Assoc. Prof. Dr. Alper Kumcu
Email | Website

Institution: Middle East Technical University
Programme: English Language Teaching; graduate level
Semester: 2026–2027 Fall
Credits: 3 METU credits, 3(3–0); 8 ECTS
Class meetings: Wednesdays, three teaching hours per week
Teaching period: 30 September–30 December 2026; the university teaching period ends on 31 December
Final assessment period: 4–16 January 2027
Class time and room: To be announced on Moodle
Office hours: By appointment
Course communication: Moodle and email

Course Description

This course introduces the principles, design, implementation, and evaluation of quantitative research in language research, with particular attention to applied linguistics, second language acquisition, and foreign language teaching. It is designed for master’s students who want to plan their own research and develop the statistical literacy needed to read published studies critically.

Students follow the research process from identifying a problem and reviewing relevant literature to formulating research questions, selecting a design, operationalising constructs, collecting or identifying suitable data, conducting analyses, and reporting findings. The course considers surveys, experiments, quasi-experiments, correlational studies, and language assessment and elicitation tasks. Quantitative, qualitative, and mixed methods approaches are compared to clarify their assumptions, purposes, and possible contributions to the same research problem.

Statistical instruction combines conceptual explanation with guided practice. Topics include data preparation, descriptive statistics, visualisation, sampling uncertainty, confidence intervals, effect sizes, hypothesis testing, comparisons between groups, categorical data analysis, correlation, and introductory regression. Throughout the course, students consider the relationship between research design and statistical analysis, the assumptions of different methods, and the limits of the conclusions that can be drawn.

Practical work begins with IBM SPSS Statistics 31. Once students are comfortable with basic analyses, they receive a gentle introduction to R and revisit familiar procedures using short, annotated scripts. Examples draw on language proficiency, vocabulary learning, language anxiety, classroom interventions, learner questionnaires, and linguistic performance.

Teaching and learning: A typical meeting comprises three connected blocks: conceptual discussion and research examples; an instructor demonstration; and guided analysis, interpretation, or a research design workshop. Practical activities are integrated into the scheduled class hours. Students complete selected readings before class and develop their own analysis project through staged assignments and feedback. Prior programming experience is not expected.

Software and preparation:

  • SPSS: Consult METU’s IBM SPSS Statistics 31 guidance before the first practical session. The current guidance directs students to on-campus computer rooms and department/unit coordinators for access. Personal installation should follow the access arrangements confirmed by the department and METU Computer Center.
  • R: Install R and a compatible open-source edition of RStudio Desktop before Week 10. Installation guidance and starter scripts will be provided. R is introduced through familiar datasets and analyses, with support for importing data, editing scripts, producing figures, and understanding common error messages.
  • Practical materials: Datasets, codebooks, SPSS instructions, R scripts, assignment briefs, and selected article readings will be provided on Moodle. Please arrange access to a computer for practical work and contact the instructor early if access or installation support is needed.

Learning Outcomes

By the end of the course, students will be able to:

  • Explain key concepts and principles of quantitative inquiry and distinguish its purposes from those of qualitative and mixed methods research.
  • Formulate researchable questions and appropriate hypotheses in language research, distinguishing exploratory questions from confirmatory tests.
  • Identify and evaluate research designs, sampling strategies, variables, and data collection procedures in applied linguistics, second language acquisition, and foreign language teaching.
  • Operationalise language-related constructs and evaluate measurement quality, reliability, validity, and potential sources of bias.
  • Apply ethical principles to participant recruitment, consent, data handling, analysis, and reporting.
  • Prepare, document, screen, summarise, and visualise quantitative datasets using SPSS and introductory R.
  • Select and conduct basic descriptive and inferential analyses that fit the research question, measurement scale, and dependency structure of the data.
  • Interpret statistical estimates, confidence intervals, effect sizes, and p-values without overstating causality, generalisability, or evidence for the absence of an effect.
  • Critically evaluate the methodological and statistical decisions made in published language research.
  • Produce an APA-style research report supported by a documented and reproducible analysis.

Weekly Schedule

Reading abbreviations: MDF = Marczyk, DeMatteo, and Festinger (2005); MB = Martin and Bridgmon (2012); G = Gorard (2003). Selected sections and supplementary readings will be specified on Moodle.

Week Day and date Topic Focus and practical work Core reading
1 Wednesday, 30 September 2026 Introduction to the course and quantitative inquiry Introduction to the course aims, content, assessment, and software arrangements. Introductions and discussion of students’ academic backgrounds, research interests, and possible research projects. Comparison of quantitative, qualitative, and mixed methods inquiry, including deduction, induction, measurement, and interpretation. Discussion of the contribution of numerical evidence to language research. MDF, Ch. 1; G, Ch. 1
2 Wednesday, 7 October 2026 From research problems to questions, hypotheses, and variables Connections between literature, theory, and researchable questions. Identification of constructs, operational definitions, outcomes, predictors, and potential confounders. Distinctions between descriptive, relational, and causal questions, and between exploratory and confirmatory analyses. Workshop: formulation of a focused language research question, a justified hypothesis where appropriate, and a variable map; introduction to SPSS Data View and Variable View. MDF, Ch. 2; MB, Ch. 4, selected sections
3 Wednesday, 14 October 2026 Research design, sampling, and ethics Comparison of surveys, correlational studies, experiments, and quasi-experiments. Introduction to between-participant and within-participant designs, random sampling and random assignment, counterbalancing, and sampling bias. Discussion of consent, classroom power relationships, confidentiality, and ethical approval. Practice: identification of the unit of analysis, methodological evaluation of an intervention design, and import of a dataset and its codebook into SPSS. MDF, Chs. 3, 5, and 8, selected sections; G, Chs. 4 and 8, selected sections
4 Wednesday, 21 October 2026 Measurement, instruments, reliability, and validity Examination of questionnaires, language tests, rating scales, and elicitation tasks. Discussion of measurement scales, individual Likert items and multi-item scales, piloting, instrument adaptation, internal consistency, and agreement between raters. Distinction between reliability and validity. SPSS practice: definition of variable labels and missing values, reverse-scoring of items, calculation of composite scores, and interpretation of a worked reliability analysis. MDF, Chs. 4 and 6, selected sections; G, Ch. 5; MB, Ch. 5, selected sections
5 Wednesday, 28 October 2026 Public holiday — no class No class meeting. —
6 Wednesday, 4 November 2026 Data screening, descriptive statistics, and visualisation Identification of entry errors, missing data, unusual observations, and skewed distributions. Selection of appropriate frequencies, percentages, measures of central tendency, and measures of variability. Discussion of denominators in percentages and rates. SPSS practice: screening of language learning data, preparation of graphs and a descriptive table, and documentation of data cleaning decisions. MB, Chs. 1 and 6, selected sections; G, Ch. 3
7 Wednesday, 11 November 2026 Statistical inference, uncertainty, effect sizes, and power Introduction to population parameters, sample estimates, sampling distributions, standard errors, confidence intervals, and null hypothesis testing. Discussion of p-values, Type I and Type II errors, statistical power, and statistical and practical importance. Practice: an instructor-led sampling demonstration and interpretation of estimates, confidence intervals, and effect sizes from SPSS output. MB, Chs. 2 and 3; MDF, Ch. 7, selected sections
8 Wednesday, 18 November 2026 Comparing two groups or two measurements Selection of independent-samples and paired-samples t-tests according to the research design. Introduction to Welch’s t-test, relevant distributional checks, mean differences, and standardised effects. SPSS practice: comparison of two instructional groups, analysis of matched pre/post scores, examination of group distributions or paired differences, and preparation of a results paragraph with attention to uncertainty and limitations. G, Ch. 9, t-test sections; MB, Ch. 5, t-test example; MDF, Ch. 7, selected sections
9 Wednesday, 25 November 2026 Comparing three or more groups: introductory ANOVA Introduction to the logic of one-way ANOVA, within-group and between-group variation, omnibus tests, planned contrasts, post-hoc comparisons, and multiple testing. Introduction to Welch ANOVA and the implications of repeated measurements and factorial designs. SPSS practice: comparison of three teaching conditions, selection of suitable follow-up comparisons, and reporting of findings and effect sizes. MB, Ch. 7, selected sections; Chs. 8–9, introductory sections; G, Ch. 9, ANOVA sections
10 Wednesday, 2 December 2026 Categorical outcomes and introductory rank-based methods Examination of contingency tables, chi-square tests of independence, Fisher’s exact test, expected counts, and Cramér’s V. Introduction to Mann–Whitney U and Wilcoxon signed-rank tests, including their assumptions and interpretation. SPSS practice: comparison of independent learners’ pass/fail outcomes across groups and selection and justification of an analysis for an ordinal outcome. G, Ch. 6, selected sections; MB, Ch. 11, selected sections
11 Wednesday, 9 December 2026 A gentle introduction to R and reproducible workflows Introduction to R and RStudio, scripts and the console, objects, data frames, functions, packages, and help resources. Connections between R workflows and familiar SPSS procedures. R practice: creation of a project, import of a CSV file, inspection of variable types and missing values, calculation of descriptive statistics, and preparation of a simple plot using an annotated starter script. Instructor’s R starter guide; selected workflow, data import, and visualisation sections from R for Data Science
12 Wednesday, 16 December 2026 Correlation and introductory linear regression Distinctions between association, prediction, and causation. Interpretation of Pearson and Spearman correlations, scatterplots, regression coefficients, confidence intervals, residuals, and explained variation. Introduction to the purpose of multiple regression through a worked example. Practice: analysis of a language anxiety–performance relationship in SPSS and reproduction of the correlation and simple regression in R using a template. G, Ch. 10; MB, Ch. 12, selected sections
13 Wednesday, 23 December 2026 Repeating familiar analyses in R and reporting results Consolidation of the relationship between research design, analysis, and interpretation. R workshop: reproduction of a familiar t-test or one-way ANOVA, examination of relevant assumptions, preparation of a figure, and comparison of equivalent SPSS and R specifications. Preparation of APA-style tables and results paragraphs. Discussion of reproducibility, transparent exclusions, missing-data decisions, and planned and exploratory analyses. MDF, Chs. 7 and 9, selected sections; MB, Ch. 13; instructor’s R templates
14 Wednesday, 30 December 2026 Revision and practice Revision of research design, measurement, data preparation, statistical method selection, and interpretation. Integrated SPSS practice: analysis of a dummy dataset in response to a research context and questions, justification of data treatment and analytical choices, and preparation of a short results report. Discussion of common methodological and statistical problems, including unjustified causal claims and interpretations of nonsignificant results. Final examination preparation and questions. Review of previous readings; instructor’s practice dataset and reporting checklist
Final examination 4–16 January 2027; exact date to be announced In-class data analysis and reporting Individual preparation and analysis of an instructor-provided dummy dataset in SPSS, with an accompanying report explaining analytical choices, the analysis strategy, and the findings. Examination instructions

Published language research will be discussed throughout the semester. Weekly readings, practical materials, and schedule updates will be provided on Moodle.

Assessment

The course grade is based on two individual assessments: a midterm examination (50%) and a final examination (50%).

Component Share of final grade
Midterm: In-class methodological response paper 50%
Final: In-class data analysis and accompanying report 50%
Total 100%

Midterm Examination: Methodological Response Paper

During a three-hour in-class examination, students will read an instructor-selected quantitative article in language research and write an 800–1,000-word critical methodological response paper.

The paper should identify the study’s research questions, hypotheses where applicable, design, sample, measures, and statistical analyses. Students should evaluate the alignment between these elements, the suitability of the analyses, the treatment of assumptions and uncertainty, and the strength of the conclusions. Criticism should be supported by specific evidence from the article and accompanied by feasible suggestions for improvement.

Assessment will focus on methodological and statistical understanding, the quality of the critical evaluation, and the clarity and organisation of the response. Proposed examination date: 25 November 2026.

Final Examination: Data Analysis and Accompanying Report

The final examination will take place in class during the final examination period, 4–16 January 2027. Students must bring their computers with working access to IBM SPSS Statistics. The instructor will provide a dummy (simulated) dataset, the research context, and the research questions.

Students will independently decide how to prepare and analyse the data, conduct the required analyses in SPSS, and submit an accompanying report with the relevant SPSS output. The report should:

  • Explain and justify the data preparation decisions, including the treatment of missing values, unusual observations, and variable coding where relevant.
  • Present an analysis strategy that fits the research questions, design, and measurement scales.
  • Explain the selected statistical procedures and evaluate their relevant assumptions.
  • Report descriptive and inferential findings clearly, including estimates, effect sizes, confidence intervals, and p-values where appropriate.
  • Interpret the findings in relation to the research questions and acknowledge the limitations of the analysis.

Assessment will focus on the appropriateness and justification of the analytical decisions, accurate execution of the analyses, and clear reporting and interpretation of the results.

The exact final examination date, duration, permitted resources, and submission instructions for both examinations will be announced on Moodle.

Attendance, participation, and preparation: Students are expected to attend, complete the assigned readings, contribute to discussion, and engage in practical work. Contributions include asking questions, explaining an analytical decision, interpreting results, and giving constructive feedback. Please communicate difficulties that affect attendance or access to practical activities.

Academic Integrity, Turnitin, and AI Use

All submitted work must comply with the principles of academic integrity. Plagiarism includes the unattributed use of another person’s words, ideas, arguments, translations, or code. Fabricating or falsifying data, misrepresenting data sources, and concealing analytical decisions also breach academic integrity. Simulated teaching data are legitimate when their origin is disclosed.

The instructor will submit the substantive written parts of take-home assignments and final reports to Turnitin. A similarity score alone does not establish plagiarism; reports will be reviewed in context. Confirmed cases will be handled under university regulations and may result in a reduced grade, a grade of zero, and/or further disciplinary action.

Students remain responsible for the accuracy, originality, analysis, citations, and quality of their work. Generative AI may assist with activities such as brainstorming, language revision, or explaining and debugging code, subject to the assignment instructions. Students must understand and verify any code, statistical procedure, numerical result, or reference that they use. AI-generated explanations and outputs must not replace their own methodological decisions or interpretation.

Any use of AI in preparing an assignment must be disclosed in a brief AI Use Acknowledgement at the end of the submission, naming the tool and explaining its contribution. Confidential, identifiable, or restricted research data must not be uploaded to external AI services without the relevant authorisation. Unacknowledged or inappropriate use of AI-generated content may be treated as a breach of academic integrity.

Example AI Use Acknowledgement: I used [name of tool] to assist with [purpose, such as language revision or debugging an R script]. I checked the suggestions, verified the analyses and references, and take full responsibility for the submitted work.

Discussion and collaboration are encouraged during guided exercises. Assessed submissions must represent each student’s own work, with borrowed code, resources, and assistance acknowledged appropriately.

General References

Core books

  • Gorard, S. (2003). Quantitative methods in social science. Continuum.
  • Marczyk, G., DeMatteo, D., & Festinger, D. (2005). Essentials of research design and methodology. John Wiley & Sons.
  • Martin, W. E., & Bridgmon, K. D. (2012). Quantitative and statistical research methods: From hypothesis to results. Jossey-Bass.

Supplementary resources

Relevant journals: Applied Linguistics; Language Learning; Studies in Second Language Acquisition; Second Language Research; Language Teaching Research; Language Testing; The Modern Language Journal.

The three core books are the supplied course preparation materials. Supplementary books support language-specific examples and the transition to R; selected materials and access information will be provided on Moodle.