Research Methods in AL

📍How can I determine the importance or weight of a factor? Does EFA help??


Standardized Path Coefficients are the most common metric used to assess the importance or weight of a factor in a structural equation model (SEM).

🖍Here's how they work:
Standardization: This process converts the original path coefficients into a standardized scale, typically a z-score.

Interpretation: The standardized path coefficient represents the strength and direction of the relationship between two variables. A higher absolute value indicates a stronger influence.

Significance: The significance of the standardized path coefficient is often assessed using p-values to determine if the relationship is statistically significant.

Additional Considerations:

Effect Size: While standardized path coefficients provide a good indication of relative importance, effect size measures like Cohen's d or f² can provide more nuanced interpretations.

Model Fit: It's important to ensure that the overall model fits the data well before drawing conclusions about the importance of individual factors.

💡 Contextual Understanding:
The interpretation of factor importance should be considered within the specific context of the research question and the theoretical framework.

💡By carefully examining standardized path coefficients and considering other relevant factors, researchers can gain valuable insights into the relative importance of different factors in their SEM models.

🛑EFA cannot directly show the importance or weight of a factor in the model. EFA is primarily used to identify the underlying structure of a set of variables and to discover the latent factors that explain the covariation among them.

✨️✨️Sources:✨️✨️

Field, A. (2013). Discovering Statistics Using SPSS. Sage Publications.

Ghanbar, H. (2024). Using SmartPLS for Structural Equation Modeling in Applied Linguistics: A Method Note. Educational Methods & Psychometrics, 2, 0-0.‏

Ghanbar, H. (2023). Applying Structural Equation Modeling to Second-language (L2) Research: Key Concepts and Fundamental Reconsiderations. Journal of English Language Pedagogy and Practice, 16(32), 101-117.‏

Ghanbar, H., & Rezvani, R. (2023). Structural equation modeling in L2 research: A systematic review. International Journal of Language Testing, 13(Special Issue), 79-108.‏

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis. Pearson Education.

Plonsky, L., & Ghanbar, H. (2018). Multiple regression in L2 research: A methodological synthesis and guide to interpreting R2 values. The Modern Language Journal, 102(4), 713-731.‏

Rezvani, R., Ghanbar, H., & Perkins, K. (2024). Considerations and Praxis of Exploratory Factor Analysis: Implications for L2 Research. Teaching English as a Second Language Quarterly (Formerly Journal of Teaching Language Skills), 43(1), 151-178.‏

Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson Education.

❓What is the difference between average variance extracted and R2 of a factor??

Average Variance Extracted (AVE) and R2 of a Factor are both important measures in assessing the quality of measurement models in structural equation modeling (SEM).

However, they serve distinct purposes:

Average Variance Extracted (AVE):

✅ Purpose: Measures the proportion of variance in a latent variable (factor) that is explained by its indicators (observed variables).

✅Interpretation: A higher AVE indicates that the construct is more precisely measured, with less measurement error.

✅Threshold: A common threshold is 0.50, meaning that at least 50% of the variance in the construct is explained by its indicators.

################
❗️R2 of a Factor:

✅Purpose: Measures the proportion of variance in a dependent latent variable (endogenous construct) that is explained by its predictor variables (exogenous constructs).

✅Interpretation: A higher R2 indicates that the model explains more of the variance in the dependent variable.

✅Threshold: While there's no strict threshold, higher R2 values are generally preferred.

🔑Key Differences:

AVE assesses the quality of your measurement model, ensuring that your constructs are reliably measured.

R2 evaluates the predictive power of your structural model, indicating how well your independent variables explain the variation in your dependent variable.

Both measures are crucial for evaluating the overall quality and validity of your SEM model.

📍Free Research Courses

The Cambridge Handbook of Methods in Conversation Analysis by Jeffrey D. Robinson, Rebecca Clift, Kobin H. Kendrick and Chase Wesley Raymond
With contributions from world-renowned experts, this volume is the first to provide a complete overview of conversation-analytic methods.
📚 https://cup.org/3AZFc7p

If you have any manuscript related to technology and LTE, we'd be happy to consider it.


https://www.castledown.com/journals/tltl/announcement/view/7

💡Example of Tables in APA Format

🎯Types of Research Objective

💡What research design should I choose?

Constant Leung
Commentary on Section III: Pedagogy and policy
Jamie Schissel
Section IV. Personal and interactive aspects of research and scholarship
Chapter 16. Managing publication expectations and collaborations: On the ethics of co-authoring in Applied Linguistics
Christopher J. Jenks and Jerry Lee
Chapter 17. Ethical dilemmas of graduate students negotiating new roles and responsibilities: The importance of mindful engagement, self‑care, and reflexivity
Carlo Cinaglia and Amr Rabie-Ahmed
Chapter 18. Research ethics and decisions: Cases of engagement and science communication
Scott Sterling
Chapter 19. The ethical gray area: Journal editors’ perspectives
Peter De Costa, Susan Gass, Rosa M. Manchón and Luke Plonsky
Commentary on Section IV: Personal and interactive aspects of Applied Linguistics research and scholarship
Peter Sayer
Afterword: Reflections on ethical issues in Applied Linguistics research
Patsy Duff

https://www.jbe-platform.com/content/books/9789027246332

Just out: Ethical Issues in Applied Linguistics Scholarship (Peter De Costa, Amr Rabie-Ahmed, & Carlo Cinaglia, 2024, Eds.)
A thorough, thoughtful, and inspiring volume, covering so many topics as they intersect with ethics in applied linguistics. Check out the TOC below!

From Benjamins website:
This volume contributes to ongoing discussions of ethics in Applied Linguistics scholarship by focusing in depth on several different sub-areas within the field. The book is comprised of four sections: methodological approaches to research; specific participant populations and contexts of research; (language) pedagogy and policy; and personal and interactive aspects of research and scholarship. Moving beyond discussions of how ethics is conceptualized or defined, the chapters in this volume explore ethics-in-practice by examining context-specific ethical challenges and offering guidance for current and future Applied Linguistics scholars. This volume responds to the need to provide context-specific research ethics training for graduate students and novice researchers interested in a variety of contexts and methodological approaches. After engaging with this volume, new and experienced applied linguists alike will gain familiarity with specific ethical challenges and practices within particular sub-disciplines relevant to their work and across the field more broadly.

Foreword
Martha Bigelow
Introduction: Ethical issues in Applied Linguistics scholarship
Carlo Cinaglia, Amr Rabie-Ahmed and Peter De Costa

Section I. Methodological approaches

Chapter 1. A taxonomy of questionable research practices in quantitative humanities
Luke Plonsky, Tove Larsson, Scott Sterling, Merja Kytö, Kate Yaw and Margaret Wood
Chapter 2. Corpus linguistics and ethics
Gavin Brookes and Tony McEnery
Chapter 3. Ethical issues in educational action research
Darío Luis Banegas
Chapter 4. Doing research in culturally and linguistically diverse K-12 classrooms: Ethical considerations, critical reflections, and future directions
Hayriye Kayi Aydar
Chapter 5. Ethical considerations for research involving computer-assisted language learning, social media, and online environments
Francesca Marino, Dacota Liska and Matt Kessler
Chapter 6. Transcription as ethics: (Re)Presenting young children’s complex communicative repertoires in Applied Linguistics research
Katie A. Bernstein, Usree Bhattacharya and Jennifer Johnson
Commentary on Section I: Methodological approaches
Gao Xuesong

Section II. Specific populations and research contexts
Chapter 7. The zero-sum game of beneficence: Conducting ethical critical inquiries in K-12 schools
Sara E.N. Kangas
Chapter 8. Ethical research with adult migrant language learners: Challenges and responses
James Simpson and Mike Chick
Chapter 9. Ethics in heritage language education: Negotiating ethical research practices with heritage speakers and their communities
Meagan Driver
Chapter 10. The ethics of indigenous language revitalization: Linguistic taxidermy or emancipation?
Satoru Nakagawa and Sandra Kouritzin
Chapter 11. “Where you from, who’s your Mob?”: Ethical considerations when undertaking Australian Aboriginal and Torres Strait Islander applied linguistic research
Robyn Ober, Sender Dovchin and Rhonda Oliver
Commentary on Section II: Specific populations and research contexts
Sue Starfield

Section III. Pedagogy and policy
Chapter 12. Ethical research considerations in classroom and online spaces with bilingual students and their teachers
Wayne Wright
Chapter 13. Ethical issues in language testing
Xun Yan and Melissa Bowles
Chapter 14. Navigating ethical challenges in L2 writing in transnational higher education
Christine Tardy, Jaime F. Mejia Mayorga and Emily Palese
Chapter 15. Challenges of justice and equity for ethical English as an additional language in school education

💡Thesis Blueprint

Comparing cases and groups in MAXQDA serves several important functions in qualitative and mixed-methods research, enabling researchers to explore differences, similarities, and patterns across subsets of data. Key functions include:

1. Identifying Patterns and Trends
By comparing cases or groups, researchers can identify consistent themes, codes, or patterns that emerge across different data segments.
This helps in understanding whether specific codes or themes are associated with certain groups or individuals.

2. Highlighting Group-Specific Differences
Comparing groups allows for analysis of differences in coding frequencies or distributions, which can reveal group-specific behaviors, perceptions, or experiences.
For example, in a study involving interviews with teachers and students, comparisons can highlight differing views on a topic.

3. Deepening Contextual Understanding
It enables researchers to contextualize data by comparing how different demographic, experiential, or situational factors influence the results.
For example, comparing age groups or genders in focus group data can provide nuanced insights.

4. Evaluating Coding Consistency
When cases or groups are compared, the tool can be used to assess the consistency and reliability of coding across datasets, ensuring methodological rigor.

5. Visualizing Comparisons
MAXQDA provides visualization tools (e.g., code matrices, crosstabs, and code relations browsers) to make comparisons clear and comprehensible.
These visual tools can aid in presentations or reports by showcasing results in an intuitive format.

6. Quantifying Qualitative Data
By comparing cases or groups, MAXQDA integrates qualitative insights with quantitative measures, such as frequencies and percentages, which are useful for mixed-methods research.

7. Testing Hypotheses
Comparing cases or groups can support hypothesis testing in qualitative research, such as examining whether a theme is more prevalent in one group than another.

This tab is not only useful in qualitative research but also it is very versatile in mixed methods works...

What is the function of comparing cases & groups tab in MAXQDA???

📚Citation Styles, Usage & Recommend Software

💡Main Types of Research in the Field of Language Teaching


1. Action Research:
* Focus: Improving classroom practice through reflective inquiry.
* Process: Teachers identify a problem or area for improvement, implement a change, collect data, reflect on the outcomes, and make further adjustments.
* Goal: To enhance teaching effectiveness and student learning.

2. Case Study Research:
* Focus: In-depth exploration of a specific case or phenomenon.
* Process: Researchers collect detailed information through various methods like interviews, observations, and document analysis.
* Goal: To gain a rich understanding of a particular context or individual.

3. Correlational Research:
* Focus: Examining the relationship between two or more variables.
* Process: Researchers measure variables and analyze the statistical relationship between them.
* Goal: To identify patterns and trends in language learning and teaching.

4. Experimental Research:
* Focus: Testing the causal relationship between variables.
* Process: Researchers manipulate an independent variable and measure its effect on a dependent variable.
* Goal: To determine the effectiveness of specific interventions or treatments.

5. Ethnographic Research:
* Focus: Understanding cultural and social contexts of language learning and use.
* Process: Researchers immerse themselves in the cultural setting and collect data through participant observation, interviews, and document analysis.
* Goal: To gain insights into the cultural factors that influence language learning and teaching.

6. Grounded Theory Research:
* Focus: Developing theories based on empirical data.
* Process: Researchers collect and analyze data inductively to generate new theories.
* Goal: To create theories that are grounded in real-world data.

7. Narrative Research:
* Focus: Exploring individual stories and experiences of language learning and teaching.
* Process: Researchers collect and analyze narratives through interviews, diaries, and autobiographies.
* Goal: To understand the subjective experiences of language learners and teachers.

8. Survey Research:
* Focus: Collecting quantitative data from a large sample of participants.
* Process: Researchers administer surveys or questionnaires to gather information on attitudes, beliefs, and behaviors.
* Goal: To describe the characteristics of a population.

9. Discourse Analysis:
* Focus: Analyzing language use in social contexts.
* Process: Researchers examine written or spoken texts to identify patterns and meanings.
* Goal: To understand how language is used to construct social identities and power relations.

10. Critical Discourse Analysis:
* Focus: Analyzing language use as a tool of power and ideology.
* Process: Researchers examine how language is used to maintain or challenge social inequalities.
* Goal: To expose hidden power structures and promote social justice.

💡4 Pro Tips To Write Literature Review

📣 شروع دوره آنلاین مقاله نویسی📣

💡مباحث مرتبط با مقاله نویسی از جمله موارد زیر در قالب یک دوره پوشش داده میشود:👇

📍آموزش نحوه نگارش بخش های مختلف مقاله و پروپوزال، آشنایی با مجلات و انتشارات، بررسی و تحلیل مقالات مختلف، و سایر موارد

✅مدت زمان دوره: ۱۶ جلسه
✅روزهای برگزاری: پنجشنبه ها و جمعه ها
📆تاریخ شروع دوره: ۴ بهمن ماه
⛔️ظرفیت محدود
🛜 امکان دسترسی همیشگی به جلسات و اسلاید ها

🧑‍💻مدرس دوره: آقای دکتر مصطفی نظری

📎اطلاعات بیشتر و نام نویسی:👇
@Zahraa_Keshvari

Some research topics with their designs 😊//Research design technicalities in Applied Linguistics

1. Impact of Corrective Feedback on Grammatical Accuracy

Design: True Experimental Design

Independent Variable: Type of corrective feedback (e.g., explicit correction, recasts, metalinguistic feedback)

Dependent Variable: Grammatical accuracy in written or spoken language

Control Group: Learners receiving no corrective feedback

Experimental Group: Learners receiving one of the types of corrective feedback

Procedure:

Pre-test to assess initial grammatical accuracy
Intervention with different types of corrective feedback
Post-test to measure changes in grammatical accuracy


2. Effect of Task Complexity on Second Language Fluency
Design: Quasi-Experimental Design

Independent Variable: Task complexity (e.g., simple vs. complex tasks)

Dependent Variable: Fluency measures (e.g., speech rate, number of pauses)

Control Group: Learners performing simple tasks

Experimental Group: Learners performing complex tasks

Procedure:

Pre-test to assess baseline fluency
Task performance under different complexity conditions
Post-test to measure changes in fluency

3. Influence of Multimedia Annotations on Vocabulary Retention

Design: True Experimental Design

Independent Variable: Type of multimedia annotation (e.g., text-only, text + image, text + audio)

Dependent Variable: Vocabulary retention (measured through recall tests)

Control Group: Learners receiving text-only annotations

Experimental Groups:

Group 1: Text + image annotations
Group 2: Text + audio annotations
Procedure:

Pre-test to assess initial vocabulary knowledge
Learning phase with different types of annotations
Immediate and delayed post-tests to measure vocabulary retention

4. Role of Peer Interaction in Developing Pragmatic Competence
Design: Quasi-Experimental Design

Independent Variable: Type of peer interaction (e.g., structured vs. unstructured)

Dependent Variable: Pragmatic competence (measured through role-plays or discourse completion tasks)

Control Group: Learners with no peer interaction

Experimental Group: Learners with structured or unstructured peer interaction

Procedure:

Pre-test to assess initial pragmatic competence
Interaction phase with different types of peer interaction
Post-test to measure changes in pragmatic competence

💡Types of Research Gap

Common Sampling Methods in Research

💡How to write a literature review.
This annotated example may be helpful.

Thank you for being with my channel and what I sent...we now reached 1.1K😊😍
As a gift I will share one new book from Routledge today.

The art of writing literature review.

** How to Estimate the Publication Time of a journal: From Submission to Acceptance
https://youtu.be/sSpd6P0s6Ww

If you are struggling with finding quality articles for your literature search. Install this ExCITATION Chrome Extension:
https://youtu.be/5j2Y36pFtew

THE ROUTLEDGE HANDBOOK OF

SECOND LANGUAGE ACQUISITION AND

SOCIOLINGUISTICS

🗣18 super useful tools for writing

❓How to come up with a research question?

📍Statistical tests you need to know about

A cheatsheet for selecting sampling methods

💡How to Write Your Paper Contributions

📚 PREET Center presents:

🔴  A Comprehensive Course on Qualitative Data Analysis with MAXQDA PRO 24

🔹 Instructor: Dr. Hessameddin Ghanber
🔹 Duration: Eight weeks (eight sessions)
🔹 Schedule: Wednesdays: 18:45-20:15 
🔹 The workshops will be held on the Google Meet platform and will have the support.

📣  Registration has started. (Limited capacity)

▪️ For more information and registration:
Telegram: @preetadmin
WhatsApp & SMS: +989359289774

▪️An official certificate will be issued by the MAXQDA Company ( *First 30 Reg* )

▪️For two months, the MAXQDA software and its powerful AI will be OFFICIALLY available to participants for free.

🌐🌐https://www.maxqda.com/de/workshoptrainer/hessameddin-ghanbar

▪️Join our Telegram Channel: @preetcenter

DIFFERENT TYPES OF JOURNAL RANKINGS

35 Tools for Researchers

https://www.hbku.edu.qa/en/academic-events/CHSS-WIFACA?utm_source=BenchmarkEmail&utm_campaign=CHSS_5539&utm_medium=email

How to write your research paper as a story?🤔

We’re excited to kick off this year’s Penn State Research Methods for Applied Linguists webinar series this afternoon! Join us at 2 PM (East Time) to learn about Social Network Analysis in Second Language Research. Register for this year’s webinars and stay connected to all upcoming events: tinyurl.com/PSURM2024

APA Publication Manual: 7th Edition

🌐🌐 https://libguides.csudh.edu/citation/apa-7

⭕️⭕️ A Quick Guide to APA Citation 7th Edition

The Code Co-Occurrence Model, as can be seen above, in MAXQDA is a powerful analytical tool that allows researchers to explore and visualize the relationships between different codes within their qualitative data. Here are the primary functions and features of the Code Co-Occurrence Model:

Visualizing Relationships: It provides a visual representation of how frequently different codes appear together in the same segments of data. This helps researchers understand the interconnectedness of themes or topics.

Identifying Patterns: By examining co-occurrence, researchers can identify patterns and associations between codes that may indicate underlying relationships or common themes within the data.

Enhancing Analysis: The model supports a more nuanced analysis of qualitative data by showing not only how often codes are used individually but also how they relate to one another. This can lead to richer interpretations of the data.

Dynamic Exploration: Users can interact with the model by selecting specific codes to see their co-occurrence with other codes, allowing for targeted exploration of specific relationships.

Data Segmentation: The model can be segmented by different variables, such as demographic information or case types, enabling a more focused analysis of how co-occurrence varies across different groups.

Generating Insights: The insights gained from the Code Co-Occurrence Model can inform theory development, hypotheses generation, and further qualitative inquiry.

📌Code Co-occurrence Model

📌Essential Tools for Every Step of Your Literature Review