Research 500 Flashcards

Question

treatment vs control group

Answer 1

"The treatment group (also called the experimental group) receives the treatment whose effect the researcher is interested in." "The control group receives either no treatment, a standard treatment whose effect is already known, or a placebo (a fake treatment to control for placebo effect)."

Answer 2

Prospective, longitudinal, observational studies ---> Attempt to explain relationship between treatment and outcome ---> Prospective = outcome has not yet occurred Members of the cohort are observed over a long period of time to see what the outcome is – e.g. some develop cardiovascular disease, others don’t, etc.

Answer 3

Cohort = a group who all experience the same treatment (or other variable); or exhibit the same characteristics (e.g. risk factor for cardiovascular disease)

Answer 4

Pros: provide strong observational “evidence” of a relationship between treatment/risk factors and the outcome Cons: take a long time and are expensive ---> Attrition is often high (e.g. participants drop out, die, move out of the region, etc)

Answer 5

A type of case series, but rather than just observing an outcome, practitioner determines hypothesis; sets eligibility criteria and methods; collects baseline data; provides tx; measures outcome for a series of patients (making it an experimental case series) ---> Often performed by practitioners in their own practices

Answer 6

Risk of many weaknesses Lacks a control group for comparison; potentially over-estimates the treatment effect Data collection may be subjective and patients may over-report good outcomes (possibly because they already have a relationship with the practitioner) May not be possible to generalize the findings to anyone outside the test group

Answer 7

Same, but less limitation d/t use of control, making it a stronger study

Answer 8

"The biggest difference between a controlled before-and-after study and a randomized controlled trial is a lack of randomization, which could introduce bias by not mitigating extraneous factors."

Answer 9

Describes events related to the care of a single patient Better than anecdote, d/t thorough rationale with presentation, description, detail, and discussion along with directions for future investigation Pros: can serve as the basis for a new hypothesis; can be used to report adverse reactions to treatment

Answer 10

Takes the case study a step further by combining individual case studies of similar patients Pros: may be the first indication of a new phenomena [sic]

Answer 11

A brief, revealing account of an individual person or an incident Not evidence, as there cause does not equal effect; lacks rationale, detail, and exploration Can be used to create a case report/case series

Answer 12

Population survey using existing data about groups NOT evidence, as correlation does not equal causation It does not prove cause and effect. It describes an association between exposure and outcome. Pros: quick way to see if there’s an association/correlation between an exposure and an outcome Pros: relatively low cost study; simple and quick to conduct

Answer 13

"For example consider the relationship between the weather and ice cream sales. As temperatures rise ice cream sales increase. There is a correlation between the temperature and ice cream sales." "Correlation means two variables change together, while causation implies that one variable directly influences another. Correlation does not imply causation, meaning that just because two things are related doesn't mean one causes the other." I.e. hot weather doesn't directly cause someone to buy ice-cream

Answer 14

abstract introduction methods results/findings conclusion/discussion references

Answer 15

Summary including background, purpose, design, methods, results, conclusion, and discussion

Answer 16

Thorough description of the purpose/importance, states the research question, and includes a literature review (which places a study in context) States the purpose for conducting the research Introduces the research question which the study addresses (sometimes the hypothesis)

Answer 17

Detailed description of how the study was carried out Readers should be able to decide if other explanations exist that explain the findings, or if the authors’ conclusions are strong Method should be so exact that the study is replicable by other researchers

Answer 18

Description of the analysis of the study data Can be qualitative or quantitative Objective; neither supports nor dismisses the hypothesis or research question

Answer 19

Answers the question, “so what do these results mean in terms of the research question?” Authors interpret the research results As the discussion sometimes puts forward the authors’ informed opinions, it’s good when they cite other studies that point to similar results

Answer 20

Listing of other research, articles, etc that the authors consulted while preparing the article you’re reading References should provide a good source of further reading

Answer 21

statement that can be demonstrated to be true or false through the methodical gathering and analysis of empirical information or data. ---> - An educated guess on how things work - Should be testable and measurable

Answer 22

1) independent variable - thing to be changed 2) dependent variable - thing to be measured

Answer 23

The null hypothesis is the commonly accepted fact; it is the opposite of the alternate hypothesis. ---> Researchers work to reject, nullify or disprove the null hypothesis by coming up with an alternate hypothesis that they believe explains a phenomenon and, therefore, rejects the null. Technically, researches do not prove a hypothesis, but disprove a null.

Answer 24

assumes there is no relationship b/w two variables "Cats show no preference to food based on shape" "plant growth is not affected by light colour" "age has no effect on musical ability"

Answer 25

alternative hypothesis: "Application of bio-fertilizer 'x' increases plant growth" ---> alternative hypothesis is a hypothesis which researcher tries to prove - it is denoted by H1 - opposite of null hypothesis (H0) null hypothesis: "Application of bio-fertilizer 'x' do not increase plant growth" ---> null hypothesis is hypothesis which researcher tries to disprove, nullify - it is denoted by H0 - opposite of alternative hypothesis (H1)

Answer 26

Cause Influencer Manipulating

Answer 27

Effect What is being influenced Measuring ---> Any measurable changes *depend* on the independent variable

Answer 28

any variable other than the independent variable that influences the dependent variable… can/should be controlled or tested itself in a high quality study.

Answer 29

By number of independent variables By subject assignment

Answer 30

Simple (1 independent variable) vs complex (>1 independent variable)

Answer 31

Between-subjects designs (independent designs): different subjects are used in each group Within-subjects designs (repeated measures designs): the same subjects are used in each group Mixed designs: include both between and within-subject components

Answer 32

each participant participates in one and only one condition of the experiment

Answer 33

all participants participate in all of the conditions of the experiment

Answer 34

Statistics Internal vs External Validity Ethics and Peer Review Critiquing an Article

Answer 35

Statistics is the study of the collection, organization, analysis and interpretation of collected data

Answer 36

Descriptive statistics present the study’s results without generalizing them to a larger group The purpose is to present data characteristics and summarize them as the average value for each variable, as well as the amount of variation of the data around the average These include range, central tendency, and standard deviations (more to come soon)

Answer 37

Inferential statistics are used to draw conclusions about observed differences between groups and whether results can be extrapolated to larger populations (i.e. statistical significance) Statistical significance tells us whether the results represent a meaningful result or are likely due to chance. It also estimates the strength of associations between variables Values of p = < .05 are considered statistically signification Tests used depend on the type of data involved

Answer 38

Analysis of variance (ANOVA) measures both statistical significance and the strength of association Examines the relationships among exposures/procedures/independent variables and how they affect outcome measures/dependent variables Multiple analysis can be utilized when there are multiple outcomes measured (MANOVA)

Answer 39

If demographic variables are unequally distributed between groups in a study, an analysis of covariance (ANCOVA) can be used to make groups comparable E.g. in a study assessing the effect of exercise on BP, there is a large attrition rate causing incomparable demographic groups (one much younger) by the end of the study. ANCOVA analysis would use age as a cofactor to eliminate any confounding effect on BP.

Answer 40

Power analysis determines how many participants/subjects were needed to have 80% chance of acquiring statistical significance This is typically done in the planning stages of a study, as authors prepare sample size and subject groups There should be at least 10 subjects per outcome measure

Answer 41

provides an estimate of how much of an outcome is due to the treatment(s) being measured. I.e. confirms the outcome (e.g. decreased SNS) is most likely due to the treatment (e.g. Swedish massage) As a research novice, the most important thing to note re: statistics in published articles is whether a study performed any tests of statistical significance and whether the authors provided rationale for the statistical tests they’ve used

Answer 42

central tendency mean median mode dispersion standard deviation range

Answer 43

of a set of data refers to a representation of the “middle” or the “expected” value of the data set; a central value for a probability distribution.

Answer 44

the sum of all the values in a list divided by the number of people in the sample (aka the average)

Answer 45

a number dividing the higher half of a list of values from the lower half. This is found by listing data numerically and finding the number in the exact middle. If the data set is an even number (ie 28), take the mean one the two central numbers

Answer 46

the most frequent value assumed by a random variable. The MODE is usually a different number than the median and mean, especially in a skewed distribution (see notes below) Mode is not necessarily unique – ie bimodal or trimodal, etc.

Answer 47

measures statistical variability and allows analysis of how widely dispersed the data are around the middle.

Answer 48

a measure of spread of numbers in a data set If data points are close to the mean, then standard deviation is small. If many data points are far from the mean, then the deviation is large. A low standard deviation (ie +/-1) means there is little variability A high standard deviation means there is a lot of variability Standard deviation may serve as a measure of uncertainty If a datapoint falls in the tail ends of a standard deviation, it is an outlier and atypical.

Answer 49

the different between highest and lowest values (subtract the two to deduce the range)

Answer 50

.. internal validity = how well study is done external validity = how applicable findings are in real world

Answer 51

is the ability to apply findings of research beyond the context of a study. That is, are the results generalizable to a larger group/population writ large? E.g. can animal studies be replicated in humans? Was the study sample large and diverse enough to apply to the larger population?

Answer 52

is the capacity of a study to link cause and effect, within the study itself. A good study excludes other hypotheses as possible explanations for the findings (ie it rules out that the results are due to any other correlation and draws a direct and absolute connection between cause and effect). I.e. correlation vs. causation

Answer 53

aka confounding variables or sources of bias

Answer 54

blurs or masks the effect of another variable (example: students’ reactions to coffee may be confounded by how much sleep they had the previous night.

Answer 55

formal “research” sense of bias is a systematic error (example: a tendency to underestimate placebo effect in treatment outcomes.

Answer 56

often an important threat to internal validity based on choosing specific or certain groups to survey/study. (example: asking Facebook users how technology has impacted their lives will have different results vs. asking non-computer-literate citizens how technology has impacted their lives. Therefore, you cannot make inferential claims about the effects of technology on society; just on the sample of Facebook users you questioned.) When selecting samples, this threat can be limited by randomizing and including generalizable sample populations

Answer 57

Another potential threat to internal validity

Answer 58

Type I error = a “false positive” – i.e. rejecting a null hypothesis that is actually true, meaning the research hypothesis is wrong (concluding a treatment works when it doesn’t) Type II error = a “false negative” – i.e. failing to reject a null hypothesis that is actually false, meaning the research hypothesis is right (concluding a treatment doesn’t work when it does)

Answer 59

This relates to power: the more participants complete a questionnaire, and the fewer the measured outcomes, the greater the power. If power is low (small sample, many treatment outcomes), a Type II error may occur. E.g., a true treatment outcome may be occurring but cannot be seen in the data because the sample is too small, and/or the treatment outcomes are too many.

Answer 60

Defined as: - Dependability, consistency, and reproducibility of information obtained in a study - The probability that the same results would be obtained with different subjects (i.e. generalizability)

Answer 61

1) Test-retest: administering the same test/therapy/etc twice to the same group of subjects after a certain amount of time 2) Equivalent-forms method: two different, but equivalent, therapies to the same group at the same time 3) Internal-consistency method: the extent to which different researchers would be consistent in matching the same data and constructs

Answer 62

Validity = accuracy; reliability = consistency Validity = degree to which an instrument measures what it supposed to measure; reliability = degree to which an instrument measures the same way each time it is used under the same conditions

Answer 63

scientists study something scientists write about results journal editor receives article & sends for peer review peer reviewers read and provide feedback to editor editor may send back to scientists who may revise & resubmit --- may then be approved or rejected, or sent back if article meets standards, it's published in journal

Answer 64

Ethics committees act to safeguard human rights and protect people who participate in health care research According to the Panel on Research Ethics Government of Canada – “Ethical principals and guidelines play an important role in advancing the pursuit of knowledge while protecting and respecting research participants” This is the ideal scenario; ethics violations have happened throughout the history of medical research

Answer 65

1) Respect for persons: recognizes the intrinsic value of human beings and the respect and consideration that they are due 2) Concern for welfare: the quality of a person’s experiences of life in all its aspects 3) Justice: the obligation to treat fairly and equitably

Research 500 Flashcards

(90 cards)