2. Expected Learning Outcomes
• At the end of this lecture the student will be able to
• The importance of result interpretation.
• Identify the concepts related to the interpretation of study
findings such as: The credibility; the precision of the estimate of
effects; the magnitude of effects; the generalizability of the
results; and the implications.
• Identify the concept of inference.
• Identify the concepts of credibility of quantitative results and the
causality in research.
3. Interpretation and Quantitative Results
• The statistical results of a study, in and of themselves, do not
communicate much meaning.
• Statistical results must be interpreted to be of use to clinicians
and other researchers.
• Why is interpretation important? While the analysis is about
making sense of the data, interpretation is identifying how to
use the findings to improve the work and tell the story.
• It involves deciding which aspects of the findings are the most
interesting and important.
4. Interpretive Task
• Involves addressing six considerations:
• The credibility and accuracy of the results
• The precision of the estimate of effects
• The magnitude of effects and importance
• The meaning of the results
• The generalizability of the results
• The implications of the results for practice, theory, and further
research
5. • The definition of credibility is the quality of being trustworthy
or believable.
• The New England Journal of Medicine is an example of a
publication with a high degree of credibility.
• Precision refers to how close estimates from different samples
are to each other.
• For example, the standard error is a measure of precision. ...
When the standard error is small, sample estimates are more
precise; when the standard error is large, sample estimates are
less precise.
6. The magnitude of effects and importance
• Effect size helps readers understand the magnitude of
differences found, whereas statistical significance examines
whether the findings are likely due to chance. Both are essential
for readers to understand the full impact of the work
• Effect size is a quantitative measure of the magnitude of the
experimental effect. The larger the effect size the stronger the
relationship between two variables.
7. The generalizability of the results
• Generalizability is a measure of how useful the results of a
study are for a broader group of people or situations.
• If the results of a study are broadly applicable to many different
types of people or situations, the study is said to have good
generalizability.
• For example, a theoretical change model would be highly
generalizable if it applied to numerous behaviors (e.g., smoking,
diet, substance use, exercise) and varying populations (e.g.,
young children, teenagers, middle-age and older adults).
8. The implications of the results
• Research implications are the conclusions that the researcher
draws from the results and explain how the findings may be
necessary for policy, practice, or theory and future research.
• A simple example, if the research is based on the effects of a
particular drug on patients with diabetes, the research
implications could highlight how administering that drug does or
does not help the patients and further suggest measures for
regulating that drug.
9. Recommendations
• Recommendations urge specific actions to be taken about
policy, practice, theory, or subsequent research.
• Recommendations are based on the results of the research and
indicate the specific measures or directions that can be taken.
• For example, a clinical study might have implications for cancer
research and might recommend against the use of a particular
hazardous substance
10. Inference and Interpretation
• Interpreting research results involves making a series of
inferences.
• An Inference is a conclusion the researcher comes to by
analyzing Information.
• It is inductive reasoning: looking at facts and concluding from
them.
• An Interpretation is an Inference from a specific Point of View.
12. The Interpretative Mindset
• Approach the task of interpretation with a critical—and even
skeptical—mindset.
• Test the “null hypothesis” that the results are wrong against the
“research hypothesis” that they are right.
• Show me!!! Expect researchers to provide strong evidence that
their results are credible—i.e., that the “null hypothesis” has no
merit.
13. Credibility of Quantitative Results
• Proxies and interpretation
• Credibility and validity
• Credibility and bias
• Credibility and corroboration
14. Proxies and interpretation
• In statistics, a proxy or proxy variable is a variable that is not in
itself directly relevant, but that serves in place of an
unobservable or immeasurable variable.
• In order for a variable to be a good proxy, it must have a close
correlation, not necessarily linear, with the variable of interest.
• Examples:
• Body Mass Index (BMI): a proxy for true body fat percentage,
• years of education and/or GPA: a proxy for cognitive ability
15. Credibility and validity
• Credibility refers to the extent to which a research account is
believable and appropriate, with particular reference to the level
of agreement between participants and the researcher.
• Validity is the state of being valid, authentic or genuine while
credibility is reputation impacting one's ability to be believed.
16. Credibility and bias
• Bias technically means a systematic error, where a particular
research finding deviates from a 'true' finding.
• This might come about through errors in the manner of
interviewing, or by errors in sampling.
• For qualitative researchers, the methods used to establish
trustworthiness include credibility, transferability, dependability,
and confirmability.
• For quantitative researchers, the methods used to establish
trustworthiness include internal validity, external validity,
reliability, and objectivity.
17. Credibility and corroboration
• Credibility refers to the quality of being trusted and believable
and
• Corroboration refers to something that proves that statement or
idea is true.
• According to this, the relationship between credibility and
corroboration is that if you can corroborate something, it will
have more credibility
18. CONSORT Guidelines
• Reporting guidelines have been developed so that readers can
better evaluate methodologic decisions and outcomes.
• The Consolidated Standards of Reporting Trials (CONSORT)
include a flow chart for documenting participant flow in a study.
19. CONSORT Guidelines
• CONSORT is a protocol developed by a group of researchers
not only to identify problems arising from conducting RCTs, but
also to report, in a full and clear manner, the results yielded by
research, thereby facilitating RCTs reading and quality
assessment.
• The CONSORT Statement comprises a 25-item checklist and a
flow diagram.
• The checklist items focus on reporting how the trial was
designed, analyzed, and interpreted; the flow diagram displays
the progress of all participants through the trial.
20.
21. Precision and Magnitude
• Results should be interpreted in light of the precision of the
estimates (often communicated through confidence intervals)
and magnitude of effects (effect sizes).
• Considered especially important to clinical decision making
• In statistics, an effect size is a number measuring the strength
of the relationship between two variables in a statistical
population, or a sample-based estimate of that quantity.
22. The Meaning of Results
• If the results are credible and of sufficient precision and
importance, then inferences must be made about what they
mean.
• An interpretation of meaning requires understanding not only
methodological issues but also theoretical and substantive
ones.
23. Meaning and Causality
• Causality (also referred to as causation, or cause and effect) is
influence by which one event, process, state or object (a cause)
contributes to the production of another event, process, state or
object (an effect)
• Great caution is needed in drawing causal inferences—
especially when the study is non-experimental (and cross-
sectional).
• Critical maxim:
• CORRELATION DOES NOT PROVE CAUSATION.
24. Interpreting Hypothesized Results
• Greatest challenges to interpreting the meaning of results:
• Non-significant results
• Unexpected significant results
• Mixed results
• Because statistical procedures are designed to provide support
for research hypotheses through the rejection of the null
hypothesis, testing a research hypothesis that is a null
hypothesis is very difficult.
25. Critiquing the interpretations
• The researcher must acknowledge the research limitations (
There is no research without limitations).
• Researchers should alter the reader to the impact of any
limitations such as:
• Methodologic shortcomings,
• Sampling deficiencies
• practical constraints (i.e. impact of COVID-19 on the research)
• data quality problems
• Financial constrains
• The readers must be assured that these limitations were
considered in interpreting the results.
26. Reference
• Polit, D.F. & Beck, C.T. (2014). Essentials of nursing research:
Appraising evidence for nursing practice (8th ed.). Philadelphia:
Lippincott.
