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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


NonExperimental (type of research)

researcher does not manipulate or control any variables of interest


QuasiExperimental

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 = (x1x2)/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% zscore) (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 NonMaleficence  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 riskbenefit 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 noninvasive 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 rankordered 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

rankorder 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 (testretest)
researcher/technician (inter/intrarater) subject/patient (intrasubject) 

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 ttest
OneWay ANOVA 

what tests are done for mean differences:
independent nonparametric 
MannWhitney U
KruskalWallace 

what tests are done for mean differences:
dependent parametric 
paired ttest
repeated measures ANOVA 

what tests are done for mean differences:
dependent nonparametric 
Wilcoxon
Friedman's 

Why no multiple ttests?

increases probability of Type I error


repeated measures

use oneway ANOVA to show differences among groups


posthoc test

used to show which group is different


examples of posthoc tests

Turkey test
NewmanKeuls test Bonferroni ttest Scheffe's comparisons 

Turkey test

PostHoc


NewmanKeuls test

PostHoc


Bonferroni ttest (Dunn's)

PostHoc


Sheffe's Comparisons

PostHoc


Independent ttest

2 groups
independent, parametric 

MannWhitney U

2 groups
independent, nonparametric 

Paired ttest

2 groups
dependent, parametric 

Wilcoxon

2 groups
dependent, nonparametric 

OneWay ANOVA

2+ groups
independent, parametric 

KruskalWallace

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 ProductMoment

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
