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EBP :/ > Module 8 - Exploratory Research and Correlation > Flashcards

Module 8 - Exploratory Research and Correlation Flashcards

1
Q

Uses of Exploratory Research

A

Estimate risk factors; (factors that may increase or decrease likelihood of developing a condition)

Explore relationships between two or more variables; (correlational research)

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2
Q

What is one of the most common uses of an observational design?

A

To draw causal inferences about the effects of a hypothetical RISK FACTOR

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3
Q

Causality

A

The goal of many observational studies is to IMPLICATE factors that contribute to health outcomes

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4
Q

If there is NO control/manipulation or randomization in an observational study, _________.

A

causality is more difficult to determine

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5
Q

Which is the only ethical and practical methods of investigating the causal impact of a potential risk factor?

A

Sometimes observational designs is the only one

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6
Q

Observational Study Approaches:

A

Longitudinal studies
-Prospective (looking forward in time)
-Retrospective (looking backward)

Cross-sectional studies

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7
Q

Longitudinal studies and Challenges in design

A

subjects are followed through time; can be performed either prospectively or retrospectively

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8
Q

Prospective study

A
  • Long time period to consistently collect data
  • Exposure status determined at start, with follow-up to see if outcome develops
  • Confounding can occur over time from other events and conditions.
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9
Q

Retrospective study

A
  • Involve the examination of previous data (e.g., medical records/notes, databases, existing survey etc.)
  • Difficulty in defining variables
  • Records may not be complete.
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10
Q

Cross-sectional studies

A
  • Considered a “snapshot” of a population
  • Data for exposure and outcome taken concurrently
  • Can describe health status of a population at a point
    in time
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11
Q

Cross-sectional studies and Challenges with design

A

Due to a lack of the time sequence, it may not be
possible to know whether the presumed cause
(exposure) truly proceeded the outcome.

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12
Q

Cohort studies (follow-up study)

A

Longitudinal investigation
* A group of individuals are followed over time
✓Subjects classified by exposure status at the start
✓All have potential for outcome condition
* Study association between exposure and the
outcome of interest
✓Outcome determined over time
* Can be either prospective or retrospective

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13
Q

Challenges for Cohort studies

A
  • Misclassification of exposure: over-report or under-report of exposure
  • Attrition and bias
    ✓ due to longitudinal nature, prospective cohort studies are prone to attrition, and
    ✓ Result in bias if the loss is related to exposure status, outcome status or both
  • Outcome may not occur in sufficient numbers
    ✓ Not appropriate for studying rare conditions
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14
Q

Case-Control Studies

A

A method of observational investigation in which groups of individuals are purposely selected on the basis of whether or not they have the health condition under study

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15
Q

Cases

A

those with the target condition

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16
Q

Controls

A

those who do not have the target condition

17
Q

Caso-Control Useful for?

A

Useful for studying rare or conditions with long latency

Exposure status compared between cases and controls

18
Q

Challenges for Case-Control Studies

A

Selection bias
Observation vias
Recall bias
Confounding

19
Q

Selection bias

A

is a special concern, because subjects are purposefully selected based on whether a target disorder is present or not (especially when volunteering to participate)

20
Q

Observation bias

A

Foreknowledge of outcomes may render bias by assessor or biased reporting by participants

21
Q

Recall bias

A

Those with a condition may have better recall of exposures

22
Q

Confounding

A

confounding effect of extraneous variables that are related to the exposure of interest (something that is known already/ using someone who is more likely)

23
Q

Correlational Research

A

Purpose: to examine relationships between /
among two or more variables

can be either retro or pros

24
Q

Correlational Research design

A

Usually involves one group of subjects with
measurements taken of different variables in order
to establish a mathematical relationship among the
variables
* To predict scores on one variable based upon
scores on another variable (predictive correlational
studies)
15

25
Q

Correlation Coefficient

A
  • Indicates the strength of the relationship
    ✓ Values between –1.0 and +1.0, where 0 is no relationship
  • Sign implies direction of the relationship
  • H0: population correlation = zero
26
Q

Assumptions for correlation

A

Scores represent the underlying population
* Scores are normally distributed
* Each subject has a score for both X and Y
✓ (e.g., X = GPA & Y = clinical performance score)
* X and Y are independent measures
* X and Y values are observed, NOT controlled/manipulated
* Relationship between X and Y is linear, not curvilinear

27
Q

Strength of correlations

A

Interpretation affected by sample size, measurement
error, types of variables being studied, and their
application.
* Authors should provide rationale for interpretation.

28
Q

Pearson Product Moment correlation

A

Parametric test that is appropriate to use when:
* X and Y continuous variables
➢ Interval or ratio scales
➢ Normal distribution
* Sample statistic: r statistic
* Population parameter, ρ (rho)
Null H0:  = 0 Alt H1:  ≠ 0
* Clinical vs. statistical significance

29
Q

Spearman Rank correlation coefficient

A
  • Nonparametric analog of Pearson’s r
    ✓ Based on ranked data
    ✓ Ordinal data or non-normal distribution
  • Symbol, rs
    Null H0: rs = 0 Alternative H1: rs > 0
30
Q

Correlation versus Comparison

A
  • Interpretation based on covariance
    ✓ The change in X is proportional to the change in Y.
  • Must be distinguished from the determination of
    differences between two means/distributions (in which
    t-test or ANOVA applicable)
31
Q

Correlation and Causation

A
  • Presence of association does NOT imply a causal
    relationship
    ✓ Strong relationship between X and Y may be a function of a third variable
32
Q

Outliers

A
  • Data points that lie outside the cluster of scores
  • Examine using a scatter plot
  • May be an extreme score due to a small sample size
    or may result from measurement error or from other
    extraneous factor
  • Need to determine if outliers should be retained or
    discarded
33
Q

Range of test values

A
  • Generalization of correlation should be limited to the
    range of values used to obtain the correlation
  • Should not extrapolate to scores outside this range

EBP :/ (11 decks)

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