final exam Flashcards
T- Test
the difference between two means (e.g., experimental vs. control, men vs women). Independent variable is nominal and the dependent variable is Continuous.
t- test example
Two means regular discharge mothers and early discharge mothers
Chase tested the difference in the mean birth weights of infants whose mothers either had or had not participated in a special prenatal education pro
Paired t-test (dependent group tests)
If means of a single group of people measured before and after an intervention were being compare
ANOVA:
Tests the mean group differences between 3 or more groups (F-value)
ANOVA example
Hutchingson compared mean pre-op anxiety levels in three groups of patients with different types of cancer.
Repeated measured ANOVA (RM ANOVA)
when means being compared are means at different points in time.(eg mean blood pressure at 2,4 and 6h after surgery)
Chi-Square:
X2 Used to test hypotheses about differences in proportions as in a cross-tabulation. Hint: compares data presented as PERCENTAGES %
chi-square example
Tucker tested the difference in the proportion of smokers vs nonsmokers who had tried an illegal drug
Messi compared the percentage of patients who had a fall in two hospital units, one of which had implemented a new patient safety protocol.
Correlations (r-value)
To test the existence and strength of a relationship between two variables. Both variables are continuous.
correlations (r-value) example
Powell tested the significance of the relationship between scores on a functional ability test and a cognitive performance test in nursing home residents.
P value
a number, calculated from a statistical test, that describes how likely you are to have found a particular set of observations if the null hypothesis were true.
P values are used in hypothesis testing to help decide whether to reject the null hypothesis.
IV:
(“I” for influence, intervention, or exposure and “C” for comparison group).
The presumed cause of the dependent variable)
DV
(“O” for outcome that a researcher is trying to understand, explain, or predict)- can also be the outcome that is being measured by variables.
Presumed effect of an independent variable
IV & DV example
smoking (IV) → lung cancer (DV)
** EXAMPLE: In a study of how different doses (IV) of a drug affect the severity of symptoms, a researcher could compare the frequency and intensity of symptoms (DV) when different doses are administered
A study was done to find if different tire treads (IV) affect the braking distance (DV) of a car
Pico components
P: patient or population
I: influence, intervention,
C: comparison group
O: outcome
nested sample
research design in which levels of one factor (say, Factor B) are hierarchically subsumed under (or nested within) levels of another factor (say, Factor A)
Girardi’s Grocery Store wants to determine how a redesign of its store layout will impact its most loyal customers
Convenience (volunteer) sampling;
not preferred approach but economical, the weakest form of sampling due to sample bias
Recruit participants by placing a notice on a bulletin board or on the internet
Snowball: (network sampling):
sample might be restricted to a small network of acquaintances. Asking early informants to make referrals.
May be affected by whether the referring sample member trusted the researcher and truly wanted to cooperate.
Purposive sample
researchers deliberately choose the cases that will best contribute to the study.
Allie explored nightmares in hospitalized children and recruited both boys and girls from different socioeconomic and ethnic backgrounds
Randomized
The experimenter assigns participants to a control or experimental condition on a random basis.
Confounding variable
A variable that is extraneous to the research question and that confounds understanding of the relationship between the independent and dependent variables; confounding variables can be controlled in the research design or through statistical procedures.
Anonymity
the most secure means of protecting confidentiality occurs when the researcher cannot link participants to their data.
e.g. A questionnaire is sent to a group of nursing home residents and was returned without any identifying information, responses would be anonymous.