variation, validity, & variables
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Variation, Validity, & Variables. Lesson 3. Research Methods & Statistics. Integral relationship Must consider both during planning Research Methods How data are collected What kind of data Statistics Analysis & interpretation depends on data & how it is collected ~. Scientific Validity. - PowerPoint PPT PresentationTRANSCRIPT
Research Methods & Statistics Integral relationship
Must consider both during planning
Research Methods How data are collected What kind of data
Statistics Analysis & interpretation
depends on data & how it is collected ~
Scientific Validity
Scientific conclusions About relationships b/n variables
Validity Soundness, legitimacy, truth
Internal validity About cause & effect
External (ecological) validity About broad applicability ~
How are data collected? 2 scientific approaches
Same or similar statistical analysis NOT same confidence in conclusions
Observational methods Observe co-occurrence of variables Naturalistic observation, case studies,
archival research, surveys, etc. Experimental method
Manipulate a variable observe effect on another variable ~
The Experimental Method At least 2 variables:
Independent (IV) & Dependent (DV) At least 2 groups (levels of IV)
control group - no treatment experimental - receives treatment random assignment to groups
Control extraneous variables Which might also affect DV Weakens internal validity ~
Experimental Variables Independent (IV)
Predictor (or cause) Manipulated
Dependent (DV) Outcome (or effect) Measured
Extraneous variables Or confounding Might also affect outcome (DV) ~
Variation within an Experiment
Systematic Variation due to manipulation of IV Difference between groups
Unsystematic Individual differences Variation due to random or
uncontrolled variables Potentially confounding variables ~
Variation within an Experiment
varianceicunsystemat
variancesystematic statistictest
IVby explainednot variance
IVby explained variance statistictest
sindividualbetween difference
groupsbetween difference statistictest
Internal Validity
Legitimacy of conclusions about cause & effect
High internal validity Confident that only changes in IV
cause change in DV Low internal validity
Confounding variables influence outcome ~
Randomization
Important for validity Helps avoid bias
Random sampling (or selection) Selection of participants for study Representative sample from population external validity
Random assignment to condition (groups) Minimize biasing of groups internal validity ~
Observational vs. Experimental Internal vs External validity
Inverse relationship based on control Observational?
internal vs external Cannot determine causality
Experimental internal vs external Establishes cause & effect relationships
For useful conclusions need both ~
Observational vs. Experimental:
Statistical Methods Misperception
Observational only correlational Experiment hypothesis tests Method not sole determinant of
analysis Strength of cause & effect conclusions Observational weaker Experiment stronger ~
Planning Research Observational or experimental research Research design
Between-groups or within-subjects Operational definition of variables
Data categorical or quantitative Statistical analysis
Depends on all of the above ~
What are data?
Information from measurement datum = single observation
Variables Dimensions that can take on
different values IQ, height, shoe size, hair color
Is not the same for all individuals being measured ~
Measuring Variables Operational definitions
Variables often abstract Intelligence, anxiety, fitness, etc. Need to objectively measure
Hypothesis: Exercise increases fitness Independent: Exercise
Operational definition? Dependent: fitness
Operational definition? ~
Levels of Measurement Limits type of statistical analysis possible Qualitative
Categorical Frequency data Discrete: only whole numbers
Quantitative Continuous or discrete represents magnitude infinite # intermediate values ~
Levels of Measurement: Categorical
Nominal scale categorical order NOT meaningful can assign arbitrary values
Ordinal scale Categorical + meaningful order No info about magnitude of
differences If assign numerical value, must
reflect order ~
Levels of Measurement: Quantitative Interval scale (numbers)
Continuous or discrete Equal intervals equal differences
Ratio scale same characteristics as interval Ratios of values must be meaningful for
magnitude scale must have true zero point
Most statistics: interval/ratio treated the same ~
Levels of Measurement: SPSS
Variable view tab Formatting of variable Measure
Nominal scale Ordinal scale Scale
Interval & ratio Reminder: IV must be nominal for most
statistical tests ~
Measurement Error Discrepancy
between actual value of observation and the reported value
Sources of measurement error Sensitivity of measuring instrument Conscientiousness of observer Surveys: inaccurate or untruthful Low reliability of instrument
unsystematic variation ~