Research Approaches and Methods of Data Collection Chapter 2 Research Approaches and Methods of Data Collection EI (2)

1) Descriptive: No Hypothesis Types of Research 1) Descriptive: No Hypothesis 2) Relational Studies: (Circumstantial Evidence) 3) Experiments - evidence of cause and effect. EI (2)

a difference between conditions is produced by researcher True Experiments objective measures a difference between conditions is produced by researcher all other variables held constant can make conclusions about cause and effect EI (2)

Independent Variable (IV): a variable that is presumed to cause changes to occur in another variable. In a true experiment we manipulate the IV to determine its effect on a Dependent Variable (DV). EI (2)

Confound Variables: anything other than the manipulated variable that is different between two conditions. Serves as an alternative explanation of the cause of differences between conditions. EI (2)

Mediating and Moderating Variables A Mediating Variable occurs between two other variables in a causal chain (e.g., anxiety causes distraction (mediating variable) which affects memory). Moderating Variables qualify a causal relationship as dependent on another variable - e.g., the impact of anxiety on memory depends on level of fatigue (moderating variable).

Criteria for Identifying a Causal Relation cause (IV) must be related to the effect (DV) (relationship condition) changes in IV must precede changes in DV (temporal order condition) no other explanation must exist for the effect (eliminate confounds)

Definition of an Experiment: objective observation of phenomenon that are made to occur in a strictly controlled situation in which one or more factors are varied and all others are kept constant.

Limitations of the Experimental Approach Can not test the effects of non-manipulated variables (many potential independent variables cannot be directly manipulated) Artificiality - refers to potential problems in generalizing findings from laboratory settings to the “real world”. Inadequate method of scientific inquiry

Experimental Research Settings Field experiments Advantage: may be easier to generalize findings. Disadvantage: less control of extraneous variables Laboratory experiments Advantage: more control than field experiments; Disadvantage: possibly more artificiality

Example: Eyewitness Testimony EI (2)

Quantitative Research Nonexperimental Quantitative Research Types correlational study cross-sectional and longitudinal studies natural manipulation research

Two (or more) variables measured on same person. Relation Studies Two (or more) variables measured on same person. Measures how well we can predict one variable if we know the value of the other variable. EI (2)

The magnitude of the correlation defines the degree of relationship. Pearson Correlation Statistic ( r ) measures the degree and the direction of the relationship. Ranges from -1 to +1 The magnitude of the correlation defines the degree of relationship. EI (2)

r = 1.0 - one variable is perfectly predicted from the other variable. r = 0.0 - no relationship. r = 1.0 - one variable is perfectly predicted from the other variable. The closer to +/- 1 the stronger the relationship. The closer to 0 the weaker the relationship. EI (2)

The sign (- or +) indicates the direction of the relationship. Positive - change in same direction. High on one variable predicts high on the other. Negative - change in opposite directions Low on one variable predicts high on the other. EI (2)

Correlations Do young hockey players take more penalties than old hockey players? r - statistical relationship twixt 2 variables (age & penalties) Penalty Minutes Age * (-) r

* * * * * * * * * * * * * Scatter Diagrams Data point. - point on diagram representing the co-occurrence of the two variables for each subject. Variable Y * * * * * * * * * * * * * Variable X EI (2)

* * * * * * * * * * * Line of Best Fit Straight line that fits the data so the summed distance between the line and each data point is lower than for any other possible line. * * * * * * * * * * * Variable Y Variable X EI (2)

Line of best fit applet Guessing Game EI (2)

Predict the correlation between these variables Concept Check Predict the correlation between these variables (High, Medium or Low? Negative or Positive) Weight and Height IQ and shoe size SAT scores and Grades in College Miles you have drive since a fill-up and amount of gas in your tank. Number of Storks and Birthrate in a town EI (2)

Correlation  Causation Directional Problem A could cause B B could cause A Third Variable ( C) Problem C could be causing both A and B EI (2)

Warning: People often try to use correlations as evidence of cause. This is bad science. EI (2)

Correlational Studies Two variables measured on same person. Relationship between 2 variables measured. Results indicate level and direction of prediction – does not indicate cause. Why do Correlational Research? - preliminary studies (e.g., epidemiology) ethical concerns (e.g., smoking and cancer) non-manipulatable variables (e.g.,sex) EI (2)

Path Analysis Correlational method of testing relationships among variables by seeing how well they fit some theoretical model Direct effects – when a variable directly impacts another Indirect effects –effect occurs through mediating variable

Hypothesized causal ordering for how Socio-economic status (SES), Intelligence (IQ) and Achievement Motivation (AM) Are related to GPA SES AM GPA IQ SES GPA AM IQ

Natural Manipulation Research - looks like experiments, but they are confounded. Variable is not manipulated by experimenter. e.g., comparisons of pre-existing or self-selecting groups. Males vs. Females Arts vs. Science Students EI (2)

If Males score better on Math tests than Females can I say being Male causes better math scores? Other explanations? EI (2)

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Developmental Change Studies Longitudinal Studies - Measure Same Subjects repeatedly at different Ages. Cross-Sectional Studies - measures different subjects at different ages. EI (2)

Age-Cohort - group of people of the same age, who share similar cultural and historic experiences. Cross-sectional - confounds Age and Age Cohort effects. EI (2)

Longitudinal - follow one Age cohort. - no Age X Cohort Confounds But … Costly, time consuming and do not necessarily generalize to other cohorts. Cohort-Sequential Design - incorporate both Longitudinal and Cross Sectional. EI (2)

Reading Readiness Scores in 1970 and 1985 EI (2)

Qualitative Research Interpretive Multimethod (triangulation) - interviews, introspective analysis, personal experience, observations, photographs ect. . . Conducted in a natural setting (field)

Methods of Data Collection Tests Questionaires Interviews Focus Groups

Descriptive Quantitative Studies Concerns - defining variables - sampling (who, where, and when) - Experimenter Bias - Reactive effect Observation can change behavior EI (2)

Naturalistic Observation Studies e.g., Ethnology EI (2)

Complete Participant Observer Studies e.g., Schouten & McAlexander, 1995

E.g., Gloria Steinem EI (2)

Steve Garasky and Brenda Lohman, pictured here in the observation room of Iowa State University’s Child Development Lab School, are authors of a new study finding increased levels of stress in adolescents are associated with a greater likelihood of them being overweight or obese. (Credit: Bob Elbert/ISU News Service) Marshmellow Study

Sampling Who, When and Where. Time Interval sampling Event Interval Sampling

Case Studies Detailed description of one person’s behavior Useful for Rare Cases e.g., Serial Killers, Rare Disorders Concerns 1) Experimenter Bias 2) Subject Bias (Reactivity) 3) Generalizability EI (2)

Profiling

Existing or Secondary Sources documents archived data bases physical data