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113 Cards in this Set

  • Front
  • Back
Deductive Reasoning
acceptance of a general proposition, or premise, from which subswequent inferences can be drawn
Inductive Reasoning
invovles a type of reverse logic, developing generalizations from specific observations
Scientific Method (steps)
1. Unanswered question
2. Propose hypothesis
3. Conduct experiment to test hypothesis
4. Measure, analyze, interpret results
5. Conclude - support or disprove hypothesis
6. Report findings
Basic (type of research)
fundamental, used to develop theories
Applied (type of research)
functional, used to dictate practice
Experimental (type of research)
researcher manipulates or controls select variables of interest and observes effects
Non-Experimental (type of research)
researcher does not manipulate or control any variables of interest
Quasi-Experimental
less control, no randomization and/or comparison of groups (e.g. education research)
Sequential Clinical Trials
continuous analysis of data as they become available (e.g. testing of new drugs)
Descriptive/Correlational
examination and interpretation of relationships (e.g. is caloric intake related to weight?)
Epidemiological
study of health determinant patterns in a population; to assess risk, prevalence of disease, disability
Evaluation
determination of merit or worth using criteria against a set of standards (e.g. success of program in meeting goals)

can be formative (during project) or summative (after project)
Methodological
validation of measurement instruments or tools again accepted standards (e.g. new equipment/devices
FINER research
Feasibility
Interesting
Novel
Ethical
Relevant
Primary Goal of Evaluating Research Reports
to determine the worth or merit of a study, which ultimately depends upon the validity of the study
Key Components for an Outstanding Research Project
Significance
Investigators
Innovation
Approach
Environment
Inferential Statistics
decision making process that allows one to estimate population characteristics from sample data
Best Sample Is
Random
Representative
Really Large
Statistical Inference
success of this process (making valid conclusions) requires certain assumptions made concerning how well the sample represents the larger population: probability, sampling error
Sampling Error
tendency for sample values to differ from population values (occurs by chance)
Use of Probability in Research
guideline for making decisions about how well sample data estimate the characteristics of a population

are observed differences likely to be representative of population differences or occurred simply by chance
Sampling
Define Population
Inclusion Criteria
Exclusion Criteria
Standard Error of the Mean (definition)
estimation of the population standard deviation
Standard Error of the Mean (equation)
Standard Error = standard deviation / the square
root of the sample size (n)
Statistical Significance (definition)
indicates results of an analysis showing any difference or relationships are unlikely to be the result of chance
Type I Error
mistakenly concluding that a real difference exists, when the difference is due to chance
Type II Error
mistakenly concluding that a difference is due to chance when the samples represent different populations
Statistical Power (def)
probability that a test will detect a difference when one actually exists

probability that a test will lead to rejection of the null hypothesis
Statistical Power (factors)
alpha level of at least p<0.05
maximize bt group differences
reduce variability
incr sample size
use of effect size
Effect Size (def)
used to help determine an adequate sample size (n) from pilot data, to protect against Type II errors

d = (x1-x2)/sd = effect size index
Effect Size (value)
small effect = 0.20
medium = 0.50 (effect is half of std dev)
large effect = 0.80

80% is industry standard
What is an 0.80 effect size?
An 80% chance that we would detect a difference between the samples if one actually existed.
Confidence Interval (CI)
boundaries of the confidence interval are based on sample mean and its standard error

wide: greater uncertainty about the true value of population mean

narrow: more certainty about the population mean
Confidence Interval (calculation)
95% CI = mean +- (95% z-score) (standard error of mean)
Acts of Academic Dishonesty (2 Types)
Fabrication: creating and/or using made up/altered info in any type of scholarly activity or academic pursuit

Scientific Misconduct: deviation from the standard accepted professional practices in the performance, analysis, reporting, and/or publication of original research
Human Research Guidelines (6)
Autonomy - pt has right to refuse or choose treatment
Beneficence - practitioner should act in best interest of pt
Non-Maleficence - first, do no harm
Justice - distribution of scarce health resources
Dignity - pt has right to respect & ethical treatment
Truthfulness/Honesty - informed consent, full disclosure
Nuremberg Code:
Year
Why
What
1947

Code of ethics written in response to criminal experimentation on captive victims performed during WWII

Defined rules and practices for obtaining informed consent and competency of researchers
Declaration of Helsinki:
Year
What
1964 (rev 1989)
Int'l Code of Ethics for Biomedical Research for development of research proposals

Addressed independent review of protocol by committee not assoc with project
Belmont Report:
Year
What
1979

Human protection guidelines - ensures protection of human subjects and considered std throughout the US
Institutional Review Board (IRB)
Mission
protection of human subjects and compliance with established ethical principles and regulations
IRB Review Process Considerations
scientific merit
feasibility of study
competence of investigators
subject selection, informed consent, confidentiality
risk to subjects/minimized
risk-benefit ratio
Exempt Review
research deemed to pose NO RISK to subjects, generally not reviewed by IRB

collection or study of existing data, evaluation of educational programs/outcomes assessment, program evaluation, taste and food quality eval
Expedited Review
research deemed to pose no more than minimal risk to subjects, generally reviewed by IRB subcommittee

examples: collection of non0invasice specimens, collection of data on non-invasive procedures, moderate exercise/strength test, data for group characteristics or behavior (survey)
Full Reivew
research deemed to pose more than minimal risk to subjects, generally review by entire IRB

examples: investigational drug/clinical trials, new surgical procedures/devices, children or pregnant, individually identifiable samples
Hawthorne Effect
observers came to check out efficiency, turns out that efficiency went up tons when the lights were on - next day, no lights, performance down - turns out that the observation itself was what was increasing performance
minimize error variance
standardize measurement process, equipment calibration, use only reliable and valid measurement tools/techniques
construct
an abstract concept used to represent unobservable behaviors or ideas by incorporating a level or scale of measurement

(i.e. intelligence, strength, pain, mood, depression, etc)
nominal data
objects or people assigned to categories - codes have no quantitative value (i.e. 0 or 1)

(categorical)
ordinal data
categories rank-ordered in a "greater than: less than relationship" - intervals between ranks may not be consistent and/or may not be known - only represent a position within a distribution (i.e. 1st, 2nd, 3rd)

(categorical)
interval data
rank-order characteristics, also have known and equal distances/interval between units of measurement - relative difference and equivalence can be determined, can have negative values (i.e. temperature scales)

(continuous)
ratio data
absolute zero point that has empirical rather than arbitrary meaning - zero represents a total absence of whatever is being measured, no negative values possible (i.e. height, weight)

(continuous)
reliability
extent to which measurement is consistent and free from error

dependability or predictability of a specific measurement
accuracy
nearness of a measurement to the actual value of the variable being measured
precision
closeness of repeated measurements of the same quantity to each other
measurement bias
when difference between measured value and actual value is consistently inaccurate (i.e. 5 lbs too low)
sources of error
instrument/equipment (test-retest)
researcher/technician (inter/intra-rater)
subject/patient (intra-subject)
validity
extent to which an instrument measures what it is intended to measure

(validity is continuum - no perfect research construct in all validity domains)
repeated testing
(definition and how to control)
prior measurement of the dependent variable may affect the results obtained from subsequent measurements

practice first, don't reveal results, randomize order
content validity
items that make up instrument adequately sample the universe of content that defines the variable being measured
construct validity
ability of an instrument to actually measure an abstract concept or construct
inadequate definition of constructs
(definition and example)
$10 word for a $1 idea - not defining the construct clearly

what is patient satisfaction?
restricted generalizability
(definition and example)
"unintended consequences" or negative consequences of the side effects of a treatment

a drug reduces pain but promotes growth of facial hair (ok for men, not women)
criterion validity
ability of a test/instrument/measure to predict results obtained using a gold standard or criterion measure already known to be valid
descriptive stats
used to summarize and describe data - generalizability & external validity
measures of central tendency
mode, median, mean
measures of variability
range, variance, standard deviation
standard deviation
square root of the variance
1 standard deviation
one sided: 34%
two sided: 68%
2 standard deviation
one sided: 47%
two sided: 95%
3 standard deviation
one sided: 49%
two sided: 99.9%
data assessment:
independent vs dependent
independent: two or more groups consist of completely different individuals

dependent: matched pairs (same individuals tested more than once)
what is the distribution that looks like a plateau? not bell curve
platykurtic
parametric data
normally distributed, analyses used are based on assumptions that population also has normal curve - increases power of analysis
nonparametric data
not normally distributed, analyses cannot be based on normal distributions
parametric stats - when can we use?
1. normal distribution
2. samples drawn at random
3. variances must be equal
4. data must be interval or ratio
nonparametric stats - when do we use?
conditions for parametric aren't met, less powerful
data transformation
convert raw data by squaring, square root or calculated logarithms to make normally distributed - then can use parametric stats
concept of robustness
if sample is large, parametric tests can withstand SLIGHT variations from parametric assumptions
what tests are done for mean differences:

independent parametric
independent t-test
One-Way ANOVA
what tests are done for mean differences:

independent nonparametric
Mann-Whitney U
Kruskal-Wallace
what tests are done for mean differences:

dependent parametric
paired t-test
repeated measures ANOVA
what tests are done for mean differences:

dependent nonparametric
Wilcoxon
Friedman's
Why no multiple t-tests?
increases probability of Type I error
repeated measures
use one-way ANOVA to show differences among groups
post-hoc test
used to show which group is different
examples of post-hoc tests
Turkey test
Newman-Keuls test
Bonferroni t-test
Scheffe's comparisons
Turkey test
Post-Hoc
Newman-Keuls test
Post-Hoc
Bonferroni t-test (Dunn's)
Post-Hoc
Sheffe's Comparisons
Post-Hoc
Independent t-test
2 groups
independent, parametric
Mann-Whitney U
2 groups
independent, nonparametric
Paired t-test
2 groups
dependent, parametric
Wilcoxon
2 groups
dependent, nonparametric
One-Way ANOVA
2+ groups
independent, parametric
Kruskal-Wallace
2+ groups
independent, nonparametric
Repeated Measures ANOVA
2+ groups
dependent, parametric
Friedman's
2+ groups
dependent, nonparametric
correlation
used to describe the relative STRENGTH of relationship between two variables

correlation coefficient (r)
regression
used to describe predictive relationship between DV and IV

y=mb+b
coefficient of determination (r2)
correlation coefficient
goodness of fit
"r"
only used for LINEAR relationships
Pearson Product-Moment
correlation
continuous, parametric
Spearman (ranks)
correlation
continuous/categorical, nonparametric
correlation coefficient does NOT represent the _____ of association between two variables
percentage
one cannot infer ______ from ______
causation, correlation
regression
involves examination of two variables linearly related to be used as basis for prediction
regression involves determining a line of best fit
y=mx+b
what does regression line do for us?
help assess the accuracy of the "prediction model"
accuracy of the prediction can be determined by the ______
Coefficient of Determination (r2)
what is r2?
indicative of percentage of total variance in the DV explained by IV
if r2 value is 0.73, what does that mean?
73% of variance in DV can be explained by IV (or...27% of the variation involves other factors)
Standard Error of the Estimate (SEE)
accuracy of the predution based on variance error around regression line

farther the data points are away from line of best fit, the more error there will be, and the larger the SEE
What test? Distribution - analysis of frequencies
Chi Square