Download Multivariate Designs and Analyses

Class 22
• Other Multivariate Designs
• Developmental Designs
• Project data analysis
Multivariate Designs and Analyses
• Multiple Regression: goal is to explain as much of the variance in the criterion
variable (Y - the DV) based on a set of predictor variables (Xs).
• Discriminant Analysis: basically Multiple regression, with a categorical
dependent variable.
Activism Among Black South Africans:
C. Motjuwadi M.Sc.
Activism Among Black South Africans:
C. Motjuwadi M.Sc.
Motjuwadi’s Discriminant Analyses
Predicting Protest Participation
• gender, friend support, personal power, perceptions of injustice, & area
Predicting political Membership
• participation, gender
Predicting Detention
• participants, gender, area
Multivariate Designs and Analyses
• Canonical Correlation: looks at the relationship between a set of predictor
variables and a set of dependent variables by creating one new predictor
variable and one new dependent variable and relates these canonical variates.
• Multivariate Analysis of Variance (MANOVA). Used when you have more than
one independent variable and more than one dependent variable that you
believe are related (i.e., not independent).
• Log-linear analysis. This non-parametric statistic is basically a multivariate
Chi-squared.
Log-Linear Example
Multivariate Designs and Analyses
• Path Analysis. Uses multiple regression methods to examine hypothesized
causal relationships among variables with only correlational data. See how well
your theoretically derived model describes relationships among variables. Can
also compare competing theories about the relationships among variables.
Possible Causal Relationships
Possible Causal Relationships
Possible Causal Relationships
Possible Causal Relationships
Causal Antecedents of Attachment
Cross-correlation in Developmental Research
Multivariate Designs and Analyses
• Factor analysis is a multivariate form of data reduction. Factor analysis is
typically use to extract a relatively small number of underlying dimensions or
factors that can account for relationships among measures (see example from
text)
Multivariate Designs and Analyses are all very powerful and
some are easy statistics to use, and misuse.
To use these the techniques appropriately depends upon
careful research design and thought.
Data Collections Methods in
Developmental Psychology
Naturalistic Observations
Interviews
• structured
– questionnaires
– surveys
• unstructured
– clinical
Case Studies
Experimental:
• lab
• field
Quasi-experimental
• correlational
• ex post facto
Experimental Designs in Developmental Psychology
• Longitudinal Designs
• Cross-sectional Designs
• Cohort-Sequential (Cross-sequential, time-sequential) Designs
Longitudinal Designs
Examine developmental changes in one cohort followed over time
Within-Subjects Quasi-analytic design
Advantages:
• Process of development can be followed with individuals
Disadvantages:
• Large investment of time and money is required (especially if the age span of
interest is large)
• Subject attrition can be a problem
• Carryover effects (e.g., learning) can be a problem
• Differences among cohorts are not addressed
Cross-sectional Designs
Examine two (or more) ages (or cohorts) at one time
Between-Subjects Quasi-analytic design
Advantages:
• Fast and cheap
• No subject attrition
Disadvantages:
• Confounds age and cohort effects
• Unable to examine the process of development within individuals
Cohort-Sequential Designs
Combination of cross-sectional & longitudinal designs
• two (or more) cohorts, each studied at two (or more) ages. (Sometimes with
additional groups tested once to "fill in" the design.)
Mixed Quasi-analytic design
Advantages & Disadvantages
• This is a compromise solution with some of the advantages and disadvantages
of cross-sectional & longitudinal designs
• depending upon the length of the within cohort component and the number of
different cohorts.
Age, Education and I.Q.
Age, Education and I.Q.
Research Projects
Due: Next Wed (A)/Thurs(B)
• .ppt presentations due Monday (A) , Tuesday (B)
• hand in on disk with your names on it to Jill
Research Project Report
• in APA format
• all materials should be included as appendices
• review verb tense, SPSS data file, SPSS output
• All consent forms, raw data and/or coding sheets to be handed in (separate
bundle)
Research Project Presentations
• Approx. 10 minutes
• all partners must participate to receive credit
What we want to hear and understand
• Research context - existing relevant literature
• your hypotheses and design to test Hexp
– (IV/DVs, how & what you used to measure them)
• results (stats, figure)
• discuss what your results mean, relate to literature
• thoughtful suggestions for improvements/future research
• be ready for questions