代做PSYC5180 Quantitative Methods I Fall 2024

PSYC5180               Quantitative Methods I

Fall 2024

Statistics is a set of tools for making reasoned arguments about data.

Textbook: Moore, D.S., McCabe, G.P. , & Craig, B.A. (2012).  Introduction to the practice of statistics.  (7th Ed.)(NOT the most recent edition).  New York: Freeman.   “MM&C” Other readings are on Canvas.

Class meets Tuesday 10:00-12:00 and Friday 2:30-3:30 in 180-NI.  Most Fridays will be problem sessions.

Syllabus (dates are approximate)

Week of:              Topic                                                    Reading

9/6 Fri. only	Introduction.  Introduction to R.	Web tutorial sections 1-3, 5 https://www.cyclismo.org/tutorial/R/index.html
9/10	Descriptive Statistics, Correlation, Regression	MM&C Ch.1&2;
9/17	Probability, Probability Distributions	MM&C Ch. 4 ; Strogatz (2010)
9/24	Sampling Distributions, Statistics	MM&C Ch. 5 pp. 297-308
10/1	Sampling Distribution of the Mean, Central Limit Theorem, Estimation	Ch. 6 pp. 342-354
10/8	Hypothesis Testing for Means (Fisher & Neyman-Pearson approaches; z-Tests)	MM&C Ch. 6 pp. 360-396;
10/15	Student’s t-Tests for Means	MM&C Ch. 7.
10/22	Confidence Intervals & Recap
10/29	Midterm
11/5 ELECTION DAY	Counts & Proportions: Estimation, Sampling Distributions, Hypothesis Testing	MM&C Ch. 5 pp. 312-332; MM&C Ch. 8
11/12	Effect size and power - practical Issues	MM&C pp. 389-397, pp 419-420, pp. 463- 464; Cohen (1983).
11/19	Chi-square distribution and Pearson’s approximate Chi-square test	MMC Ch. 9
11/26	Introduction to resampling methods	MM&C Ch. 16; Wilcox (2003; Chap. 7)
12/3	Hypothesis Testing: fundamental issues	Cohen(1994); Loftus (1996); Simmons et al (2011); Gigerenzer(2018); Button et al. (2013)
12/10	Backup Day & Takehome Final Exam

Required software: R and G* Power

We will be using R for calculations and plotting.

Other potential topics depending on time and interest: 

Graphical Analysis and Error Bars

Classical Nonparametric Methods (statistics on ranks)

Introduction to Bayesian Methods

Specific Course Goals/Study Questions (bold items are relevant to the midterm)

Demonstrate your ability to use simple mathematical notation (subscripted variables, summation

signs, exponents).

You should be able to correctly interpret stem-and-leaf and box and whiskers graphs, scatter plots, error bars.

What is a random variable?  If you take a questionnaire response from 20 subjects and calculate the

mean with an eye towards estimating some population effect, what is/are the random variable(s) you are most interested in?

What is probability? What is a probability distribution?  Cumulative probability distribution? Quantile?

What is a correlation coefficient?  A regression line?  Interpret r2  in terms of variances.

What is a conditional probability?  a joint probability? Make a 2x2 and a 3x3 table with entries representing simple, joint, and conditional probabilities of events.  What is Bayes Rule?

Define “statistic” and “test statistic”.

Compare and contrast three types of distributions: sample, sampling, and population distributions.

What is “statistical estimation”?  What is meant by a “good estimator”?  Name two widely-used methods for finding ‘good’ estimators.

What is the central limit theorem?  Why is the normal distribution so important?

What is a “standard error”?  Why are most test statistics of the form “statistic”/“standard error of the statistic”?

Give, in the form. of a syllogism, the pseudo-logic behind statistical hypothesis tests.

Describe the Neyman-Pearson analysis of statistical decisions, including the two formal types of possible errors.  Describe effect size and power, conceptually.

What is “statistical robustness”?

You should be able to calculate and interpret: Pearson correlation coefficients,  z tests,  t tests (3 types), sign tests, Chi-square tests, measures of effect size and power, permutation tests, randomization  tests, bootstrap confidence intervals

What is the relationship between a statistical “p” value, effect size, and sample size? What is the bootstrap?  What’s the logic behind resampling statistics?

What is the relationship, if any, between inferential statistics and experimental replication?   Compare and contrast the Neyman-Pearson and Fisherian approaches to hypothesis testing.

Discuss the controversy over statistical hypothesis testing, experimenter degrees of freedom, and the ‘crisis of replicability.’ Discuss the probability of replicating a measured effect .