BEHAVIORAL SCIENCE

Epidemiology

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Prevalence is the total number of cases in a population at a given time; -?-incidence is the number of new cases in a population per unit time.
Sensitivity is the number of true positives divided by the number of -?-all people with the disease.
-?-specificity is the number of true negatives divided by the number of all people without the disease.
Positive -?-predictive value (PPV) is the number of true positives divided by the number of people who tested positive for the disease.
The higher the prevalence of a disease, the -?-higher the positive predictive value of the test.
Odds ratio (OR) approximates the relative risk if the prevalence of the disease is not too high; it is used for case-control (-?-retrospective) studies.
Relative risk is used for -?-cohort studies.
SEM -?-decreases as n increases.
Gaussian presents a -?-normaldistribution which is -?-bell-shaped.
A positive skew is asymmetric with an elongated tail on the -?-right side of a distribution.
-?-validity is whether a test truly measures what it purports to measure and its -?-reliability describes its dependability.
-?-ANOVA checks difference between the meaning of 3 or more groups; and -?-t-test checks the difference between the means of two groups; and, -?-χ2 checks difference between 2 or more percentages or proportions of categorical outcomes (not mean values).
A cohort study has its sample chosen based on the presence or absence of -?-risk factors and its subjects are followed over time for development of disease.
Clinical trials which compares the therapeutic benefit of 2 or more treatments is the highest-quality study when -?-randomized and double-blind.
Match the bias type with its definition:
-?-Sampling bias subjects are not representative
-?-Late-look bias information gathered at an inappropriate time
-?-Selection bias subjects choose group
-?-Recall bias knowledge of presence of disorder alters recall by subjects
Selection bias Recall bias Sampling bias Late-look bias
A -?-type I error (α) states that there IS an effect or difference when none exists (mistakenly accepts experimental hypothesis and rejects the null hypothesis) and a -?-type II error (β) states that there is IS NOT an effect or difference when one exists.
Primary (1°) disease prevention -?-prevents disease. (example-vaccination)
Secondary (2°) disease prevention refers to -?-early detection. (example-Pap smear)
Tertiary (3°) disease refers to -?-reducing disability from disease. (example-insulin for diabetes)
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