 # PSYC512: Research Methods PSYC512: Research Methods Lecture 10 Brian P. Dyre University of Idaho.

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PSYC512: Research Methods PSYC512: Research Methods Lecture 10 Brian P. Dyre University of Idaho

PSYC512: Research Methods Lecture 10 Outline Exam Tuesday of Next Week Exam Tuesday of Next Week Will cover all lecture material, all material in Howell Chapters 1-5, broad concepts assumptions from Howell Chapters 6-11 Will cover all lecture material, all material in Howell Chapters 1-5, broad concepts assumptions from Howell Chapters 6-11 Questions about material covered in Lecture 9 Questions about material covered in Lecture 9 The Normal Distribution The Normal Distribution Testing Hypotheses Testing Hypotheses Inferential Statistics Inferential Statistics

PSYC512: Research Methods Hypothesis Testing: Inferential Statistics All inferential statistics are evaluating this ratio: All inferential statistics are evaluating this ratio: Effect (good) Variance Test statistic = -------------------------------------- Error (bad) Variance Error (bad) Variance Example test statistics: Chi-square, t, F 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) 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,  Decision to reject null is made by comparing p to some generally accepted criterion for Type I error probability, 

PSYC512: Research Methods How is the probability of a Type I error, p, calculated? It depends on… Scaling properties of your dependent variable (DV) Scaling properties of your dependent variable (DV) DV is interval or ratio  parametric tests DV is interval or ratio  parametric tests DV is nominal or ordinal  non-parametric tests DV is nominal or ordinal  non-parametric tests Research design Research design Experimental – test differences on measure between conditions or groups  t-test, ANOVA, sign test, Chi-square, Mann-Whitney Experimental – test differences on measure between conditions or groups  t-test, ANOVA, sign test, Chi-square, Mann-Whitney Correlational – test relations between different measures  Pearson product-moment correlation, point-biserial correlation, etc. Correlational – test relations between different measures  Pearson product-moment correlation, point-biserial correlation, etc. Manner in which you phrase your hypotheses Manner in which you phrase your hypotheses One tailed vs. two-tailed tests One tailed vs. two-tailed tests

PSYC512: Research Methods Four Questions (with subparts) to Guide Your Choice of Inferential Test What are the scaling properties of my measure(s) or dependent variable(s)? What are the scaling properties of my measure(s) or dependent variable(s)? How many measures do I have? How many measures do I have? If nominal  how many categories (dichotomous, 2, or non- dichotomous, > 2)? If nominal  how many categories (dichotomous, 2, or non- dichotomous, > 2)? Is/Are my manipulations or independent variable(s) qualitative (discrete categories) or quantitative? Is/Are my manipulations or independent variable(s) qualitative (discrete categories) or quantitative? If qualitative, how many levels? Note: Often quantitative variables are manipulated as discrete categories If qualitative, how many levels? Note: Often quantitative variables are manipulated as discrete categories How many manipulations (factors) do I have? How many manipulations (factors) do I have? Are the factors manipulated independently and exhaustively (factorial design)? Are the factors manipulated independently and exhaustively (factorial design)? Are the hypotheses directional or not? Are the hypotheses directional or not? Is effect size (strength of relationship) important to my hypotheses? Is effect size (strength of relationship) important to my hypotheses?

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