Advanced Quantitative Methods - PowerPoint PPT Presentation

Loading...

PPT – Advanced Quantitative Methods PowerPoint presentation | free to view - id: 1ea988-ZDc1Z



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Advanced Quantitative Methods

Description:

Types of Research Methods: (all have rules of evidence!) Quantitative ... Understanding rigor correct methods for any type of research design ... – PowerPoint PPT presentation

Number of Views:1445
Avg rating:3.0/5.0
Slides: 200
Provided by: williamh91
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Advanced Quantitative Methods


1
Advanced Quantitative Methods
  • William L. Holzemer, RN, Ph.D., FAAN
  • Professor, School of Nursing
  • University of California, San Francisco
  • bill.holzemer_at_nursing.ucsf.edu

2
Objectives
  • 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.

3
Assignments
  • PhD Students -individual assignments
  • MS Students group assignment
  • Mini-literature review
  • Outcomes Model
  • Substruction
  • Synthesis Tables
  • Summary

4
Nursing Nursing Science?
  • Definition of Nursing
  • American Nurses Association
  • Nursing is the assessment , diagnoses, and
    treatment of human responses

5
Definition 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.

6
Definition 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.

7
Common ElementsDefinitions of Nursing
  • Person (individual, family, community)
  • Health (Wellness Illness)
  • Environment
  • Nursing (care, interventions, treatments)

8
Nursing Science
  • The body of knowledge that supports
  • evidence-based practice

9
Nursing 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

10
Types 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
11
Outcomes Model for Health Care Research(Holzemer,
1994)
12
Outcomes Model
  • Heuristic
  • Systems model (inputs are outputs, outputs become
    inputs)
  • Relates to Donabedians work on quality of care
    (Structure, Process, and Outcome Standards)

13
Outcomes Model Nursing Process
14
Outcomes Model for Health Care Research
15
Outcomes Model Your assignment(Think about a
project or program of research)
16
Where 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

17
Types 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

18
Summary 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.

19
Some 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.

20
When 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?

21
Where 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.

22
Uses of Substruction
  • Critique a published study
  • Plan a new study

23
Substruction
  • 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

24
Substruction
25
Substruction Building Blocks or Statements of
Relationships
26
Statements of Relationships
27
Substruction Research Design Perspective
Focus of Study (RCT?)
28
Substruction 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
29
Substruction 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?
30
Scaling
  • 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

31
Issues
  • 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?

32
Is there a relationship between touch and pain
control, accounting for initial amount of
post-operative pain? rx,y.z
33
Literature 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?

34
Literature Review
  • What is known?
  • What is not known?
  • Resources
  • The Cochran Library
  • Library Data Bases
  • PubMed
  • CINYL

35
Literature ReviewHow to combine, synthesis, and
demonstrate direction?
36
Literature Review
37
Table 1. Outline of study variables related to
your topic
38
Table 2. Threats to validity of research studies
related to topic
39
Table 3. Instruments
40
Table 4. Power analysis for literature review on
topic.
41
Literature 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?

42
Research Designs
43
Research Design Qualitative
  • Ethnography
  • Phenomenology
  • Hermeneutics
  • Grounded Theory
  • Historical
  • Case Study
  • Narrative

44
Rigor in Qualitative Research
  • Dependability
  • Credibility
  • Transferability
  • Confirmability

45
Types of Quantitative Research Designs
  • We will focus on RIGOR
  • Experimental
  • Non-experimental

46
X,Y, Z notation
  • Z covariate
  • Severity of illness
  • X independent variable (interventions)
  • Self-care symptom management
  • Y dependent variable (outcome)
  • Quality of life

47
Types 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?

48
Rigor 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

50
Aim, 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.

51
Experimental Designs
52
Definition Experimental Design
  • There is an intervention that is controlled or
    delivered
  • There is an experimental and control group
  • There is random assignment to groups

53
Classic 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

54
Classic Experimental Design
  • O1exp X O2exp
  • ?
  • R
  • ?
  • O1con O2con
  • (pretest) (posttest)
  • The RCT is the Gold Standard for
  • Evidence-Based Practice

55
Randomization
  • 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

56
Goal
  • Statement of Causal Relationship

57
Conditions 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.

58
Controlling 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

59
Dimensions of Research Designs Groups Time
  • O1exp X O2exp
  • ?
  • Groups (n2 experimental control)
  • ?
  • O1con O2con
  • --------------------------
    ---------------------
  • ? Time (n2) ?
  • (repeated measures)

60
Dimensions of Research Designs Groups Time
  • Groups between factors
  • Time within factors

61
Types of Designs
  • O - descriptive, one time
  • O1 O2 O3 - descriptive, cohort, repeated
    measures)
  • O1 X O2 (not an experimental design!) -
    pre-post-test

62
Types of Designs
  • O1 X O2
  • O1 O2
  • RCT randomized controlled trial

63
Types 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)

64
Types of Design
  • O1 X1 O2
  • R O1 X2 O2
  • O1 O2
  • of groups? ___
  • points in time? ___

65
Types of Designs
  • Post-test only design
  • X O2
  • O2
  • What is the biggest threat to this post-test only
    design?

66
Types of Research Design
  • Experimental (true)
  • Quasi-Experimental (quasi)
  • No random assignment to groups

67
Design Validity
  • Statistical conclusion validity
  • Construct validity of Cause Effect (X Y)
  • Internal validity
  • External

68
Design 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?

69
Design 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?

70
Design 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?

71
Design Validity
  • External Validity
  • Threat of low generalizability to people, places,
    time
  • Can we generalize to others?

72
Building 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.

73
Sampling
  • Sample selection, not assignment

74
Terms
  • Population
  • Sample
  • Element
  • - All possible subjects
  • -A subset of subjects
  • - One subject

75
What do we sample?
  • People (e.g. subjects)
  • Places (e.g. hospitals, units, cities)
  • Time (e.g. season, am vs. pm shift )

76
Sampling 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

77
Types 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

78
Types 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)

79
Types 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

80
Sampling Error
81
How 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)

82
Sample Size and Sampling Error
83
Sample Size Calculations
  • Type of design
  • Accessibility of participants
  • Statistical tests planned
  • Review of the literature
  • Cost (time and money)

84
Strategies 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

85
Power 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.

86
Power Analysis Programs
  • SPSS Pakcage
  • nQuery Adviser Release 4.0 (most recent?)
  • http//www.statsolusa.com

87
Power
  • 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.

88
Effect 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

89
Effect 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

90
Effect Size
  • Meanexp Meancon
  • Effect Size
  • SD e c

91
Eta Squared (?2)
  • In ANOVA, it is the proportion of dependent
    variable (Y) explained.
  • Estimate of Effect Size
  • Similar to R2 in multiple regression analysis.

92
alpha
  • 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

93
Hypothesis Testing Is it true or false?
  • Null hypothesis H0
  • Mean (experimental) Mean (control)
  • Alternative hypothesis H1
  • Mean (experimental) / Mean (control)

94
Hypothesis Testing and Power
95
Quiz
  • 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?

96
Sample Loss in RCT
97
Measurement
  • If it exists, it can be measured
  • R. Cronbach

98
What we measure
  • Knowledge, Attitudes, Behaviors (KAB)
  • Physiological variables
  • Symptoms
  • Skills
  • Costs

99
Classical Measurement Theory
100
Type 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

101
Types 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.

102
Validity
  • 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

103
Theory-related Validity
  • Face validity
  • participant believability
  • Content validity (observable)
  • Blue print
  • Skills list
  • Construct validity (unobservable)
  • Group differences
  • Changes of times
  • Correlations/factor analysis

104
Criterion-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.

105
Reminder
  • 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?

106
Instrument Reliability
  • Question can you trust the data?
  • Stability change over time
  • Consistency within item agreement
  • Rater reliability rater agreement

107
Instrument 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

108
Cronbachs alpha
alpha
SD
109
Cronbach 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?

110
Internal Consistency Cronbachs alphaPerson A
Internally consistentPerson B Internally
inconsistent
111
Error 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

112
Reliability Values
  • Range 0 to 1
  • No negative signs like correlations
  • Cohens kappa and Scotts pi are always lower,
    i.e. 0.50, 0.60

113
Utility 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?

114
Reporting 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

115
Quiz
  • Can a scale be valid and not reliable?
  • Can a scale be reliable and not valid?

116
Scale 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

117
Translation
  • Forward translation (A to B)
  • Backward translation (B to A)
  • Conceptual equivalency across cultures
  • Using of slang, idioms, etc.

118
Data Analysis
119
Data 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

120
Where 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.

121
Data 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.

122
Coding 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

123
Dichotomous Variables
  • Do not do this
  • 1 Male
  • 2 Female
  • Do this!
  • 1 male
  • 0 female
  • Why? Add function

124
Dummy 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

125
Missing 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.

126
Missing Data
  • Pairwise just a particular correlation is
    removed, best choice to conserve power
  • Listwise removes variables, required in repeated
    measures designs.

127
Measures
  • Central Tendency
  • Relationships
  • Effects

128
Measures 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)

129
Formulas
Mean
130
Measures 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?

131
Normal Curve very robust!
132
Normal Curves
133
Normal Curve(MeanMedianMode)
134
Non-Normal Curves
135
Scaling
  • 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)

136
Degrees of Freedom
  • Statistical correction so one does not over
    estimate

137
Degrees of Freedom for ball 1?
138
Degrees of Freedom for ball 2?
139
Degrees of Freedom for ball 3?
140
Degrees of Freedom
  • Sample size (n-1)
  • Number of groups (k-1)
  • Number of points in time (l-1)

141
Relationships or Associations
142
Measures 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.

143
Correlations
  • 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

144
Types 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

145
Estimate the value of the correlation
146
Variance
147
Shared variance r2
148
Shared variance r2
149
Types 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)

150
Testing Effects of Interventions
151
Testing Group Differences
  • t tests
  • F tests (Analysis of Variance or ANOVA)
  • (t tests are F tests with two groups)

152
Types 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

153
Classic Experimental Design
  • O1exp X O2exp
  • ?
  • R
  • ?
  • O1con O2con
  • (pretest) (posttest)
  • Group Between Factor
  • Time Within Factor

154
Tests of Significance
155
Testing Group Differences
  • Between Variance
  • F (or t)
  • Within Variance

156
Examining Variance
157
Examining Variance No difference between the
means
158
Examining Variance Big difference between means
159
Examining Variance Three groups
160
Types of Designs
  • O1 O2 O3
  • change within group over time, repeated measures
    design

161
Types of Designs
  • O1e X O2e
  • O1c O2c
  • change within group from O1e to O2e
  • change between groups O2e and O2c

162
How 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.

163
Post-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

165
Standard Error of the Mean Average of sample SDs
166
Conceptual
  • MeanE MeanC
  • t
  • standard error of the mean

167
Assumptions of ANOVA
  • Normal distribution
  • Independence of measures
  • Continuous scaling
  • Linear relationship between variables

168
3 X 2 ANOVA
  • O1exp X1 O2exp
  • ?
  • R O1exp X2 O2exp
  • ?
  • O1con O2con
  • One between factor group (3 levels)
  • One within factor time (2 levels)

169
Omnibus 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

170
Post-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?)

171
Tests of Significance
172
Galloping 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)

173
ANOVA
  • 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).

174
Multiple 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

175
Multiple 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

176
Multiple 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?

177
Multiple 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.

178
Health Status Age Gender Smoking
179
Multiple Regression Shared Variance
Smoking 40
Age 25
Gender 4
180
Multiple 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.

181
Multiple 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

182
Multiple 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

183
Regression 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.

184
Multivariate 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.

185
Multiple Dependent Independent Path Analysis
Modeling
186
Structural Equation Modeling
187
Factor 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

188
Conceptual Types of Factor Analysis
  • Exploratory see what is in the data set
  • Confirmatory see if you can replicate the
    reported structure.

189
Factor Analysis
  • Principal Components
  • (principal factor
  • or
  • principal axes)

190
Correlation Matrix of Scale Items Which items
are related?
191
Factor Analysis
  • An iterative process
  • Factor extraction

192
Factor Analysis
193
Definitions
  • 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.

194
Factor 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)

195
Summary 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

196
Ethical 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.

197
Ethical 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

198
Ethical Principles that Guide Research
  • Confidential
  • names kept guarded
  • vs.
  • Anonymous
  • no identifiers

199
  • Best Wishes
About PowerShow.com