Data Analysis in Psychology
- Abuses of statistics
- Organizing and describing data
- Measures of central tendency
- Measures of variability
- Standard scores
- Description of frequency distributions
- Properties of normal distributions
- Central Limit Theorem
- Introduction to probability concepts
- Null hypothesis significance testing
- Analysis of Variance and t-tests
- Correlation methods
- Regression and prediction
- Nonparametric statistical methods
- Statistical significance versus practical importance
- Measures of effect size and confidence intervals
This course will employ a number of instructional methods to accomplish its objectives and will include some of the following:
- audio visual materials
- small group discussion
- research projects
- computer based tutorial exercises
Evaluation will be carried out in accordance with Douglas College policy. Evaluation will be based on course objectives and will include some of the following: quizzes, multiple choice exams, essay type exams, term paper or research project, computer based assignments, etc. The instructor will provide the students with a course outline listing the criteria for course evaluation.
An example of one evaluation scheme:
|Computer based homework assignments||10%|
At the conclusion of the course the successful student will be able to:
- Define, describe and distinguish between descriptive and inferential statistics, and identify in what contexts each is appropriate.
- Define, describe, distinguish between, and demonstrate ability to calculate, by hand and/or using statistical software, various key descriptive statistical terms, such as: empirical distribution, frequency distribution, histogram, percentile, quartile, measures of central tendency (median, mode, mean), sum of squares, measures of variance (range, variance, standard deviation, within-groups variance, between-groups variance), standard score/z-scores, covariance, Pearson r, regression coefficient, model, and effect size (e.g., Cohen’s d, Eta squared).
- Define, describe, distinguish between, and demonstrate ability to calculate, by hand and/or using statistical software, various key research designs and inferential statistical terms, such as: scales of variables (nominal, ordinal, interval, ratio), independent variable (IV), dependent variable (DV), theoretical distribution, population, sample, statistic, random sampling, estimator, estimate, probability distribution, parameter, normal distribution, t distribution, F-distribution, Chi-square distribution, sampling distribution, null hypothesis significance testing (NHST), null hypothesis, alternative hypothesis, p-value, alpha, beta, power, type 1 error, type 2 error, critical value, statistical significance, and confidence interval.
- Describe and explain the logic of inferential statistics. This includes being able to explain what a p-value is, what statistical significance means, and how various factors, such as sample size, effect size, alpha and violation of assumptions, influence the p-value and statistical significance.
- Calculate, interpret, explain the rationale for, and analyze the assumptions for appropriate test statistics and p-values for situations such as the following: a) there is one IV that has two or more levels and the DV is a scale variable – the IV may be a between or within subjects IV, b) there may be a linear relationship between two variables and the null hypothesis is that the population relationship is 0, c) there are observed frequencies for one or two variables and the null hypothesis is that the distribution of observed frequencies is non-proportional.
- Interpret basic research results as published in academic journals.
Textbook(s) such as the following, the list to be updated periodically.
- Aron, A., Coups, E.J., & Aron, E. N. (2013) Statistics for psychology (6th ed.) Upper Saddle River, NJ: Pearson Education.
- Howell, D. C. (2017) Fundamental statistics for the behavioral sciences (9th Ed.) Pacific Grove, CA: Brooks/Cole.
- Gravetter, F.J., Wallnau, L.B. & Forzano, L.B. (2018). Essentials of statistics for the behavioral sciences (9th ed.). Boston, MA: Nelson/Cengage.
- a C or better in Foundations of Math 11 or Pre-calculus 11 (or equivalent)
Courses listed here must be completed either prior to or simultaneously with this course:
- No corequisite courses
Courses listed here are equivalent to this course and cannot be taken for further credit:
- No equivalency courses
Course Guidelines for previous years are viewable by selecting the version desired. If you took this course and do not see a listing for the starting semester / year of the course, consider the previous version as the applicable version.
These are for current course guidelines only. For a full list of archived courses please see https://www.bctransferguide.ca
|Institution||Transfer Details for PSYC 2300|
|Athabasca University (AU)||AU SOCI 301 (3)|
|Capilano University (CAPU)||CAPU PSYC 213 (3)|
|Coast Mountain College (CMTN)||CMTN PSYC 2XX (3)|
|College of New Caledonia (CNC)||CNC PSYC 201 (3)|
|Kwantlen Polytechnic University (KPU)||KPU PSYC 2300 (3)|
|Langara College (LANG)||LANG PSYC 2321 (3)|
|Okanagan College (OC)||OC PSYC 2XX (3)|
|Simon Fraser University (SFU)||SFU PSYC 210 (3)|
|Thompson Rivers University (TRU)||TRU PSYC 2100 (3)|
|Trinity Western University (TWU)||TWU PSYC 207 (3)|
|University of British Columbia - Okanagan (UBCO)||UBCO PSYO 271 (3)|
|University of British Columbia - Vancouver (UBCV)||UBCV PSYC 218 (3)|
|University of Northern BC (UNBC)||UNBC PSYC 315 (4)|
|University of the Fraser Valley (UFV)||UFV PSYC 110 (3) or UFV STAT 104 (4)|
|University of Victoria (UVIC)||UVIC PSYC 300A (1.5)|
|Vancouver Community College (VCC)||DOUG MATH 1160 (3) or DOUG PSYC 2300 (3) = VCC MATH 1111 (3)|
|Vancouver Island University (VIU)||VIU PSYC 204 (3)|
All course activities will be asynchronous. Students will not be required to be online at specific scheduled times.