exam review

Question 1

name three different types of populations

Answer 1

target, sample, accessible

Question 2

Define target, accessible and sample pops

Answer 2

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.

Question 3

what are the different types of sampling

Answer 3

probabilistic and nonprobabilistic

Question 4

when is probabalistic sampling used

Answer 4

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

Question 5

when is nonprobabalistic sampling used

Answer 5

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

Question 6

subtypes of probabilistic sampling

Answer 6

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

Question 7

subtypes of nonprobablistic sampling

Answer 7

convenience, snowball, quota, reasoned chocie, theoretical

Question 8

define simple random sampling

Answer 8

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

Question 9

define systematic sampling

Answer 9

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

Question 10

define stratified sampling, name and define its two subtypes

Answer 10

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.

Question 11

define cluster sampling

Answer 11

Randomly selects heterogeneous groups (clusters) instead of individuals.

Question 12

define convenience sampling

Answer 12

Selection based on easy accessibility.

Question 13

define snowball sampling

Answer 13

Participants recruit others they know who meet study criteria.

Question 14

define quota sampling

Answer 14

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

Question 15

define reasoned choice

Answer 15

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

Question 16

define theoretical sampling

Answer 16

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

Question 17

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

Answer 17

the number of variables needed for statistical significance

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

Question 18

explain the factors that influence sample size in quantitative studies

Answer 18

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?

Question 19

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

Answer 19

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

Factors: scope, nature, data quality

Question 20

explain the factors that influence sample size in qualitative studies

Answer 20

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

Question 21

name the principles underlying measurement

Answer 21

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

Question 22

define measurement

Answer 22

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

Question 23

types of data collection instruments

Answer 23

questionnaires, structured observations, physiological measurements

Question 24

define each data collection instrument and possible advantages

Answer 24

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).

Question 25

Define and name the two subtypes of measurement error

Answer 25

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

Question 26

What causes random error vs systematic error?

Answer 26

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

Question 27

Define relibaility versus validity and how they differ

Answer 27

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

Question 28

name and define the constituents of relibaility

Answer 28

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.

Question 29

Name and define the constituents of validity

Answer 29

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

Question 30

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

Answer 30

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.

Question 31

name and define the subtypes of qualitative data analysis

Answer 31

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

Question 32

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

Answer 32

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.

Question 33

how do you differentiate between descriptive and inferential statistics?

Answer 33

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

Question 34

explain measurement tools involved in assessing descriptive statistics

Answer 34

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.)

Question 35

explain the measurement tools involved in assessing inferrential statistics

Answer 35

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

Question 36

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

Answer 36

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

Question 37

Define statistical and clinical significance

Answer 37

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

Question 38

How do you interpret results of statistical analyses?

Answer 38

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.

Question 39

What is epidemiology?

Answer 39

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

Question 40

What are the key components of epidemiology?

Answer 40

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

Question 41

What are prevalence and incidence? name and describe their subtypes

Answer 41

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. Period Prevalence: Measured over a specific period. - 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.

Question 42

How do you calculate prevalence? what about point prevalence?

Answer 42

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

Question 43

How do you calculate cumulative incidence?

Answer 43

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)

Question 44

How do you calculate the incidence rate?

Answer 44

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)

Question 45

Differentiate between ratios, proportions, and rates.

Answer 45

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.

Question 46

Define a systematic review vs. meta-analysis

Answer 46

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.

Question 47

What are the steps of a systematic review? explain them

Answer 47

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

Question 48

How do you interpret a forest plot?

Answer 48

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

Question 49

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

Answer 49

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.

Question 50

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

Answer 50

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.

Question 51

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

Answer 51

posters, scientific articles, presentations

Question 52

how do you interpret the p-value?

Answer 52

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

Question 53

What are the subtypes of qualitative studies?

Answer 53

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.

Question 54

what are the subtypes of quantitative studies?

Answer 54

Descriptive: Describe characteristics of a population or phenomenon. Analytical: Test hypotheses about relationships between exposures and outcomes.

Question 55

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

Answer 55

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.

Question 56

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

Answer 56

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.

Question 57

list the 3 measurements of risk

Answer 57

absolute risk, relative risk and odds ratio

Question 58

define and explain how to calculate absolute risk (AR)

Answer 58

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.

Question 59

define and explain how to calculate relative risk (RR)

Answer 59

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.

Question 60

define and explain how to calculate odds ratio (OR)

Answer 60

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)