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

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 ~

Reliability & Validity

Accurate measurement requires both Reliability

Consistency of measurement Criterion validity

Extent instrument actually measures what it claims to measure

Score on IQ test measures intelligence? pulse rate a measure of fear?

Important for internal & external validity ~