1 / 36

Foundations of Research Objectives: Theory, Hypotheses, and Measurement

This presentation explores the foundations of research objectives, including the difference between theories and hypotheses, the importance of measurement, and the reliability and validity of measurement. It also covers different types of hypotheses and their utility in guiding research. The presentation concludes with an overview of sampling techniques and the role of random assignment in experimental research.

violettel
Download Presentation

Foundations of Research Objectives: Theory, Hypotheses, and Measurement

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

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

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

  4. 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

  5. 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

  6. Role of Theory, Hypothesis, Research

  7. 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

  8. 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

  9. 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

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

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

  12. 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: • X before Y in time • X, Y are correlated • 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 • From hypothesis, identify the constructs • Choose a form(s) of measurement that allows us to address each construct best

  16. 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?

  17. 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

  18. Reliability of Measurement • Maximum consistency is the goal • Challenges: • Measurement error - random • Bias error – consistent/constant Score = True score +/- Measurement error • Table 3.5

  19. 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?

  20. Unreliable and Invalid

  21. Reliable, but Invalid Measure can be reliable, but still be invalid

  22. Reliable and Valid Measure must be reliable to be valid

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

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

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

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

  27. Random Sampling from Population INFERENCE POPULATION SAMPLE

  28. 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

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

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

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

  32. Questions so far this chapter?What about so far in this course?

  33. What is Next? • **instructor to provide details

More Related