Slides to accompany Weathington, Cunningham & Pittenger (2010), Chapter 3: The Foundations of Research 1

Objectives Hypotheses and research Utility of hypotheses Types of hypotheses Measurement Reliability of measurement Validity of measurement Populations and samples 2

What is the difference between a theory and a hypothesis? 3

Hypotheses Are informed, specific predictions about how multiple variables will be related Based on theory or previous research Guide the progress of science –What data to collect –What research methods to use –How to analyze the data 4

A theory is… A broad set of general statements and/or claims that helps us to explain and predict events Not the same thing as a hypothesis May develop from a series of studies that test hypotheses 5

Role of Theory, Hypothesis, Research 6

Confirmatory Research When goal is to support or confirm the validity of an existing theory Hypotheses are used in these types of studies –Helps to protect against the Idols of the Theatre 7

Exploratory Research Focus is on examining an interesting phenomenon Prior theory is not required Caution against Idols of the Cave –Systemmatic observation can and should still be used 8

Utility of Hypotheses Guide to specific variables –Dependent (DV) vs. independent (IV) –Subject –Control Describe the variables’ relationship(s) –Causal or correlational? Link research to population 9

Types of Hypotheses - #1 Estimating population characteristics Inferring population details from sample 1.Data collected from sample 2.Descriptive statistics calculated 3.Infer to the population level If sample is truly representative Statistics are always estimates of parameters 10

Types of Hypotheses - #2 Correlational X and Y are related Positive vs. negative relationship Strength of the relationship 11

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Types of Hypotheses - #3 Difference among Populations Testing for differences between average members of separate populations 1 variable classifies members of groups, another variable is the DV of interest Sample statistics to make inferences about population-level differences 13

Types of Hypotheses - #4 Cause and effect X  Y Causal relationship supported if: 1.X before Y in time 2.X, Y are correlated 3.No other variables explain X  Y 14

Measurement is... a way of quantifying our observations objective replicable 15

Operational Definitions Formula for a construct that other scientists can use to duplicate it in future studies –Focus on observable signs of constructs –Not simple description 1.From hypothesis, identify the constructs 2.Choose a form(s) of measurement that allows us to address each construct best 16

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Measurement Scales Nominal = qualitative, categories –Measures the property of difference –Sorts objects/attributes into categories –Please indicate your sex: M F Ordinal = quantitative, ranks –Measure differences in magnitude –Grade scale  A > B > C, but how much? 18

Measurement Scales Interval, quantitative –Different, magnitude, and equal interval –Can add and subtract –Personality test Ratio, quantitative –Diff., magnit., equal ints., true 0 –Time on task 19

Reliability of Measurement Maximum consistency is the goal Challenges: –Measurement error - random –Bias error – consistent/constant Score = True score +/- Measurement error Table

Validity of Measurement How accurate is the measurement? –“Trueness” of the interpretations researchers make from the test scores Judgment call based on data –Face validity = authenticity? –Content validity = true behavior sampling? –Predictive/concurrent validity = X, Y relationship as expected? –Construct validity = accurate construct measurement? 21

Unreliable and Invalid 22

Reliable, but Invalid Measure can be reliable, but still be invalid 23

Reliable and Valid Measure must be reliable to be valid 24

Relating Samples to Populations Samples = smaller set of the larger population of interest –Representative vs. convenience Size and matching characteristics –Manageability Resources and costs 25

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Random Sampling Best way to generate a representative subset of a population Simple random sampling = Each member with an equal probability of being sampled 27

Random Sampling Essential when: 1. Goal is to estimate pop. characteristics 2. Trying to develop a test or intervention for a larger population 28

Random Sampling Not essential when we are interested in basic relationships among variables BUT, –Risky generalization –Requires multiple replications 29

Random Sampling from Population POPULATION SAMPLE INFERENCE 30

Random Assignment Not same as random sampling Assignment = actual placement in experimental groups Minimizes confounds and maximizes transfer of results to pop. –Good for internal and external validity 31

No Confounding Variables SAMPLE Control Group Experimental Group Differences are due to manipulation, not an extraneous variable because mood is randomly determined. 32

Confounding Variables SAMPLE Control Group Experimental Group Unclear if differences are due to manipulation or confounding variable (mood) 33

Random Assignment SAMPLE Control Group Experimental Group Now you can test these two groups for differences with less concern for confounds 34

Questions so far this chapter? What about so far in this course? 35

What is Next? **instructor to provide details 36