Nursing Research
63-377
Dr. Wally J. Bartfay
“The most important lesson that
can be given to nurses is to teach
them what to observe, how to
observe, what symptoms indicate
improvement, what the reserve…”
Florence Nightingale (1860)
Methods of Statistical Data Analysis
for Quantitative Nursing Research
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(1) PARAMETRIC TESTS: must have following 3
assumptions present
(a) sample has been drawn from a population for
which the variance can be calculated, & distribution
is expected to be normal (or near normal)
(b) level of measurement should be interval or
ordinal with an approximately normal distribution
(c) data can be treated as random samples
Methods of Statistical Data Analysis
for Quantitative Nursing Research
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(2) NONPARAMETRIC TESTS:
Statistical techniques used when the
assumptions of parametric statistics are NOT
met
Most commonly used for nominal & ordinal
level data
Methods of Statistical Data Analysis
for Quantitative Nursing Research:
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(1) NOMINAL DATA: (Independent Groups, 2
variables)
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(a) Chi-Square: used to determine significant differences
between observed frequencies within the data & frequencies
that were expected
(b) Phi coefficient: used to determine relationships in
dichotomous, nominal data
(c) Cramer’s V: Analysis technique that is a modification of Phi
for continency tables larger than 2 X 2
(d) Contingency Coefficient: a stat. test used with 2 nominal
variables & is the most commonly used of the chi-squaredbased measures of association
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Methods of Statistical Data Analysis
for Quantitative Nursing Research
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(2) ORDINAL DATA (Independent Groups, 2
variables) :
(a) Mann-Whitney U: has 95% of the power of t-test to detect
differences between groups of normally distributed populations
(b) Spearman’s Rank-Order Correlation: A nonparametric
analysis technique that is an adaption of Pearson product
moment correlation used to examine relationships among
variables in a study
(c) Kendall’s Tau: Nonparametric test to determine correlation
among variables used when both variables are at the ordinal
level
Methods of Statistical Data Analysis
for Quantitative Nursing Research
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(3) RATIO-LEVEL and/ or INTERVAL DATA: (Independent
Groups, 2 variables) :
(a) T-test for independent samples: Parametric analysis tech. used
to determine significant differences between measures of 2
samples
(b) Pearson’s Correlation: Parametric tests used to determine the
relationship between variables (-1 to +1 = perfect correlation,
whereas 0 = no correlation)
(c) ANOVA: Used to examine differences among 2 or more groups
by comparing the variability between the groups with the variability
within the groups
(d) Simple linear regression: parametric analysis tech. that
provides a means to estimate the value of a dependent variablebased on the value of an independent variable
Bivariate Statistical Data Analysis
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(1) Nominal data:
(a) McNemar Test (2 samples):
nonparametric tests in which a 2 X 2 table is
used to analyze changes that occur in
dichotomous variables
(b) Cochran Q test (3 or more samples):
nonparametric test that is an extension of
above for two related samples
Bivariate Statistical Data Analysis
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(2) ORDINAL DATA:
(a) Sign test (2 samples): a nonparametric tech.
developed for data that is difficult to assign numberical
values to but the data can be ranked on some dimension
(b) Wilcoxon matched paired signed ranks tests (2
samples): Nonparametric tech. used to examine
changes that occur in pretest/posttest measures or
matched-pairs measures
(c)Friedman Two-way analysis of variance by ranks (3 or
more samples): Nonparametric test used with matched
samples or in repeated measures
Bivariate Statistical Data Analysis
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(3) Interval or Ratio Data:
(a) T-test for related samples (2 variables): a
parametric test used for analyzing the
difference between two means
(b) Analysis of covariance (ANCOVA)(3 or
more samples): Used to test the effect of one
or more treatments on different groups while
controlling for one or more extraneous
variables (covariates)
Multivariate data analysis
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Interval or Ratio Data (3 or more variables):
(a) Multiple regression analysis: extension of simple linear
regression with more than one independent variable entered
(b) Factorial analysis of variance: mathematically it is a
specialized form of multiple regression- a No. of types of
factorial ANOVA’s have been developed to analyze data from
experimental designs
(c)Structured equation modeling: designed to test theories
(d) Time-series analysis: tech. designed to analyze changes
in a variable across time and thus to uncover a pattern in the
data
Type I Errors
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occurs when researcher concludes that the samples
tested are from different populations (difference
between groups is significance), when in fact the
samples are from the same population (the
difference between groups is NOT significant)
Null hypothesis is rejected when it is, in fact, true
Can help to decrease chance of this, in part, by
increased significance level (e.g., from p < 0.05 to p
< 0.001)
Type II errors
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Occurs when the researcher concludes that there
are no significant difference between the samples
examined when, in fact, a difference exists
Null hypothesis is regarded as true, when it is false
Can help to control for this, in part, by increasing
sample size, which increases power of study (do
power statistical power analysis prior to determine
the risk of type II and needed sample size)
Errors in Data Collection in Qualitative
Research
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Can be introduced in 3 major ways:
(1) In the process of data collection;
(2) With the process of data analysis, and
(3) When both of the above occurs
Errors in Data Collection in Qualitative
Research
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Qualitative researchers employ several
techniques and processes to ensure the rigor
of the data collection and analysis processes
(1) RIGOR: term reflects overall quality of
study
It is reflected in the consistency of data
analysis and interpretation, trustworthiness of
the data collected, transferability of the
themes & credibility of data is ensured
Errors in Data Collection in Qualitative
Research
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(2) Trustworthiness: refers to the “honesty” of
the data collected
Hence, researcher must have meaningful
relationship with subjects & have willingness
to share their feelings, insights, experiences
Consistency in the data collection process
helps to establish this
Errors in Data Collection in Qualitative
Research
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(3) Confirmability: refers to the consistency
and repeatability of the decision making
about the process of data collection &
analysis
Can do “audit trail” which is an ongoing
documentation regarding the researcher’s
decisions made by researcher (e.g., field
notes about processes undertaken)
Errors in Data Collection in Qualitative
Research
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(4) Transferability: refers to the extent to which the
findings of a study are confirmed by or are applicable
to a different group or in a different setting from
where the data was collected
Different from “generalizability” B/C focus is NOT on
predicting specific outcomes in a general population
Rather, focus is on confirming that what was
meaningful in one specific setting or group, is also
meaningful & accurate in a different setting or group
(e.g., caregiver burden experienced by stroke
survivors with aphasia versus those without aphasia)
Errors in Data Collection in Qualitative
Research
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(5) Credibility: overlaps with transferability &
trustworthiness- refers to the confidence that the
researcher & user of research can have in the truth of the
findings of the study
Can do “member checks” where data & findings are
brought back to subjects to seek their input, check for
accuracy, completeness & interpretation of data
Can also do “Triangulation”: a process of using more than
one approach or source to include different views or to look
at the phenomenon from different angles (e.g., Q-sort
methods & structured interviews)
Analysis of Qualitative Data: General
overview
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Various methods have been developed (e.g.,
Glaser and Strauss/ Corbin’s methods;
phenomenologic methods etc)
All involve coding of data schemes and
“clustering” of emerging themes from the
data (thematic analysis of data)
Qualitative Data Analysis
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Computer programs available now to help
researchers organize & analyze their data
(e.g., NU.DIST; N. VIVO;ATLAS/TI; MARTIN;
QUALPRO and HyperQual2)
Importantly, these programs DO NOT
perform the thinking & conceptualizing that is
at the heart of qualitative data analysis
Can help to organize data around “themes”
as they are identified in the data
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Have a fantastic week.
Come and see me regarding your research
proposals…