Module Code: H7APSTAT
Long Title Applied Statistics
Title Applied Statistics
Module Level: LEVEL 7
EQF Level: 6
EHEA Level: First Cycle
Credits: 10
Module Coordinator: Caoimhe Hannigan
Module Author: Caoimhe Hannigan
Departments: School of Business
Specifications of the qualifications and experience required of staff

Lecturer with PhD in Psychology or related cognate discipline
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Demonstrate a familiarity with various statistical tests and make decisions as to when such tests should be used.
LO2 Demonstrate a comprehensive understanding of using SPSS for data analysis.
LO3 Report the results of statistical analyses in accordance with APA rules.
LO4 Demonstrate a practical understanding of statistical analyses and be able to correctly interpret the meaning of statistical output and the results section of scientific papers.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

There are no additional entry requirements for this module. The programme entry requirements apply.
 

Module Content & Assessment

Indicative Content
Introduction to Inferential Statitics
• Role of inferential statistics. • Recap of descriptive statistics. • Central tendency, variability, normality, confidence intervals. • Hypothesis testing, Type I and Type II errors. • Entering data in SPSS, recoding and computing, calculating reliability and producing descriptive statistics.
Correlation Analysis
• Bivariate correlation analysis and its limitations. • An introduction to partial correlation analysis and controlling for covariates. • Conducting a Pearson correlation and a partial correlation in SPSS. • Reporting these results in APA format.
Multiple Regression Analysis I
• From correlation to regression (multivariate modelling). • Standard multiple regression analysis. • Applications of regression in psychological research. • Conducting a standard multiple regression analysis in SPSS. • Reporting these results in APA format.
Multiple Regression Analysis II
• Hierarchical modelling and the difference between hierarchical and standard multiple regression analysis. • Theoretical basis for hierarchical multiple regression. • Conducting a hierarchical multiple regression analysis in SPSS. • Reporting these results in APA format.
Logistic Regression Analysis
• Categorical outcomes compared continuous outcomes. • Binary compared to multinomial logistic regression. • The advantages of logistic regression and assumptions. • Pseudo R-values and odds ratios. • Model building for logistic regression. • Conducting a binary logistic regression analysis in SPSS. • Reporting these results in APA format.
t-Test
• A recap of t-tests (independent, dependent/paired samples, and one-sample t-tests). • Assumptions of t-tests. • Conducting an independent and paired samples t-tests in SPSS. • Reporting these results in APA format.
Analysis of Variance Testing 1
• From t-tests to ANOVAs. • The difference between between-groups and within-groups ANOVA. • Assumptions associated with each test. • Conducting a one-way between-groups ANOVA and a one-way within-groups ANOVA in SPSS. • Reporting these results in APA format.
Analysis of Variance Testing 2
• Multiple independent variables in ANOVA and interaction effects. • Multifactorial between-groups ANOVA. • The many applications of ANOVA testing. • Conducting a two-way between-groups ANOVA in SPSS. • Reporting these results in APA format.
Introduction to Latent Variable Modelling
• What is factor analysis/latent variable modelling? • Latent Variables and Observed Variables • Covariation and measurement error. • Factor loadings. • Steps to conducting factor analysis (e.g., extraction, rotation, and naming). • Conducting exploratory factor analysis in SPSS.
Assessment Breakdown%
Coursework50.00%
End of Module Assessment50.00%

Assessments

Full Time

Coursework
Assessment Type: Continuous Assessment % of total: 50
Assessment Date: n/a Outcome addressed: 1,4
Non-Marked: No
Assessment Description:
Students are presented, in class, with an unseen research paper that uses a statistical test that they have learnt about previously (e.g., standard multiple regression analysis). The students are required to read the research paper and answer short questions regarding the analysis and findings presented in the paper. These questions evaluate knowledge of the relevant statistical test. Students will have 90 minutes to complete this assessment.
End of Module Assessment
Assessment Type: Terminal Exam % of total: 50
Assessment Date: End-of-Semester Outcome addressed: 1,2,3,4
Non-Marked: No
Assessment Description:
Students are presented with a dataset and a brief description of a study. The student is required to determine and carry out the appropriate statistical analyses using SPSS, based on the objectives of the study. The student uses the dataset to carry out these analyses and must write up the results in accordance with the APA guidelines. Students will have 2 hours to complete this assessment.
No Workplace Assessment
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
Reassessment Description
Students must attempt all assessment components. If the student fails the module overall, they must repeat all failed, missed, or deferred assessments.

NCIRL reserves the right to alter the nature and timings of assessment

 

Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Classroom and demonstrations 24 Per Semester 2.00
Practical Other: Practical Classes 24 Per Semester 2.00
Independent Learning Independent learning 202 Per Semester 16.83
Total Weekly Contact Hours 4.00
 

Module Resources

Recommended Book Resources
  • Julie Pallant. SPSS Survival Mannual, 7th Ed. McGraw Hill.
  • Andy Field. (2017), Discovering statistics using IBM SPSS statistics, 5th Ed. Sage.
Supplementary Book Resources
  • Howitt, D. & Cramer, D.. (2017), Introduction to statistics in psychology with SPSS., 7th. Pearson.
Recommended Article/Paper Resources
Other Resources
Discussion Note: