Title: Advanced Quantitative Methods
1Advanced Quantitative Methods
- William L. Holzemer, RN, Ph.D., FAAN
- Professor, School of Nursing
- University of California, San Francisco
- bill.holzemer_at_nursing.ucsf.edu
2Objectives
- Develop your definition of nursing science
- Use the Outcomes Model to think about your
area(s) of interest - Review quantitative methods
- Think about how we build knowledge to improve
health and nursing practice.
3Assignments
- PhD Students -individual assignments
- MS Students group assignment
- Mini-literature review
- Outcomes Model
- Substruction
- Synthesis Tables
- Summary
4Nursing Nursing Science?
- Definition of Nursing
- American Nurses Association
- Nursing is the assessment , diagnoses, and
treatment of human responses
5Definition of Nursing
- Japan Nurses Association
- Nursing is defined as to assist the
- individual and the group, sick or well, to
- maintain, promote and restore health.
6Definition of NursingInternational Council of
Nurses
- Nursing encompasses autonomous and collaborative
care of individuals of all ages, families, groups
and communities, sick or well and in all
settings. Nursing includes the promotion of
health, prevention of illness, and the care of
ill, disabled and dying people. Advocacy,
promotion of a safe environment, research,
participation in shaping health policy and in
patient and health systems management, and
education are also key nursing roles.
7Common ElementsDefinitions of Nursing
- Person (individual, family, community)
- Health (Wellness Illness)
- Environment
- Nursing (care, interventions, treatments)
8Nursing Science
- The body of knowledge that supports
- evidence-based practice
9Nursing Science Uses Various Research
Methodologies
- Qualitative
- Understanding
- Interview/observation
- Discovering frameworks
- Textual (words)
- Theory generating
- Quality of informant more important than sample
size - Rigor
- Subjective
- Intuitive
- Embedded knowledge
- Quantitative
- Prediction
- Survey/questionnaires
- Existing frameworks
- Numerical
- Theory testing (RCTs)
- Sample size core issue in reliability of data
- Rigor
- Objective
- Public
10Types of Research Methods (all have rules of
evidence!)
- Quantitative
- Non-Experimental or Descriptive
- Experimental or Randomized Controlled Trials
- Ethnography
- Content Analysis
- Models of analysis Parametric vs.
non-parametric
- Qualitative
- Grounded theory
- Ethnography
- Critical feminist theory
- Phenomenology
Models of analysis fidelity to text or words of
interviewees
11Outcomes Model for Health Care Research(Holzemer,
1994)
Inputs? 1970s Processes ? 1980s Outcomes 1990s
Client
Provider
Setting
12Outcomes Model
- Heuristic
- Systems model (inputs are outputs, outputs become
inputs) - Relates to Donabedians work on quality of care
(Structure, Process, and Outcome Standards)
13Outcomes Model Nursing Process
Inputs? Processes ? Outcomes
Client Problem Outcome
Provider Intervention
Setting
14Outcomes Model for Health Care Research
Inputs? (Covariate, confounding variable) Processes ? (Independent Variable) Outcomes (Outcome Variable)
Client Age, gender, SES, Ethnicity Severity of Illness Self-care Adherence Family care Quality of Life Pain control Pt. satisfaction Pt. falls,
Provider Age, gender, SES, Education, Experience, Certification Perc. Autonomy Interventions Care Talking, touch, time Vigilance, communication Quality of Work life Turnover Errors Satisfaction
Setting Resources Philosophy Staffing levels Actual staffing ratios Mortality Morbidity Cost
15Outcomes Model Your assignment(Think about a
project or program of research)
Inputs? z Processes ? x Outcomes y
Client
Provider
Setting
16Where Should We Find Evidence-Based Practice
Guidelines?
- Clinical practice guidelines
- Nursing Standards/ Procedural Manuals
- Great demand, low level of delivery (Great
demand, growing level of delivery) - Knowledge base from research literature
17Types of Evidence How do we know what we know?
- Clinical expertise
- Intuition
- Stories
- Preferences, values, beliefs, rights
- Descriptive/quasi-experimental studies
- Randomized clinical (controlled) trials (RCTs) -
the gold standard
18Summary Introduction to Research
- Think about nursing research nursing science
- Outcomes Model designed to put boundaries around
your area of study and expertise (very difficult
challenge in nursing!) - Variable identification
- Understanding rigor correct methods for any
type of research design - Enhance enjoyment in reading research articles
- Understand the challenge of the words so easily
used, evidence-based practice.
19Some Challenges
- Think about developing your definition of nursing
science. - Use the Outcomes Model to help you think about
your program of research. - Enhance your understanding of rigor in all types
of research designs. - Increase your enjoyment of reading research
articles. - Understand the complexities of evidence-based
practice.
20When thinking about your research problem
- Is it significant?
- Are you really interested in it?
- Is it novel?
- Is it an important area?
- High cost, high risk?
- Can it be studied?
- Is it relevant to clinical practice?
21Where do ideas come from?
- Literature reviews
- Newspaper stories
- Being a research assistant
- Mentors/teachers
- Fellow students
- Patients
- Clinical experience
- Experts in the field
- Build your area of expertise from multiple
sources.
22Uses of Substruction
- Critique a published study
- Plan a new study
23Substruction
- A strategy to help you understand the theory and
methods (operational system) in a research study - Applies to empirical, quantitative research
studies - There is no word, Substruction, in the
dictionary. It has an inductive meaning,
constructing and a deductive meaning,
deconstructing - Hueristic
24Substruction
Theory (Theoretical system) Construct ? Concept Deductive ? ? ? (qualitative) ? ? ?
Methods (Operational System) Measures ? Scaling/Data analysis ? ? ? (quantitative) ? ? ? Inductive
25Substruction Building Blocks or Statements of
Relationships
Construct Pain ? ?axiom? Construct quality of life ?
Concept Intensity ? ?proposition? Concept functional status ?
Measure 10 cm scale ?hypothesis? Measure mobility scale
26Statements of Relationships
Construct
Postulate Statement of relationship between a construct and concepts Pain consists of three concepts
Concepts Intensity Location Duration
27Substruction Research Design Perspective
Focus of Study (RCT?)
28Substruction Theoretical System, an example
Pain Intervention Study
Post Surgical Patient Severity of illness age
gender
Pain Management Intervention Patient
communication Standing PRN orders Non
pharmacological tx
Pain Control Length of stay Patient Satisfaction
29Substruction Operational System
- Pain Intensity
- Instrument
- VAS 10 cm scale
- (low to high pain)
- Functional Status
- Instrument1-5 Likert scale, 1low 5high
function - Scale continuous or discrete?
Scale continuous or discrete?
30Scaling
- Discrete non-parametric (Chi square)
- Nominal gender
- Ordinal low, medium, high income
- Continuous parametric (t or F tests)
- Interval Likert scale, 1-5 functionality
- Ratio money, age, blood pressure
31Issues
- What is the conceptual basis of the study?
- What are the major concepts and their
relationships? - Are the proposed relationships among the
constructs and concepts logical and defensible? - How are the concepts measured? valid? reliable?
- What is the level of scaling and does it relate
to the appropriate statistical or data analytical
plan? - Is there logical consistency between the
theoretical system and the operational system?
32Is there a relationship between touch and pain
control, accounting for initial amount of
post-operative pain? rx,y.z
Inputs? Z Processes ? X Outcomes Y
Client Post operative pain Pain Control
Provider Therapeutic Touch vs NL care
Setting
33Literature Review
- We review the literature in order to understand
the theoretical and operational systems relevant
to our area of interest. - What is known about the constructs and concepts
in our area of interest? - What theories are proposed that link our
variables of interest?
34Literature Review
- What is known?
- What is not known?
- Resources
- The Cochran Library
- Library Data Bases
- PubMed
- CINYL
35Literature ReviewHow to combine, synthesis, and
demonstrate direction?
36Literature Review
37Table 1. Outline of study variables related to
your topic
Studies Covariates Z Interventions Independent variable X Outcomes Dependent Variable Y
Smith (1999)
Jones (2003)
Etc.
38Table 2. Threats to validity of research studies
related to topic
Author (year) Type of Design Diagram Statistical Conclusion Validity Construct Validity of Cause Effect Internal Validity External Validity
Smith (1999) RCT O X1 O O X2 O O O n/a
Jones (2003)
39Table 3. Instruments
Studies Instrument items Validity Reliability Utility
Smith (1999) McGill Pain Questionnaire
Jones (2003)
40Table 4. Power analysis for literature review on
topic.
Studies Sample Size Alpha Power Effect Size
Smith (1999) 32 exp 40 cont 0.05 0.60 Est. at medium
Jones (2003)
41Literature Synthesis
- Synthesis - what we know and do not know
- Strengths rigor, types of design, instruments?
- Weaknesses lack of rigor, no RCTs, poorly
developed instruments - Future needs what is the next step?
42Research Designs
43Research Design Qualitative
- Ethnography
- Phenomenology
- Hermeneutics
- Grounded Theory
- Historical
- Case Study
- Narrative
44Rigor in Qualitative Research
- Dependability
- Credibility
- Transferability
- Confirmability
45Types of Quantitative Research Designs
- We will focus on RIGOR
- Experimental
- Non-experimental
46X,Y, Z notation
- Z covariate
- Severity of illness
- X independent variable (interventions)
- Self-care symptom management
- Y dependent variable (outcome)
- Quality of life
47Types of Quantitative Research Designs
- Descriptive X? Y? Z?
- What is X, Y, and Z?
- Correlational rxy.z
- Is there a relationship between X and Y?
- Causal ?X ? ?Y?
- Does a change in X cause a change in Y?
48Rigor in Quantitative Research
- Theoretical Grounding Axioms postulates
substruction-validity of hypothesized
relationships - Design validity (internal external) of research
design Instrument validity and reliability - Statistical assumptions met (scaling, normal
curve, linear relationship, etc.) -
- (Note Polit Beck reliability, validity,
generalizability, objectivity)
49- Literature Review Study Aims
- Study Aims Study Question
- Study Question Study Hypothesis
50Aim, Question, and Hypothesis
- Study Aim To explore if it is possible to reduce
patient falls for elderly in nursing homes. - Study Question Does putting a sitter in a
patient room reduce the incidence of falls? - Study Hypothesis
- Null H0 There is no difference between
patients who have a sitter and those who do not
in the incidence of falls.
51Experimental Designs
52Definition Experimental Design
- There is an intervention that is controlled or
delivered - There is an experimental and control group
- There is random assignment to groups
53Classic Experimental Design
- O1exp X O2exp
- ?
- R
- ?
- O1con O2con
- (pretest) (posttest)
-
- Oobservation
- 1 pretest or time one 2 posttest or time
two - X intervention
- R random assignment to groups
54Classic Experimental Design
- O1exp X O2exp
- ?
- R
- ?
- O1con O2con
- (pretest) (posttest)
-
- The RCT is the Gold Standard for
- Evidence-Based Practice
55Randomization
- Random assignment to groups (internal validity
issue) equals Z variables in both groups - Random selection from population to sample
(external validity issue) equals Z variables in
the sample that are true for the population
56Goal
- Statement of Causal Relationship
57Conditions Required to Make a Causal Statement
X causes Y
- X precedes Y
- X and Y are correlated
- Everything else controlled or eliminated. No Z
variables impacting outcome. - We never prove something, we gather evidence that
supports our claim.
58Controlling Z variables
- Minimize threats to internal validity
- Limit sample (e.g. under 35 years only) to
control variation - Statistical manipulation (ANCOVA)
- Random assignment to groups
59Dimensions of Research Designs Groups Time
- O1exp X O2exp
- ?
- Groups (n2 experimental control)
- ?
- O1con O2con
- --------------------------
--------------------- - ? Time (n2) ?
- (repeated measures)
-
-
-
60Dimensions of Research Designs Groups Time
- Groups between factors
- Time within factors
61Types of Designs
- O - descriptive, one time
- O1 O2 O3 - descriptive, cohort, repeated
measures) - O1 X O2 (not an experimental design!) -
pre-post-test -
62Types of Designs
- O1 X O2
- O1 O2
- RCT randomized controlled trial
63Types of Designs
- O1 O2 O3 X O4 O5 O6
- O1 O2 O3 O4 O5 O6
- O1 X O2 Xno O3 X O4 Xno O5
- (repeated measures vs. time series designs)
-
64Types of Design
- O1 X1 O2
- R O1 X2 O2
- O1 O2
- of groups? ___
- points in time? ___
65Types of Designs
- Post-test only design
- X O2
- O2
- What is the biggest threat to this post-test only
design? -
66Types of Research Design
- Experimental (true)
- Quasi-Experimental (quasi)
- No random assignment to groups
67Design Validity
- Statistical conclusion validity
- Construct validity of Cause Effect (X Y)
- Internal validity
- External
68Design Validity
- Statistical Conclusion Validity rxy?
- Type I error (alpha 0.05)
- Type II error (Beta) Power 1-Beta, inadequate
power, i.e. low sample size - Reliability of measures
- Can you trust the statistical findings?
69Design Validity
- Construct Validity of Putative Cause Effect
(?X ? ?Y?) - Theoretical basis linking constructs and concepts
(substruction) - Outcomes sensitive to nursing care
- Link intervention with outcome theoretically
- Is there any theoretical rationale for why X and
Y should be related?
70Design Validity
- Internal Validity
- Threat of history (intervening event)
- Threat of maturation (developmental change)
- Threat of testing (instrument causes an effect)
- Threat of instrumentation (reliability of
measure) - Threat of mortality (subject drop out)
- Threat of selection bias (poor selection of
subjects) - Are any Z variables causing the observed changes
in Y?
71Design Validity
- External Validity
- Threat of low generalizability to people, places,
time - Can we generalize to others?
72Building Knowledge
- Goal is to have confidence in our descriptive,
correlational, and causal data. - Rigor means to follow the required techniques and
strategies for increasing our trust and
confidence in the research findings.
73Sampling
- Sample selection, not assignment
74Terms
- Population
- Sample
- Element
- - All possible subjects
- -A subset of subjects
- - One subject
75What do we sample?
- People (e.g. subjects)
- Places (e.g. hospitals, units, cities)
- Time (e.g. season, am vs. pm shift )
76Sampling What do we do?
- Random Assignment
- -is designed to equalize the Z variables in the
experimental and control groups
- Random Selection
- -is designed to equalize the z variables that
exist in the population to be equally distributed
in a sample
77Types of Probability Sampling
- Probability
- Simple random sampling using a random table of
numbers - Stratified random sampling divide or stratify by
gender and sample within group - Systematic random sampling take every 10th name
- Cluster sampling select units (clusters) in
order to access patients or nurses
78Types of Non-probability sampling
- Convenience first patients to walk in the door
- Purposive patients living with an illness
- Quota equal numbers of men women
- (volunteers)
- (convenience)
79Types of Samples
- Homogeneous subjects are similar, all females,
all between the ages of 21-35 - Heterogeneous subjects are diverse, wide age
range, all types of cancer patients
80Sampling Error
81How to control sampling error?
- Use random selection of subjects
- Use random assignment of subjects to groups
- Estimate required sample size using power
analysis to ensure adequate power - Overestimate required sample size to account for
sample mortality (drop out)
82Sample Size and Sampling Error
83Sample Size Calculations
- Type of design
- Accessibility of participants
- Statistical tests planned
- Review of the literature
- Cost (time and money)
84Strategies for Estimating Sample Size
- Ratio of subjects to variables in correlational
analysis. 31 up to 301 subjects to variables.
30 item questionnaire requires 90 to 900
subjects. - Chi square cant work if less than 5 subjects
per cell -
85Power Analysis
- Power - commonly set at 0.80
- Alpha - commonly set at 0.05 or 0.01
- Effect Size - based upon pilot studies or
literature review small, medium, large - Sample Size - subjects required to ensure
adequate power - Power is a function of alpha, effect size, and
sample size.
86Power Analysis Programs
- SPSS Pakcage
- nQuery Adviser Release 4.0 (most recent?)
- http//www.statsolusa.com
87Power
- Power is the ability to detect a difference
between mean scores, or the magnitude of a
correlation. - If you do not have enough power in a study, it
does not matter how big the effect size, i.e. how
successful your intervention, you can not
statistically detect the effect. - Many studies are under powered.
88Effect Size
- Effect size can be thought of as how big a
difference the intervention made. - Statistical significance and clinical
significance are often not the same thing
89Effect Size
- Small (correlations around 0.20)
- Requires larger sample size
- Medium (correlations around 0.40)
- Requires medium sample size
- Large (correlations around 0.60)
- Requires smaller sample size
90Effect Size
- Meanexp Meancon
- Effect Size
- SD e c
91Eta Squared (?2)
- In ANOVA, it is the proportion of dependent
variable (Y) explained. - Estimate of Effect Size
- Similar to R2 in multiple regression analysis.
92alpha
- alpha relates to hypothesis testing and how often
you are willing to make a mistake in drawing a
conclusion - alpha is equivalent to Type 1 error or saying
that the intervention worked, when in fact the
effect size observed, is just due to chance - alpha of 0.01 is more conservative than 0.05 and
therefore, harder to detect differences
93Hypothesis Testing Is it true or false?
- Null hypothesis H0
- Mean (experimental) Mean (control)
- Alternative hypothesis H1
- Mean (experimental) / Mean (control)
94Hypothesis Testing and Power
Goal Reject H0 REALITY REALITY
Null H0 True H0McMe Null H0 False H0Mc/Me
DECISION Reject H0 Type I Error Power (1-Beta)
DECISION Accept H0 Correct Decision Type II Error (Beta)
95Quiz
- If sample size goes up, what happens to power?
- If alpha goes from .05 to .l01, what happens to
required sample size? - If power falls from .80 to .60, what type of
error is most likely to occur? - If effect size is estimated based upon the
literature as large, what effect does this have
on the required sample size?
96Sample Loss in RCT
97Measurement
- If it exists, it can be measured
- R. Cronbach
98What we measure
- Knowledge, Attitudes, Behaviors (KAB)
- Physiological variables
- Symptoms
- Skills
- Costs
99Classical Measurement Theory
100Type of Measures
- Standardized evidence as follows
- Systematically developed
- Evidence for instrument validity
- Evidence for instrument reliability
- Evidence for instrument utility time, scoring,
costs, sensitive to change over time - Non-standardized
101Types of Measurement Error
- Systematic - can work to minimize systematic
error due to poor instructions, poor reliability
of measures, etc. - Random - can do nothing about this, always
present, we never measure anything perfectly,
there is always some error.
102Validity
- Question Does the instrument measure what it is
supposed to measure? - Theory-related validity
- Face validity
- Content validity
- Construct validity
- Criterion-related validity
- Concurrent validity
- Predictive validity
103Theory-related Validity
- Face validity
- participant believability
- Content validity (observable)
- Blue print
- Skills list
- Construct validity (unobservable)
- Group differences
- Changes of times
- Correlations/factor analysis
104Criterion-related Validity
- Concurrent
- Measure two variables and correlate them to
demonstrate that measure 1 is measuring the same
thing as measure 2 same point in time. - Predictive
- Measure two variables, one now and one in the
future, correlate them to demonstrate that
measure 1 is predictive of measure 2, something
in the future.
105Reminder
- Design Validity
- Does the research design allow the investigator
to answer their hypothesis? - (Threats of internal and external validity)
- Instrument Validity
- Does the instrument measure what it is supposed
to measure?
106Instrument Reliability
- Question can you trust the data?
- Stability change over time
- Consistency within item agreement
- Rater reliability rater agreement
107Instrument Reliability
- Test-retest reliability (stability)
- Pearson product moment correlations
- Cronbachs alpha (consistency) one point in
time, measures inter-item correlations, or
agreements. - Rater reliability (correct for change agreement)
- Inter-rater reliability Cohens kappa
- Intra-rater reliability Scotts pi
108Cronbachs alpha
alpha
SD
109Cronbach alpha Reliability Estimates
- gt 0.90
- Excellent reliability, required for
decision-making at the individual level. - 0.80
- Good reliability, required for decision-making at
the group level. - 0.70
- Adequate reliability, close to unacceptable as
too much error in the data. Why?
110Internal Consistency Cronbachs alphaPerson A
Internally consistentPerson B Internally
inconsistent
Item All the time Much of the time A little of the time Rarely
1 4 A 3 2 1 B
2 4 B 3 A 2 1
3 4 3 A 2 B 1
4 4 A 3 B 2 1
111Error in Reliability Estimates
- Error 1 (Reliability Estimate)2
- If alpha 0.90, 1-(0.90)2
- 1-0.89 .11 error
- If alpha 0.70, 1 (0.70)2
- 1-.49 .51 error
- If alpha 0.70, it is the 5050 point
- of error vs. true value
112Reliability Values
- Range 0 to 1
- No negative signs like correlations
- Cohens kappa and Scotts pi are always lower,
i.e. 0.50, 0.60
113Utility Things you would like to know about an
instrument.
- Time to complete (subject fatigue)?
- Is it obtrusive to participants?
- Number of items (power analysis)?
- Cultural, gender, ethnic appropriateness?
- Instructions for scoring?
- Normative data available?
114Reporting on Instruments
- Concept(s) being measured
- Length of instrument or number of items
- Response format (Likert scale, etc.)
- Evidence of validity
- Evidence of reliability
- Evidence of utility
115Quiz
- Can a scale be valid and not reliable?
- Can a scale be reliable and not valid?
116Scale Development
- Generation items from focus groups/interviews
- Scaling decisions capture variation
- Face validity - check with experts and
participants - Standardize scale (evidence for validity,
reliability, utility) - Estimate correlates of concept
- Explore sensitivity to change over time
117Translation
- Forward translation (A to B)
- Backward translation (B to A)
- Conceptual equivalency across cultures
- Using of slang, idioms, etc.
118Data Analysis
119Data Analysis Why?
- Capture variability (variance) how the scores
vary across persons - Parsimony data reduction technique, how to
describe many data points in simple numbers - Discover meaning and relationships
- Explore potential biases in data (sampling)
- Test hypotheses
120Where to begin
- After data is collected, we begin a long process
of data entry cleaning - Data entry requires a code book be developed for
the statistical program you plan to use, such as
SPSS. - Data codebooks allow you to give your variables
names, values, and labels.
121Data Entry Cleaning
- Data entry is a BIG source of error in data
- Double data entry is one strategy
- Cleaning data looking for values outside the
ranges, e.g. age of 154 is probably a typo. - We examine frequencies, high score, low scores,
outliers, etc.
122Coding Variables
- Capture data in its most continuous form
possible. - Age 35 years - get the actual value
- vs.
- Check one _lt25
- _ 25-35
- _ 36-45
- _ gt45
123Dichotomous Variables
- Do not do this
- 1 Male
- 2 Female
-
- Do this!
- 1 male
- 0 female
- Why? Add function
124Dummy Coding
- Ethnicity
- 1 Black 2 White 3 Hispanic
- N-1 or 3-1 2 variables
- Black 1 Black 0 White and Hispanic
- White 1 White 0 Black and Hispanic
125Missing Data
- SPSS assigns a dot . to missing data
- SPSS often gives you a choice of pairwise or
listwise deletion for missing values. - Mean Substitution give the variable the average
score for the group, e.g. age, adds no variation
to the data set.
126Missing Data
- Pairwise just a particular correlation is
removed, best choice to conserve power - Listwise removes variables, required in repeated
measures designs.
127Measures
- Central Tendency
- Relationships
- Effects
128Measures of Central Tendency
- Mean arithmetic average score
- Standard deviation (SD) how the scores cluster
around the mean - Range high and low score.
- (Example M 36.4 years
- SD 4.2
- Range 22-45)
129Formulas
Mean
130Measures of Central Tendency
- Mean arithmetic average
- Median score which divides the distribution in
half (50 above and 50 below) - Mode the most frequently occurring value
- When does the meanmedianmode?
131Normal Curve very robust!
132Normal Curves
133Normal Curve(MeanMedianMode)
134Non-Normal Curves
135Scaling
- Discrete
- (qualitative)
- Nominal
- Ordinal
- Continuous (quantitative)
- Interval
- ratio
- Non-parametric
- (no assumptions required Chi square)
- Parametric
- (assumes the normal curve, e.g. t and F tests)
136Degrees of Freedom
- Statistical correction so one does not over
estimate
137Degrees of Freedom for ball 1?
138Degrees of Freedom for ball 2?
139Degrees of Freedom for ball 3?
140Degrees of Freedom
- Sample size (n-1)
- Number of groups (k-1)
- Number of points in time (l-1)
141Relationships or Associations
142Measures of Association Correlations
- Range -1 to 1
- Dimensions
- Strength (0-1)
- Direction ( or -)
- Definition a change in X results in a
predictable change in Y shared variation or
variance.
143Correlations
- Sample specific (each sample is a subset of the
population) - Unstable
- Dependent upon sample size
- Everything is statistically significant with a
very large sample size may not be clinically
significant. - Expresses relation not a causal statement
144Types of Correlations
- Pearson product moment r
- continuous by continuous variable
- Phi correlation
- discrete by discrete variable (Chi square)
- Rho rank order correlation
- discrete ranks by ranks
- Point-biserial
- discrete by continuous variable
- Eta Squared
145Estimate the value of the correlation
146Variance
147Shared variance r2
148Shared variance r2
149Types of Data Analyses
- Descriptive X? Y? Z?
- Measures of central tendency
- Correlational rx,y?
- Is there a relationship between X and Y?
- Measures of relationships (correlations)
- Causal ?X ? ?Y?
- Does a change in X cause a change in Y?
- Testing group differences (t or F tests)
150Testing Effects of Interventions
151Testing Group Differences
- t tests
- F tests (Analysis of Variance or ANOVA)
- (t tests are F tests with two groups)
152Types of tests of group differences
- Between groups
- (unpaired)
- Within groups
- (paired or repeated measures if two groups it is
also test-retest) - requires identified subjects
153Classic Experimental Design
- O1exp X O2exp
- ?
- R
- ?
- O1con O2con
- (pretest) (posttest)
-
- Group Between Factor
- Time Within Factor
-
154Tests of Significance
3 4
1 O1 X O2
2 O1 O2
155Testing Group Differences
- Between Variance
- F (or t)
- Within Variance
156Examining Variance
157Examining Variance No difference between the
means
158Examining Variance Big difference between means
159Examining Variance Three groups
160Types of Designs
- O1 O2 O3
- change within group over time, repeated measures
design -
161Types of Designs
- O1e X O2e
- O1c O2c
-
- change within group from O1e to O2e
- change between groups O2e and O2c
162How to analyze this design?
- O1e O2e O3e X O4e O5e O6e
- O1c O2c O3c O4c O5c O6c
- Two group repeated measures analysis of variance.
- One between factor (group) and one within factor
(time) with six levels.
163Post-test only design
- X O2e
- O2c
- Unpaired t test
- Null hypothesis
- H0 O2e O2c
- Alternative directional hypothesis
- H1 O2e gt O2c
164- Standard Deviation
- how scores vary around a mean
- Standard Error of the Mean
- how mean scores vary around a population mean
165Standard Error of the Mean Average of sample SDs
166Conceptual
- MeanE MeanC
- t
- standard error of the mean
167Assumptions of ANOVA
- Normal distribution
- Independence of measures
- Continuous scaling
- Linear relationship between variables
1683 X 2 ANOVA
- O1exp X1 O2exp
- ?
- R O1exp X2 O2exp
- ?
- O1con O2con
- One between factor group (3 levels)
- One within factor time (2 levels)
-
169Omnibus F Test
- O1exp X1 O2exp
- ?
- R O1exp X2 O2exp
- ?
- O1con O2con
- F test group Is there a difference among the
three groups? - F test time Is there a difference between time
1 and 2? - If yes to either question, where is the
difference? - Interaction Group by Time
-
170Post-hoc comparisons
- O1exp1 X1 O2exp1
- ?
- R O1exp2 X2 O2exp2
- ?
- O1con O2con
- Types Scheffé, Tukey control for degrees of
freedom in different ways compares all possible
two way comparisons - H0 O2exp1 O2exp2 O2con If you reject
Null, or F test is significant, then you can look
for two-way differences. - (O2exp1 O2exp2?) or (O2exp2 O2con?) or
(O2exp1 O2con?) -
171Tests of Significance
Non-parametric Parametric
Two-groups Paired Unpaired Wilcoxin Rank Mann-Whitney U Paired t test Unpaired t test
More than two-groups Repeated measures Independent groups Friedman test Kruskal -Wallis ANOVA Repeated measures ANOVA
172Galloping alpha
- Danger in conducting multiple t tests or doing
item-level analysis on surveys - alpha probability of rejecting the Null
hypothesis - alpha 0.05 divided by number of tests,
distributes alpha over tests - If conducting 10 t tests, alpha at 0.005 per test
(0.05/100.005)
173ANOVA
- ANOVA analysis of variance
- ANCOVA analysis of co-variance, includes Z
variable(s) - MANOVA multivariate analysis of variance (more
than one dependent variable) - MANCOVA multivariate analysis of co-variance,
includes Z variable(s).
174Multiple Regression Analysis
- Correlational technique
- Unstable values
- Sample specific
- Reliability of measures very important
- Requires large sample size
- Easy to get significance with large sample size
175Multiple Regression Analysis
- Attempts to make causal statements of
relationship - Y X1X2X3
-
- Y dependent variable (health status)
- X1-3 predictors or independent variables
- Health Status Age Gender Smoking
176Multiple Regression Questions
- What is the contribution of age, gender, and
smoking to health status? - How much of the variation in health status is
accounted for by variation in age, gender, and
smoking?
177Multiple Regression Analysis
- Creates a correlation matrix.
- Selects the most highly correlated independent
variable with the dependent variable first. - Extract the variance in Y accounted for by that X
variable. - Repeats the process (iterative) until no more of
the variance in Y is statistically explained by
the addition of another X variable.
178Health Status Age Gender Smoking
Health Status Y Age X1 r2 Gender X2 r2 Smoking X3 r2
Health Status Y 1 0.25 6 0.04 0 0.40 16
Age X1 1 0.11 1 .05 0
Gender X2 1 .20 4
Smoking X3 1
179Multiple Regression Shared Variance
Smoking 40
Age 25
Gender 4
180Multiple Regression
- Correlation results in a r
- Multiple regressions results in an r2
- R squared is the total amount of the variance in
Y that is explained by the predictors, removing
the overlap among the predictors.
181Multiple Regression
- Types
- Step-wise based upon highest correlation, that
variable is entered first (computer makes the
decision), theory building - Hierarchical choose the order of entry, forced
entry, theory testing
182Multiple Regression
- Allows one to cluster variables into Blocks.
- Block 1 Demographic variables
- (age, gender, SES)
- Block 2 Psychological Well-Being
- (depression, social support)
- Block 3 Severity of Illness
- (CD4 count, AIDS dx, viral load, OIs)
- Block 4 Treatment or control
- 1 treatment and 0 control
183Regression Analysis
- Multiple regression one Y, multiple Xs.
- Logistic regression Y is dichotomous, popular
in epidemiology, Ydisease or no disease odds -
risk ratio (not explained variance) - Canonical variate analysis multiple Y and
multiple X variables Y1Y2Y3X1X2X3 - -linking physiological variables with
- psychosocial variables.
184Multivariate Regression Models
- Path Analysis and now Structural Equation
Modeling - Software program AMOS
- Measurement model is combined with predictive
model - Keep in the picture the multicolinearity of
variables (they are correlated!) - Allows for moderating variables (direct and
indirect effects.
185Multiple Dependent Independent Path Analysis
Modeling
186Structural Equation Modeling
187Factor Analysis
- Exploration of instrument construct validity
- Correlational technique
- Requires only one administration of an instrument
- Data reduction technique
- A statistical procedure that requires artistic
skills
188Conceptual Types of Factor Analysis
- Exploratory see what is in the data set
- Confirmatory see if you can replicate the
reported structure.
189Factor Analysis
- Principal Components
-
- (principal factor
- or
- principal axes)
190Correlation Matrix of Scale Items Which items
are related?
Item 1 Item 2 Item 3 Item 4
Item 1 1 0.80 0.30 0.25
Item 2 1 0.40 0.25
Item 3 1 0.70
Item 4 1
191Factor Analysis
- An iterative process
- Factor extraction
192Factor Analysis
Factor I Factor II Factor III Communality
Item 1 0.80 0.20 -0.30 0.77
Item 2 0.75 0.30 0.01 0.65
Item 3 0.30 0.80 0.05 0.63
Item 4 0.25 0.75 0.20 0.67
Eigenvalue 2.10 2.05 0.56
var 34 30 10
193Definitions
- Communality Square item loadings on each factor
and sum over each ITEM - Eigenvalue Square items loading down for each
factor and sum over each FACTOR - Labeling Factors figments of the authors
imagination. Items 1 2 Factor I Items 3
4 Factor II.
194Factor Rotation
- Factors are mathematically rotated depending
- upon the perspective of the author.
- Orthogonal right angels, low inter-factor
correlations, creates more independence of
factors, good for multiple regression analysis,
may not reflect well the actual data. (varimax) - Oblique different types, lets factors
correlate with each other to the degree they
actually do correlate, some like this and believe
it better reflects that actual data, harder to
use in multiple regression because of the
multicolinearity. (oblimax)
195Summary Data Analysis
- Measures of Central Tendency
- Measures of Relationships
- Testing Group Differences
- Correlational
- Multiple regression as a predictive (causal)
technique. - Factor analysis as a scale development, construct
validity technique
196Ethical Guidelines for Nursing Research
- Vulnerability a power relationship between
health care provider and patient, family, or
client. - Vulnerable participants in research require more
protection from harm.
197Ethical Principles that Guide Research
- Beneficence doing good
- Non-malfeasances doing no harm
- Fidelity creating trust
- Justice being fair
- Veracity telling the truth
- Confidentiality protecting or safeguarding
participants identifying information
198Ethical Principles that Guide Research
- Confidential
- names kept guarded
- vs.
- Anonymous
- no identifiers
199