1. Hawthorne effect
1.1. is a threat to external validity. is an effect on results caused by the subjects' knowing that they are participating in an experiment.
1.1.1. when the workers were aware that they were part of a "productivity"study the productivity improved, not b/c of the change in lighting that they introduced
2. Mean, median, mode
2.1. Mean=average(add them all up and then divide by the # of numbers.
2.2. Median=the middle #
2.3. Mode=# that is repeated the most
3. Standard deviation and standard error
3.1. SD=the measure of variability
3.2. SE=a measure of the statistical accuracy of an estimate, equal to the standard deviation of the theoretical distribution of a large population of such estimates.
4. Case-control study
4.1. study a disease by identifying those who already have it and those who do not and then comparing the 2 groups for factors that might have been responsible for the disease
4.1.1. ex: cervical cancer
5. N=the total # of participants
6. P value
6.1. probability value=the probability that the observed result could occur by chance if the null hypothesis is true.
7. type II error
7.1. failing to reject the null hypothesis when it is actually false (Beta)
8. range
8.1. difference between the highest and lowest scores
9. alpha level
9.1. by convention the alpha level is set at .05 or .01 and is reported w/o the zero in front of the decimal point. the alpha level is set by the investigator and can be some level other than the one indicated.
9.1.1. If p<a then the researcher rejects the null hypothesis.
9.1.2. If p>a, then the researcher retains (does not reject) the null hypothesis
9.1.3. If the researcher rejects the null hypothesis, then the researcher is saying the opposite of the null hypothesis is true. If the researcher retains the null hypothesis, then the researcher is stating that the null hypothesis is true
10. Scientific method (research process)
10.1. systematic observation, measurement, and experiment, and the formulation, testing, and modification of hypotheses.
10.2. identify question-->select study approach-->design study; collect data--> analyze data-->write & share report
11. Research healthcare
11.1. research/science
11.2. Evidence-Based Medicine
11.3. Clinical Decision Making
12. Needs assessment (community research)
12.1. systematically evaluate population needs (type & extent) to set priorities
13. Observational studies
13.1. Case controlled study--> Cohort Study
13.2. Retrospective or prospective
14. Evidence pyramid
14.1. The Evidence-Based Medicine Pyramid is simply a diagram that was created to help us understand how to weigh different levels of evidence in order to make health-related decisions. It helps us put the results of each study design into perspective, based on the relative strengths and weaknesses of each design
15. confounding
15.1. A confounding variable is an outside influence that changes the effect of a dependent and independent variable. This extraneous influence is used to influence the outcome of an experimental design. Simply, a confounding variable is an extra variable entered into the equation that was not accounted for
15.1.1. essentially a fake correlation b/w 2 variables due to effects of a 3rd variable (the confounder)
15.1.1.1. ex: relationship b/w bifocal use & nocturnal enuresis is confounded by age.
16. sampling
16.1. It is the process of choosing a representative sample from a target population and collecting data from that sample in order to understand something about the population as a whole.
17. correlation
17.1. association between two quantitative variables
18. Internal validity
18.1. unintended factors/conditions that can affect the results
18.1.1. ex: assessing the effects of 2 dietary regimens & does not take into account the subjects' levels of activity; then the internal validity of the study is threatened
19. External validity
19.1. concerned w/ factors that may affect the generalizability of the conclusions drawn from the study. These factors are referred to as threats to external validity.
19.1.1. threats related to the population used
19.1.2. threats related to the environment in which the study takes place(environmental threats)
20. Cross-sectional study
20.1. a database of "snapshots" of subjects at one period
20.1.1. ex: childhood bone maturation may be studied by describing bone densities in a group of children ages 1, 3, 6, & 9 years old
21. Cohort study
21.1. study those that have or do not have a particular risk factor and then examine the 2 groups over time to identify those who develop the disease or condition.
21.1.1. ex: HPV exposure
22. Statistics used primarily in 3 ways in med. literature
22.1. 1.used to describe & summarize group info.
22.2. 2. allow us to infer or generalize sample results to the larger population
22.3. 3. test for significant relationships or differences b/w groups or subjects
23. alpha or type 1 error
23.1. rejecting the null hypothesis when it is actually true
24. Qualitative studies
24.1. is a scientific method of observation to gather non-numerical data
25. Inferential statistics
25.1. allows you to make predictions (“inferences”) from that data
26. Descriptive statistics
26.1. the four trends of the sample are mean, mode, median and range. They are used to describe the sample & sometimes to demonstrate how the sample may reflect a population, if that population's measures of central tendencies (descriptive statistics) are known.
27. Research process
27.1. Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings.
28. Efficacy & Effectiveness
28.1. efficacy=ideal setting
28.2. effectiveness="real world"
29. Risk assessment (IRB)
29.1. consider threats/health issues that pose element of harm to participants
30. Experimental study
30.1. Meta-analysis; Randomized Controlled Trial
30.2. Follow research protocol
30.3. Meta-analysis; Randomized Controlled Trial
31. selection bias
31.1. the error introduced when the study population does not represent the target population
32. information bias
32.1. occurs during data collection. 3 main types
32.1.1. misclassification bias
32.1.1.1. it is originated when sensitivity and/or specificity of the procedure to detect exposure and/or effect is not perfect, that is, exposed/diseased subjects can be classified as non-exposed/non-diseased and vice versa.
32.1.2. ecological fallacy
32.1.2.1. An ecological fallacy (or ecological inference fallacy) is a formal fallacy in the interpretation of statistical data that occurs when inferences about the nature of individuals are deduced from inferences about the group to which those individuals belong.
32.1.2.1.1. MAKING CONCLUSIONS ABOUT INDIVIDUALS BASED ON GROUP DATA ANALYSIS
32.1.3. regression to the mean
32.1.3.1. regression toward (or to) the mean is the phenomenon that arises if a random variable is extreme on its first measurement but closer to the mean or average on its second measurement and if it is extreme on its second measurement but closer to the average on its first.