ANTALYA BİLİM UNIVERSITY
Course Information Package

PSYC 2001 - Statistics I

Basic Information

Course Code:
PSYC 2001
Course Name:
Statistics I
Language of Instruction:
English
Course Type:
Class
Course Level:
Bachelor
ECTS:
7.00
Instructor:
Dr. Öğr. Üyesi Demet KARA

Course Objectives

This course aims to familiarize students with a range of statistical analysis techniques, from basic to advanced. It focuses on quantitative statistical methods, introduces students to SPSS, and emphasizes the application of appropriate statistical tests in relation to various research methods.

Course Content

This course introduces psychology students to the fundamentals of statistical analysis used in research. Students will learn key concepts such as data distribution, measures of central tendency, variability, and statistical significance. The course covers essential techniques for analyzing data, including frequency analysis, correlation, regression, and t-tests, with a focus on practical application using SPSS software. Through lectures, quizzes, and hands-on exercises, students will develop the skills necessary to interpret data and apply statistical methods in psychological research.

Prerequisites / Corequisites

PSYC 1001 or PSYC 1002 and PSYC 1004

Course Books / Materials / Recommended Resources

Andy Field. Discovering statistics using IBM SPSS Statistics (4th Edition)

Learning Outcomes

Code Description
LO1 Design studies to collect valid and reliable data.
LO2 Produce and interpret graphical and numerical summaries of data.
LO3 Understand and use theoretical distributions to assign probabilities to events.
LO4 Graphically and numerically describe the relations between quantitative variables.
LO5 Infer properties of a population from a sample and predict values of an outcome variable by using a set of indicator variables.

Weekly Course Content

Week Content
1 Introduction and Review of the Syllabus
2 Introduction to quantitative methods: research question, hypothesis, prediction, variables, statistical significance
3 Frequency distributions and Central tendency
4 Shapes of distribution and the standard units in statistics: Variance, standard deviation
5 Shapes of distribution and the standard units in statistics: Variance, standard deviation
6 Z-score calculations, introduction to SPSS and Introduction to Correlation Analysis
7 Correlation Analysis - Review
8 Midterm
9 Comparing means: One sample t-test and Paired-samples t-test analysis
10 Comparing means: Independent samples t-test analysis
11 One-way ANOVA
12 One-way ANOVA
13 Presentations
14 Chi Square Test
15 Presentations

Workload Calculation

Activity Count Duration (Hours) Total
Attendance 14 3.00 42.00
Practice 14 2.00 28.00
Pre-Class Individual Study 14 2.00 28.00
Post-Class Individual Study 14 2.00 28.00
Midterm Exam/Preparation 1 20.00 20.00
Final Exam/Preparation 1 20.00 20.00
Quiz Preparation 3 5.00 15.00
Homework 1 20.00 20.00
Research Presentation 1 5.00 5.00
Total Workload (Hours) 206
ECTS Credit (Workload / 25) 7

Assessment

# Assessment Type Contribution (%)
1 Midterm Exam %20
2 Quiz %15
3 Project %25
4 Final Exam %40
TOTAL %100

PO - LO Matrix

PO \ LO
LO1
LO2
LO3
LO4
LO5
PO-1
PO-2
PO-3
PO-4
PO-5
PO-6
PO-7
PO-8
PO-9
PO-10
PO-11
PO-12
PO-13
PO-14
PO-15
PO-16
1
Low Contribution
2
Medium Contribution
3
High Contribution

Teaching and Learning Methods

# Method Name Description Tools
1 Lecture (expository teaching), interactive discussion Listening and taking notes. Standard classroom technologies, multimedia tools (projector, computer, digital presentations)
2 Group Work Students determine the slope and cross-sectional geometric characteristics of a parabolic irrigation canal on campus, plot the flow rate curve at different sections of the canal, and submit a report. Land surveying instruments
3 Laboratory Listening, note-taking, conceptual analysis, critical thinking, questioning, and participation in discussions. Special equipment
4 Homework Solving and analyzing homework questions using a computer and Excel. Computer
5 Oral Research – lifelong learning, processing situations, developing questions, interpreting, presenting

Academic Integrity and Artificial Intelligence

Academic Integrity Policy on AI and Plagiarism: The use of Artificial Intelligence (AI) tools in writing assignments must be limited and responsible. Excessive reliance on AI-generated content (over 30%) and/or a similarity index above 30% in Turnitin will be considered violations of academic integrity. Consequences may include, but are not limited to:  (1) Reduction in grade (2) Non-evaluation of the homework/assignment (3) Referral for disciplinary action in accordance with university policies. Students are encouraged to use AI tools only as supportive resources (e.g., brainstorming, grammar checks) while ensuring that submitted work reflects their own original thinking and effort.