the effect or relationship.
○
Confidence Intervals: Provide a range of values within which the true population parameter is likely to lie.

39
Q

What is epidemiology?

A

The study of the distribution and determinants of disease frequency in human populations.

40
Q

What are the key components of epidemiology?

A

Distribution: How diseases are spread across different populations.
■
Determinants: Factors that influence the risk of disease (exposures).

41
Q

What are prevalence and incidence? name and describe their subtypes

A

Prevalence: The proportion of individuals in a population who have a disease at a specific time. Subtypes: point and period prevalence

Point Prevalence: Measured at a single point in time.

Incidence: The rate at which new cases of a disease occur in a population over a specific time period. Subtypes: cumulative incidence and incidence rate.

Cumulative Incidence: The proportion of individuals who develop the disease during a specific time period.

Incidence Rate: The number of new cases per unit of person-time at risk.

42
Q

How do you calculate prevalence? what about point prevalence?

A

number of existing cases/total population

this concept, but at a specific point in time (specified in explanation, not as units), often represented as a percentage

43
Q

How do you calculate cumulative incidence?

A

of new cases / # of the population at risk at the start of the time period

over a range of time but only analyzes cases that are active (time is specified in the explanation, not denominator)

44
Q

How do you calculate the incidence rate?

A

of new cases / total person-time of observation

at a specific point in time but time is shown as a unit of PT or person-time (population that is not immune and can still get the disease: people - those already known to be affected or vaccinated * the time they were observed for)

45
Q

Differentiate between ratios, proportions, and rates.

A

Ratio: A comparison of two numbers.
○
Proportion: A ratio where the numerator is a subset of the denominator.
○
Rate: A measure of frequency that includes a unit of time in the denominator.

46
Q

Define a systematic review vs. meta-analysis

A

Systematic Review: A comprehensive and rigorous review of existing research on a specific question, following pre-defined methods to minimize bias.
○
Meta-Analysis: A statistical technique that combines data from multiple studies to provide a quantitative summary of the overall effect.

47
Q

What are the steps of a systematic review? explain them

A
  1. define question: use PICOS (population, intervention, comparison, outcome, study design)
  2. Develop research strategy: identify keywords so you can systematically search databases
  3. screen and select studies: apply pre-defined inclusion/exclusion crtieria to identify studies that meet it
  4. extract data: collect from chosen studies
  5. assess the risk of bias: evaluate methological quality of studies
  6. analyze and synthesize data: summarize findings and assess strength of evidence
48
Q

How do you interpret a forest plot?

A

horizontal lines represent confidence intervals (wider the lines, the greater the uncertainty)

square in the middle represents point estimate (study’s effect size; larger squares = studies with larger effects on a meta-analysis)

vertical line represents the null effect; studies that cross this line have no statistical significance (odds ratio: line at 1, mean difference line: at 0)

diamond represents the overall confidence interval and effect size; if not touching the null effect line, pooled effect is statistically significant

I2 statistic: level of heterogeneity (<25% = low heterogeneity and more consistent studies, >50% = high heterogeneity and studies are less consistent) or p-value over 0.05

49
Q

What are the different types of bias that affect reviews and meta-analyses?

A

Publication Bias: The tendency to publish studies with positive findings more often than studies with negative findings.
○
Language Bias: The tendency to include studies published in certain languages.

50
Q

What components increase the clarity and effectiveness of a presentation? Explain

A

Use tables and figures: Present numerical data and illustrate trends.
○
Highlight key findings: Emphasize the most important results and their implications.
○
Consider the audience: Tailor the presentation to the specific audience.

51
Q

list examples of formats that can be used to disseminate research results

A

posters, scientific articles, presentations

52
Q

how do you interpret the p-value?

A

p-value represents the probability of obtaining observed results if the null hypothesis is true

statistical significance: p<0.05 so we reject the null

53
Q

What are the subtypes of qualitative studies?

A

Phenomenology: Explores lived experiences of individuals.
○
Ethnography: Studies the culture and social practices of a group.
○
Grounded Theory: Aims to develop theories based on data.
○
Case Study: In-depth investigation of a single case or small group.

54
Q

what are the subtypes of quantitative studies?

A

Descriptive: Describe characteristics of a population or phenomenon.

Analytical: Test hypotheses about relationships between exposures and outcomes.

55
Q

what further sections can descriptive quantitative studies be broken into? explain them

A

Case Report: Detailed description of a single case.
■
Case Series: Description of a group of individuals with similar characteristics.
■
Ecological Studies: Examine relationships between exposures and outcomes at a population level.
■
Cross-Sectional Studies: Collect data on exposures and outcomes at a single point in time.
■
Longitudinal Studies: Follow a group of individuals over time.

56
Q

what further sections can analytical quantitative studies be broken into? explain them

A

Case-Control Studies: Compare individuals with a disease (cases) to individuals without the disease (controls) to identify risk factors.
■
Cohort Studies: Follow a group of individuals with and without exposure over time to see if they develop the disease.
■
Randomized Control Trials (RCTs): Experimentally test the effectiveness of an intervention by randomly assigning participants to treatment and control groups.
■
Quasi-Experimental: Similar to RCTs, but participants are not randomly assigned to groups.

57
Q

list the 3 measurements of risk

A

absolute risk, relative risk and odds ratio

58
Q

define and explain how to calculate absolute risk (AR)

A

Absolute Risk: Probability of an event happening (e.g. developing a disease) in a specific group of people.
○
Formula: Number of instances of an event in a group / Total number of people in that group.

59
Q

define and explain how to calculate relative risk (RR)

A

Relative Risk: Ratio of the risk of an event in the exposed group to the risk of an event in the unexposed group.
○
Formula: Absolute risk in the exposed group / Absolute risk in the unexposed group.

60
Q

define and explain how to calculate odds ratio (OR)

A

Odds Ratio: Ratio of the odds of an event in the exposed group to the odds of an event in the unexposed group.
○
Formula: (Odds of event in exposed group) / (Odds of event in unexposed group)

Health Research (1 decks)

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