investigate.educate.inform. - kean universityorsp.kean.edu/documents/research design advanced...
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investigate. educate. inform.
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Research Design › What drives your research design? › The battle between Qualitative and
Quantitative is over › Think before you leap › What SHOULD drive your research design.
Advanced Statistical Techniques › Why a t test just doesn’t cut it anymore › Regression Models and SEM
Statistical Decisions › What drives your analysis choices? › What SHOULD drive your analysis choices.
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Due to lack of research and statistical knowledge/education researchers naively: › use incorrect statistical methods › misinterpret statistical results › use unsound research designs › can’t recognize other poor research
Techniques change and adapt with increased technology and complexity of the research
Grant and Publication Reviewers are more educated and looking for these techniques
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Ethnographies › intact cultural group in a natural setting
Grounded Theory › constant comparison of data to refine theory
Case Studies › study an individual, event, or activity
Phenomenological Research › identify with participants to develop patterns
Narrative Research › provide stories about their lives
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Descriptive or Non-Experiments › Case Studies, Content Analysis, Survey
Research, Naturally Occurring Designs, Field Studies
Quasi-Experiment Designs › Nonequivalent Groups, Interrupted Time
Series, Correlational Experiments
› Between Subjects, Within Subjects, Mixed Models
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Describe the state of nature at a point in time.
Map out a situation or set of events in order to describe what is happening behaviorally.
Does not directly concern itself with causal explanations, except perhaps speculatively.
Answer who, what, when, where, how.
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Identification of causes “X is responsible for Y” Four Critical Elements
› Random selection of Ss › Random assignment of Ss to treatments › Experimenter manipulation of treatments › Experimenter control
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Simplest of the experimental designs › Two Group Design › Multiple Group Posttest Design › Multiple Group Pretest Posttest Design › Ex Post Facto Design › Multiple Group Time Series Design
Factorial Designs Soloman Four Group Design Multivariate Designs
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“Resembling” Fails to allocate the treatments at
random Common Types
› Nonequivalent Groups Designs › Interrupted Time Series Designs › Correlational Designs
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Sequential › expand the findings of one method with
another method Concurrent
› converge both to provide a comprehensive analysis of the research problem
Transformative › use theory as perspective changes within a
design that contains both qualitative and quantitative
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Research design defines the statistical analysis you use through your › Within and Between Subjects Designs › Measures of Interest › Sample Size
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Between Subjects › Ss exposed to one treatment group each › Free of carry over effects › Less control over subject variables
randomization matching selection of homogeneous subject population
Within Subjects (Repeated Measures) › Each Ss receives all treatments in turn › Control for subject variables (individual differences) › Reduces number of subjects needed › Before and After Design
possibility of carry over effects counter-balancing
Mixed Models Factorial Designs
› All possible combinations of levels of the variables are included in the experimental design.
› Main Effects and Interactions
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Research goals, objectives Prior Research Quality research questions and
hypotheses Validity and Reliability Resources Feasibility
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Appropriateness or meaningfulness Measures what it is designed to measure Internal and External
› Instrument Reactivity › Unreliability of instruments › Invalidity of Instruments › Instrument change over time › Differential subject loss › Bias in Assignment of Subjects to Treatment › Hawthorne Effects › Non-representative samples
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Consistency or stability of a measuring device
Calibration Measurement error Can be increased by multiple
measurements or multiple measures › Test-Retest › Equivalent Forms
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Your research design drives the inferences and conclusions you can draw from your data and analyses.
Pitfalls to Avoid › Assuming the only possible reason for
change is the intervention › Experimenter/Interviewer Bias › Choosing the wrong groups to compare › Claiming that results from a small scale study
also apply to a wide group
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Crucial to the understanding of underlying scientific phenomena and to advancing knowledge and practice.
Statistics give more accurate information
than your own experience.
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Descriptive vs. Inferential
Parametric vs. Nonparametric
Univariate, Bivariate, and Multivariate
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Correlations Regressions T-tests Analysis of Variance (ANOVA) Analysis of Covariance (ANCOVA)
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Multiple Regression MANOVA Cluster Analysis Discriminant Analysis Factor Analysis Multidimensional Scaling Principle Components Analysis
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Examination of past research
Does effect, association, or difference, really exist?
Need at least 2 studies….
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Most of what we study is much more complicated than a simple X is different than Y relationship.
With the complexity of human research, relationships between variables often consist of multiple confounding variables that should be taken into account
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Complex research questions and methodologies require complex statistics
Journals and Grant Funders are expecting more complex research
We need complexity to build on the available knowledge in our fields
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Multiple Regression › Linear, Logistic, Negative Binomial, etc › Hierarchical, Stepwise, Forward/Backward,
Weighted Path Analysis
› Moderation, Mediation Structural Equation Models Partial Least Squares Models Multi-Level Models
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Variable types Sample size Distribution of the data Participant demographics Research question Relationships among key variables Assumptions Testing
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Quantitative vs. Qualitative
Independent vs. Dependent
Continuous › Ratio, Interval
Categorical/Nominal › Binary, Dichotomous
Ordinal
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Measures of Central Tendency › Mean – the average outlier sensitive
› Median – central value of a set of ranked values; 50th percentile; not influenced by outliers
› Mode – most frequent value; not influenced by outliers
Measures of Dispersion - Statistics that show the amount of variation or spread in the scores, or values of, a variable. › Variance › Standard Deviation › Standard Error
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What is the effect of breast feeding on child’s BMI?
IV = ?
DV =?
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What is the effect of breast feeding on child’s BMI?
IV = Breast Milk vs. Formula
DV = BMI
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What is the effect of breast feeding on child’s BMI?
IV = ?
DV =?
Measure when they leave the hospital, 2 months later, 1 year later, and at 5 years
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What is the effect of breast feeding on child’s BMI?
IV = Breast Milk vs. Formula
DV = BMI
Measure when they leave the hospital, 2 months later, 1 year later, and at 5 years
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