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PSYC512: Research Methods Lecture 9. Brian P. Dyre University of Idaho. Lecture 8 Outline. Exam Next Week Will cover all lecture material, all material in Howell Chapters 1-5, broad concepts assumptions from Howell Chapters 6-11 What do I mean by “broad concepts?”

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Psyc512 research methods lecture 9

PSYC512: Research MethodsLecture 9

Brian P. Dyre

University of Idaho

PSYC512: Research Methods


Lecture 8 outline
Lecture 8 Outline

  • Exam Next Week

    • Will cover all lecture material, all material in Howell Chapters 1-5, broad concepts assumptions from Howell Chapters 6-11

    • What do I mean by “broad concepts?”

      • which tests are associated with which types of scaling properties of variables?

      • What variants of the tests exist and why?

      • What assumptions underlie the test?

  • Questions about material covered in Lecture 8

    • Describing Data

  • The Normal Distribution

  • Testing Hypotheses

  • Inferential Statistics

PSYC512: Research Methods


Review the normal distribution
Review: The Normal Distribution

  • What is the difference between a normal distribution and a standard normal distribution?

  • What is the difference between a raw score and a standardized score?

  • What are confidence intervals?

PSYC512: Research Methods


Testing hypotheses
Testing Hypotheses

  • Hypothesis testing is the process by which hypothetical relationships between intervening variables are assessed

  • Hypotheses are always tested relative to one-another or to a “null” hypothesis

  • Examples

    • Comparing groups

    • Assessing performance interventions

    • Assessing relationships between variables

PSYC512: Research Methods


Null hypothesis testing and inferential statistics
Null-Hypothesis Testing and Inferential Statistics

  • 2 possible realities

    • Relationship between your variables does not exist—a null relationship (Ho, the null hypothesis)

    • Relationship between the two variables in question actually exists (H1, the experimental or alternative hypothesis)

  • 2 possible decisions when looking at the data

    • Conclude that a relationship exists (reject the null hypothesis, Ho  DISCONFIRMATION!)

    • Conclude that no relationship exists (do not reject the null hypothesis  CONFIRMATION? NO!)

PSYC512: Research Methods


Null hypothesis testing and inferential statistics1
Null-Hypothesis Testing and Inferential Statistics

True State of the World

2 realities by 2 decisions form a 2 x 2 matrix of 4 possibilites

Decision

PSYC512: Research Methods


Null hypothesis testing and inferential statistics2
Null-Hypothesis Testing and Inferential Statistics

1 Population

  • Why might we observe a difference between two groups if no difference actually exists (null is true; samples are drawn from the same population)?

  • Each sample may have a unique mean due to sampling error

Frequency

m

2 samples

Frequency

PSYC512: Research Methods


Null hypothesis testing and inferential statistics3
Null-Hypothesis Testing and Inferential Statistics

2 Populations

  • How does this change if a difference actually exists between my groups?

  • Each sample has a unique mean that represents both sampling error and the differences between the 2 populations

Frequency

m1

m2

Frequency

PSYC512: Research Methods


Hypothesis testing probability and statistics
Hypothesis Testing: Probability and Statistics

  • Problem: How do we distinguish real differences or relationships from measurement noise?

  • Probability and statistics may be used to assess (descriptive statistics) or compare (inferential statistics) the relative magnitude of different types of variability

    • Effect (treatment) Variance

      • Variability due to relationship between variables or effect of different levels of independent variable (treatments)

      • “Good” variance that we want to maximize

    • Error Variance

      • Variability in measure due to factors other than the treatment

      • “Bad” variance that we want to minimize

PSYC512: Research Methods


Hypothesis testing inferential statistics
Hypothesis Testing: Inferential Statistics

  • All inferential statistics are evaluating this ratio:

    Effect (good) Variance

    Test statistic = --------------------------------------

    Error (bad) Variance

  • Example test statistics: Chi-square, t, F

  • These test statistics have known distributions that then allow us to estimate p, the probability of a Type I error (inappropriately rejecting the null hypothesis)

  • Decision to reject null is made by comparing p to some generally accepted criterion for Type I error probability, a = .05

PSYC512: Research Methods


How is the probability of a type i error p calculated it depends on
How is the probability of a Type I error, p, calculated? It depends on…

  • Scaling properties of your dependent variable (DV)

    • DV is interval or ratio parametric tests

    • DV is nominal or ordinal non-parametric tests

  • Research design

    • Experimental – test differences on measure between conditions or groups  t-test, ANOVA, sign test, Mann-Whitney

    • Correlational – test relations between different measures  Pearson product-moment correlation, point-biserial correlation, etc.

  • Manner in which you phrase your hypotheses

    • One tailed vs. two-tailed tests

PSYC512: Research Methods


Examples
Examples?

PSYC512: Research Methods


Next time
Next Time…

  • Topic: Review of broad concepts related to power, Chi-square, t-tests, and correlation

  • Be sure to:

    • Review Howell chapters 6-10

    • Bring questions!

    • Continue searching and reading the scientific literature for your proposal

PSYC512: Research Methods


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