Trending December 2023 # Education Research Highlights From 2023 # Suggested January 2024 # Top 21 Popular

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A look at the research that made an impact in 2023, from the benefits of well-designed classroom spaces to the neuroscience behind exercise and math ability.

2023 was a great year for education research. fMRI technology gave us new insight into how exercise can improve math ability by changing the structure of children’s brains (#13 below). We saw how Sesame Street’s 40-year history has made an impact on preparing young children for school (#7). Several studies reinforced the importance of social and emotional learning for students (#2, 5, and 9). Two must-read publications were released to help educators understand how students learn (#4 and 11). Here are 15 studies published this year that every educator should know about.

1. Well-Designed Classrooms Boost Student Learning

A classroom’s physical learning space makes a difference in how well students learn. In this study of 27 schools in England, researchers found that improving a primary classroom’s physical design, including lighting, layout, and decorations, can improve academic performance by as much as 16 percent (although too many decorations can be a distraction).

Barrett, P. S., Zhang, Y., Davies, F., & Barrett, L. C. (2023). Clever Classrooms: Summary report of the HEAD project. University of Salford, Manchester.

2. The Benefits of Being Kind Last From Kindergarten to Adulthood

Kindness matters. Kindergarten students who share, help others, and show empathy are more likely to have personal, educational, and career success as adults, finds this study that tracked 753 children from 1991 to 2010.

Jones, D. E., Greenberg, M., Crowley, M. (2023). Early social-emotional functioning and public health: The relationship between kindergarten social competence and future wellness. American Journal of Public Health, e-View Ahead of Print.

3. Theatre Programs Help Students With Autism

Did you know that participating in theatre programs can help students with autism learn to play in groups, communicate with others, and recognize faces? These are the findings of a study by researchers from Vanderbilt University.

Corbett, B. A., Key, A. P., Qualls, L., Fecteau, S., Newsom, C., Coke, C., & Yoder, P. (2023). Improvement in Social Competence Using a Randomized Trial of a Theatre Intervention for Children With Autism Spectrum Disorder. Journal of Autism and Developmental Disorders, 1-15.

4. The Science of Learning

If you’re looking for an excellent review of research on how students learn, check out The Science of Learning. Drawing from cognitive science, this report breaks down the research into six principles with a full reference list and teaching tips.

Deans for Impact (2023). The Science of Learning. Austin, TX: Deans for Impact.

5. Investing $1 in Social and Emotional Learning Yields $11 in Long-Term Benefits

We know that SEL has tremendous benefits for student learning, but what are the long-term economic benefits? Researchers analyzed the economic impact of six widely-used SEL programs and found that on average, every dollar invested yields $11 in long-term benefits, ranging from reduced juvenile crime, higher lifetime earnings, and better mental and physical health.

Belfield, C., Bowden, B., Klapp, A., Levin, H., Shand, R., & Zander, S. (2023). The Economic Value of Social and Emotional Learning. New York, NY: Center for Benefit-Cost Studies in Education.

6. Low-Income Students Now a Majority

51 percent of the students across the nation’s public schools now come from low-income families.

A New Majority Research Bulletin: Low Income Students Now a Majority in the Nation’s Public Schools

7. Sesame Street Boosts Learning for Preschool Children

Sesame Street was introduced over 40 years ago an educational program to help prepare children for school. Examining census data, researchers discovered that preschool-aged children in areas with better reception did better in school. Children living in poorer neighborhoods experienced the largest gains in school performance.

Kearney, M. S., & Levine, P. B. (2023). Early Childhood Education by MOOC: Lessons From Sesame Street (No. w21229). National Bureau of Economic Research.

8. Don’t Assign More Than 70 Minutes of Homework

For middle school students, assigning up to 70 minutes of daily math and science homework was beneficial, but assigning more than 90-100 minutes resulted in a decline in academic performance. Read more about the research on homework.

Fernández-Alonso, R., Suárez-Álvarez, J., & Muñiz, J. (2023). Adolescents’ Homework Performance in Mathematics and Science: Personal Factors and Teaching Practices. Journal of Educational Psychology, 107(4), 1075–1085

9. Mindfulness Exercises Boost Math Scores

Mindfulness exercises help students feel more positive, and a new study found that it can also boost math performance. Elementary school students that participated in a mindfulness program had 15 percent better math scores, in addition to several emotional and psychological benefits.

10. Boys Get Higher Math Scores When Graded by Teachers Who Know Their Names

In this Israeli study, middle and high school students were randomly assigned to be graded anonymously or by teachers who knew their names. Despite performing worse than girls in math when graded anonymously, boys had better scores when teachers knew who they were.

Lavy, V., & Sand, E. (2023). On the Origins of Gender Human Capital Gaps: Short and Long Term Consequences of Teachers’ Stereotypical Biases (No. w20909). National Bureau of Economic Research.

11. Top Psychology Principles Every Teacher Should Know

How do students think and learn? The American Psychological Association sought to answer this question with the help of experts across a wide variety of psychological fields. The result: 20 science-backed principles that explain how social and behavioral factors influence learning.

American Psychological Association, Coalition for Psychology in Schools and Education. (2023). Top 20 Principles from Psychology for PreK–12 Teaching and Learning.

12. To Help Students With ADHD Concentrate, Let Them Fidget

Since hyperactivity can be a natural state for students with ADHD, preventing them from fidgeting can hurt their ability to stay focused. For tips on how to let students fidget quietly, check out 17 Ways to Help Students With ADHD Concentrate.

Hartanto, T. A., Krafft, C. E., Iosif, A. M., & Schweitzer, J. B. (2023). A trial-by-trial analysis reveals more intense physical activity is associated with better cognitive control performance in attention-deficit/hyperactivity disorder. Child Neuropsychology, (ahead of print), 1-9.

13. The Neuroscience Behind Exercise and Math Ability

Research shows that exercise has a positive effect on learning, but studies generally tend to be observational. With the use of fMRI technology, however, researchers have gained new insight into how people learn. A team of scientists examined the brain structures of children and found that when young children exercise, their brains produce a thinner layer of cortical gray matter, which can lead to stronger math skills.

Chaddock-Heyman, L., Erickson, K. I., Kienzler, C., King, M., Pontifex, M. B., Raine, L. B., Hillman, C. H., & Kramer, A. F. (2023). The Role of Aerobic Fitness in Cortical Thickness and Mathematics Achievement in Preadolescent Children. PLOS ONE, 10(8), e0134115.

14. The Benefits of a Positive Message Home

Getting parents more involved in their child’s education is a great way to boost student learning. When teachers sent short weekly messages to parents with tips on how their kids could improve, it led to higher-quality home discussions and cut course dropout rates by almost half.

Kraft, M. A., & Rogers, T. (2023). The underutilized potential of teacher-to-parent communication: Evidence from a field experiment. Economics of Education Review, 47, 49-63.

15. When Teachers Collaborate, Math and Reading Scores Go Up

Teaching can feel like an isolating profession, but this new study shows that working in groups — especially instructional teams — can boost student learning.

Ronfeldt, M., Farmer, S. O., McQueen, K., & Grissom, J. A. (2023). Teacher Collaboration in Instructional Teams and Student Achievement. American Educational Research Journal, 52(3), 475-514.

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Generative Ai In Education: Visual Storytelling From Text – A Python Guide


Discover the fascinating world of generative AI in education through my captivating blog! In this immersive guide, we’ll explore:

The Magic of Visual Storytelling: Discover how AI can convert ordinary text into remarkable visuals, enriching the learning experience for students.

Mastering Python for Creative AI: Get hands-on with Python to implement powerful text-to-image models like Dreambooth-Stable-Diffusion.

Dive Deep into Cutting-edge Algorithms: Understand the inner workings of state-of-the-art models and their applications in educational settings.

Empower Personalization in Education: Explore how AI can personalize content for each learner, delivering tailored and captivating visual stories.

Prepare for the Future of Learning: Stay ahead of the curve by embracing AI-driven technologies and their potential to revolutionize education.

This article was published as a part of the Data Science Blogathon.

Table of Contents Project Description

In this project, we will delve into a deep learning method to produce quality images from textual descriptions, specifically targeting applications within the education sector. This approach offers significant opportunities for enriching learning experiences by providing personalized and captivating visual stories. By leveraging pre-trained models such as Stable Diffusion and GPT-2, we will generate visually appealing images that accurately capture the essence of the provided text inputs, ultimately enhancing educational materials and catering to a variety of learning styles.

Problem Statement

The primary objective of this project is to create a deep learning pipeline capable of generating visually engaging and precise images based on textual inputs. The project’s success will be gauged by the quality and accuracy of the images generated in comparison to the given text prompts, showcasing the potential for enriching educational experiences through captivating visuals.


To successfully follow along with this project, you will need the following:

A good understanding of deep learning techniques and concepts

Proficiency in Python programming.

Familiarity with libraries such as OpenCV, Matplotlib, and Transformers.

Basic knowledge of using APIs, specifically the Hugging Face API.

This comprehensive guide provides a detailed end-to-end solution, including code and output harnessing the power of two robust models, Stable Diffusion and GPT-2, to generate visually engaging images from the textual stimulus.

Stable Diffusion is a generative model rooted in the denoising score-matching framework, designed to create visually cohesive and intricate images by emulating a stochastic diffusion process. The model functions by progressively introducing noise to an image and subsequently reversing the process, reconstructing the image from a noisy version to its original form. A deep neural network, known as the denoising score network, guides this reconstruction by learning to predict the gradient of the data distribution’s log-density. The final outcome is the generation of visually engaging images that closely align with the desired output, guided by the input textual prompts.

GPT-2, the Generative Pre-trained Transformer 2, is a sophisticated language model created by OpenAI. It builds on the Transformer architecture and has undergone extensive pre-training on a substantial volume of textual data, empowering it to produce a contextually relevant and coherent text. In our project, GPT-2 is employed to convert the given textual inputs into a format suitable for the Stable Diffusion model, guiding the image generation process. The model’s ability to comprehend and generate contextually fitting text ensures that the resulting images align closely with the input prompts.

Combining these two models’ strengths, we generate visually impressive images that accurately represent the given textual prompts. The fusion of Stable Diffusion’s image generation capabilities and GPT-2’s language understanding allows us to create a powerful and efficient end-to-end solution for generating high-quality images from text.


Step 1: Set up the environment

We begin by installing the required libraries and importing the necessary components for our project. We will use the Diffusers and Transformers libraries for deep learning, OpenCV and Matplotlib for image display and manipulation, and Google Drive for file storage and access.

# Install required libraries !pip install --upgrade diffusers transformers -q # Import necessary libraries from pathlib import Path import tqdm import torch import pandas as pd import numpy as np from diffusers import StableDiffusionPipeline from transformers import pipeline, set_seed import matplotlib.pyplot as plt import cv2 from google.colab import drive

Step 2: Access the dataset

We will mount Google Drive to access our dataset and other files in this step. We will load the CSV file containing the textual prompts and image IDs and update the file paths accordingly.

# Mount Google Drive drive.mount('/content/drive') # Update file paths data = pd.read_csv('/content/drive/MyDrive/SD/promptsRandom.csv', encoding='ISO-8859-1') prompts = data['prompt'].tolist() ids = data['imgId'].tolist() dir0 = '/content/drive/MyDrive/SD/'

Using OpenCV and Matplotlib, we will display the images from the dataset and print their corresponding textual prompts. This step allows us to familiarize ourselves with the data and ensure it has been loaded correctly.

# Display images for i in range(len(data)): img = cv2.imread(dir0 + 'sample/' + ids[i] + '.png') # Include 'sample/' in the path plt.figure(figsize=(2, 2)) plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) plt.axis('off') print(prompts[i]) print()

Step 4: Configure the deep learning models: We will define a configuration class (CFG) to set up the deep learning models used in the project. This class specifies parameters such as the device used (GPU or CPU), the number of inference steps, and the model IDs for the Stable Diffusion and GPT-2 models.

We will also load the pre-trained models using the Hugging Face API and configure them with the necessary parameters.

# Configuration class CFG: device = "cuda" seed = 42 generator = torch.Generator(device).manual_seed(seed) image_gen_steps = 35 image_gen_model_id = "stabilityai/stable-diffusion-2" image_gen_size = (400, 400) image_gen_guidance_scale = 9 prompt_gen_model_id = "gpt2" prompt_dataset_size = 6 prompt_max_length = 12 # Replace with your Hugging Face API token secret_hf_token = "XXXXXXXXXXXX" # Load the pre-trained models image_gen_model = StableDiffusionPipeline.from_pretrained( CFG.image_gen_model_id, torch_dtype=torch.float16, revision="fp16", use_auth_token=secret_hf_token, guidance_scale=9 ) image_gen_model = prompt_gen_model = pipeline( model=CFG.prompt_gen_model_id, device=CFG.device, truncation=True, max_length=CFG.prompt_max_length, num_return_sequences=CFG.prompt_dataset_size, seed=CFG.seed, use_auth_token=secret_hf_token )

Step 5: Generate images from prompts: We will create a function called ‘generate_image’ to generate images from textual prompts using the Stable Diffusion model. The function will input the textual prompt and model and generate the corresponding image.

Afterward, we will display the generated images alongside their corresponding textual prompts using Matplotlib.

# Generate images function def generate_image(prompt, model): image = model( prompt, num_inference_steps=CFG.image_gen_steps, generator=CFG.generator, guidance_scale=CFG.image_gen_guidance_scale ).images[0] image = image.resize(CFG.image_gen_size) return image # Generate and display images for given prompts for prompt in prompts: generated_image = generate_image(prompt, image_gen_model) plt.figure(figsize=(4, 4)) plt.imshow(generated_image) plt.axis('off') print(prompt) print()

Our project also experimented with generating images using custom textual prompts. We used the ‘generate_image’ function with a user-defined prompt to showcase this. In this example, we chose the custom prompt: “The International Space Station orbits gracefully above Earth, its solar panels shimmering”. The code snippet for this is shown below:

custom_prompt = "The International Space Station orbits gracefully above Earth, its solar panels shimmering" generated_image = generate_image(custom_prompt, image_gen_model) plt.figure(figsize=(4, 4)) plt.imshow(generated_image) plt.axis('off') print(custom_prompt) print()

Let’s create a simple story with five textual prompts, generate images for each, and display them sequentially.


A lonely astronaut floats in space, surrounded by stars.

The astronaut discovers a mysterious, abandoned spaceship.

The astronaut enters the spaceship and finds an alien map.

The astronaut explores the new planet, filled with excitement and wonder.

Now, let’s write the code to generate and display images for each prompt:

story_prompts = [ "A lonely astronaut floats in space, surrounded by stars.", "The astronaut discovers a mysterious, abandoned spaceship.", "The astronaut enters the spaceship and finds an alien map.", "The astronaut decides to explore the new planet, filled with excitement and wonder." ] # Generate and display images for each prompt in the story for prompt in story_prompts: generated_image = generate_image(prompt, image_gen_model) plt.figure(figsize=(4, 4)) plt.imshow(generated_image) plt.axis('off') print(prompt) print()#import csv

Executing the above code will generate images for each story prompt, displaying them sequentially along with their corresponding textual prompts. This demonstrates the model’s ability to create a visual narrative based on a sequence of textual prompts, showcasing its potential for storytelling and animation.


This comprehensive guide explores a deep learning approach to generate visually captivating images from textual prompts. By harnessing the power of pre-trained Stable Diffusion and GPT-2 models, an end-to-end solution is provided in Python, complete with code and outputs. This project demonstrates the vast potential deep learning holds in industries that require custom and unique visuals for various applications like storytelling, which is highly useful for AI in Education.

5 Key Takeaways: Harnessing Generative AI for Visual Storytelling in Education

Importance of Visual Storytelling in Education: The article highlights the significance of visual storytelling in enhancing the learning experience by engaging students, promoting creativity, and improving communication skills.

Python Implementation: The article provides a step-by-step Python guide to help educators and developers harness the power of generative AI models for text-to-image synthesis, making the technology accessible and easy to integrate into educational content.

Potential Applications: The article discusses various applications of generative AI in education, such as creating customized learning materials, generating visual aids for storytelling, and assisting students with special needs, like visual impairments or learning disabilities.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion. 


Chatgpt For Market Research: Gather Insights From Customer Reviews At Scale

As the digital world continues to expand, businesses now have greater access to customer feedback. With increased connectivity, customers are more willing than ever to express their opinions. However, the abundance of feedback and its unorganized nature present challenges in deriving meaningful insights. Consequently, businesses are finding it difficult to keep up with the real-time analysis of customer data.

Unstructured data holds immense value for businesses, as it offers insights into customer behavior, preferences, and emotions that structured data fails to capture. Nonetheless, extracting these insights requires the implementation of appropriate tools and strategies.

In this sense, artificial intelligence (AI) has recently emerged as a potent force, with one technology standing out as a game changer: ChatGPT.

Chatgpt: Groundbreaking Technology To Completely Transform The Market Research Landscape

ChatGPT is an artificial intelligence (AI) platform that blends natural language processing (NLP) and machine learning (ML) to create hypotheses, insights, and exceptional analysis at scale. It can help to expedite market research, condense and analyze massive amounts of data, and give a firm foundation of audience insights to inform marketing strategy.

Its real-time user interactions, personalized coaching, and lightning-fast replies set it apart from other technologies that have assisted us in our work. This technology has the ability to completely transform the market research landscape.

ChatGPT, however, is more than simply a tool; it is a way of thinking about how to blend human creativity with data-driven technology.

Unlocking Insights: How ChatGPT is changing Market Research

In almost no time, AI can integrate hundreds of data sources, detect patterns, and isolate essential facts and findings. As a result, the time constraints connected with this type of research are essentially eliminated.

Handle large volume of data:

With the exponential growth of digital data, businesses need to be able to analyze and process vast amounts of information quickly and efficiently.

Natural language processing and machine learning capabilities of ChatGPT will allow it to analyze and process large huge amounts of unstructured data, such as customer feedback and social media posts, in real-time.

For example, a global e-commerce company can use Chatgpt to analyze customer reviews and feedback from multiple countries and languages. By using Chatgpt’s language translation capabilities, the company can gather insights from a larger pool of customers and identify global trends and patterns. Now this data can be used to adjust their marketing strategies and product offerings in each market.

This enables businesses to gain meaningful insights into customer behavior, preferences, and trends, leading to improved decision-making and better customer experiences.

Analyze unstructured data:

However, this type of data can be challenging to analyze using traditional methods. ChatGPT will allow to analyze unstructured data quickly and efficiently, identifying key themes, sentiments, and trends.

By leveraging ChatGPT’s ability to analyze unstructured data, brands can gain a deeper understanding of customer needs and preferences, leading to improved decision-making and better customer experiences.

Real-Time insights

In the world of market research, time is of the essence. To stay well ahead of the competition, organizations must understand their customers’ needs and preferences as quickly as possible.

Imagine you are a company that has just launched a new product. You want to know how your customers are responding to it, and more importantly, why.

Personalize market research:

The capacity of ChatGPT to understand natural language and handle massive volumes of data makes it an excellent tool for customized marketing. Companies can use ChatGPT to evaluate customer data, learn their preferences, and personalize marketing campaigns to specific customers. Customers tend to respond positively to marketing communications that are targeted particularly to them, which can lead to more effective marketing and higher consumer engagement.

For example, a clothing retailer can use Chatgpt to gather personalized insights into their customers’ fashion preferences. By asking questions about their customers’ style and clothing choices, Chatgpt can provide the retailer with valuable data on which styles are most popular and why. This sort of data can then be used to inform future product designs and marketing campaigns, resulting in increased customer satisfaction and sales.

Less expensive, more readily available

AI is making market research less expensive and more accessible to businesses of all sizes. For example, chatbots powered by AI can gather customer feedback and provide insights at a fraction of the cost of hiring a market research firm. This means that small businesses can now benefit from market research insights without breaking the bank.

Real-life Examples of ChatGpt Adoption in Market Research 1.Coca-Cola

The Coca-Cola Company will use OpenAI’s generative AI technology for marketing and customer experiences, among other things, making it one of the first large consumer products corporations to officially reveal usage of the much-touted technology.

Expedia announced the beta launch of a new in-app travel planning experience powered by ChatGPT. Expedia already incorporates artificial intelligence (AI) and machine learning (ML) into their platform to provide a unified experience from planning to post-booking. AI and machine learning are utilized to provide personalized and relevant trip options to travelers based on characteristics such as hotel location, room type, date ranges, price points, and much more.

ChatGpt’s Role in Market Research

In short, it appears that Chatgpt and market research have a long, happy, and productive future together.  As AI improves at understanding human emotions, expressions, speech, and language usage, it will become more accurate and valuable. AI is already assisting in information gathering, processing, and analysis; we can expect it to continue and expand into a crucial market research ally.

Author Bio

Market Survey Research In 2023: Benefits, 3 Use Cases & Tips

It is estimated that the market research services industry will exceed $90 billion in 2025. Understanding the market is not only required in the establishment phase of a company or while you are launching a new product or a service but also crucial to understand the existing trends to stand out in the market. That’s why B2B and B2C companies regularly conduct market research surveys. 

 In this article, we aim to: 

Highlight the benefits of conducting market research surveys,

Provide some suggestions before investigating the market through surveys,

Review some applications of market research in different industries,

And give some general tips to improve your market research

Why should you conduct market research surveys?

To position your brand in the market.

To get customer insights and improve customer experience

To determine the optimum pricing for your products or services

To measure the effectiveness of your marketing strategies

To get a grasp of the opinions and preferences of the public

Here are some suggestions to consider before investing your resources and money into new survey research:

Conduct web and social media research to learn about the market to build your research upon the existing knowledge.

Determine your scope if you aim to focus on a specific target. Otherwise, you can randomly select the participants.

Decide when you plan to finalize the study so you can have a systematic process and know when you will be done.

List all expected outcomes, what you want to learn most, and what you expect from the study.

Three real-world use cases of market research in different industries 1. Energy Industry – Shell

Shell, one of the largest energy companies in the world, conducted market research and segmented customers based on their pain points by recording more than 500 videos of customers from the US and China. 

They have analyzed the customer’s voices and opinions to grasp their emotions, thoughts, etc. Thus, they could understand the challenges customers face through real-world examples. 

2. Food Industry – Endangered Species Chocolate (ESC)

Endangered Species Chocolate (ESC) is not a regular company that sells chocolates. Their products’ organic ingredients and efforts to protect wildlife make them stand out in the market. They donated more than $2.6 million to the farmers in Africa through fair trade sourcing.

To understand their customers better, they conducted a market research survey and asked their customers about their preferences, demographics, lifestyles, consumptions, etc. With the insights they gathered from the research, they have started to work on producing new flavors and products, positioning their brand in the market, developing new multimedia strategies, and reaching new targets. 

3. Car Industry – Mercedes Benz

Mercedes Benz is a German luxury car brand ranked 38th in Fortune 500. The company’s success mainly depends on understanding its customers well. 

The company makes a segmentation analysis based on geographic, demographic, and behavioral data of the public to understand what percentage of the population can afford to buy the cheapest and most basic vehicle of Mercedes Benz. As they can determine their target through their comprehensive market research surveys, they can identify customer groups better and develop different strategies for each group. 

Here are some general tips and best practices for your market research survey. 1. Use focus groups if the existing knowledge of the market is limited.

There are some industries newly emerging or gained popularity over the last years, such as commercial space travel or lab-grown meat. If the existing knowledge does not provide enough insights, you can start with a focus group and learn how they approach the emerging industry. Focus groups usually consist of ten people, and you ask them questions about the market, product, or service.

2. The number of respondents does not necessarily mean high-quality responses. 

You may intuitively think that the more respondents you have, the more accurate results will be gathered. However, studies show that this is not the case. Even though 150 responses would provide you with more precise results than just 30 people, as the number of respondents grows, variability in the responses decreases.

Figure 1. The graph shows how the variability of the responses changes with the sample size. 3. Apply different sampling methods depending on market types

If you are interested in the B2C market, try to be as inclusive as possible and survey a representative group of customers or potential ones. However, if you want to focus on the B2B market, then make sure you balance the number of vertical markets and include businesses of different sizes. 

You can find more tips that increase the number of responses in our recent article on online survey research. 

If interested, here is our data-driven list of survey participant recruitment services.

Reach us if you have any questions about conducting research surveys:

Begüm Yılmaz

Begüm is an Industry Analyst at AIMultiple. She holds a bachelor’s degree from Bogazici University and specializes in sentiment analysis, survey research, and content writing services.





What Is A Research Design

A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about:

Your overall research objectives and approach

Whether you’ll rely on primary research or secondary research

Your sampling methods or criteria for selecting subjects

Your data collection methods

The procedures you’ll follow to collect data

Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Step 1: Consider your aims and approach

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

Research question exampleHow can teachers adapt their lessons for effective remote learning?

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative approach Quantitative approach

Understand subjective experiences, beliefs, and concepts

Gain in-depth knowledge of a specific context or culture

Explore under-researched problems and generate new ideas

Measure different types of variables and describe frequencies, averages, and correlations

Test hypotheses about relationships between variables

Test the effectiveness of a new treatment, program or product

Qualitative research designs tend to be more flexible and inductive, allowing you to adjust your approach based on what you find throughout the research process.

Qualitative research exampleIf you want to generate new ideas for online teaching strategies, a qualitative approach would make the most sense. You can use this type of research to explore exactly what teachers and students struggle with in remote classes. Quantitative research exampleIf you want to test the effectiveness of an online teaching method, a quantitative approach is most suitable. You can use this type of research to measure learning outcomes like grades and test scores.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics.

How much time do you have to collect data and write up the research?

Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?

Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?

Will you need ethical approval?

At each stage of the research design process, make sure that your choices are practically feasible.

Step 2: Choose a type of research design

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

Experimental and


designs allow you to test cause-and-effect relationships




designs allow you to measure variables and describe relationships between them.

Type of design Purpose and characteristics


Used to test causal relationships

Involves manipulating an independent variable and measuring its effect on a dependent variable

Subjects are randomly assigned to groups

Usually conducted in a controlled environment (e.g., a lab)


Used to test causal relationships

Similar to experimental design, but without random assignment

Often involves comparing the outcomes of pre-existing groups

Often conducted in a natural environment (higher ecological validity)


Used to test whether (and how strongly) variables are related

Variables are measured without influencing them


Used to describe characteristics, averages, trends, etc

Variables are measured without influencing them

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation).

Correlational design exampleYou could use a correlational design to find out if the rise in online teaching in the past year correlates with any change in test scores.

But this design can’t confirm a causal relationship between the two variables. Any change in test scores could have been influenced by many other variables, such as increased stress and health issues among students and teachers.

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Experimental design exampleIn an experimental design, you could gather a sample of students and then randomly assign half of them to be taught online and the other half to be taught in person, while controlling all other relevant variables.

By comparing their outcomes in test scores, you can be more confident that it was the method of teaching (and not other variables) that caused any change in scores.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Type of design Purpose and characteristics

Case study

Detailed study of a specific subject (e.g., a place, event, organization, etc).

Data can be collected using a variety of sources and methods.

Focuses on gaining a holistic understanding of the case.


Detailed study of the culture of a specific community or group.

Data is collected by extended immersion and close observation.

Focuses on describing and interpreting beliefs, conventions, social dynamics, etc.

Grounded theory

Aims to develop a theory inductively by systematically analyzing qualitative data.


Aims to understand a phenomenon or event by describing participants’ lived experiences.

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Step 3: Identify your population and sampling method

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Population exampleIf you’re studying the effectiveness of online teaching in the US, it would be very difficult to get a sample that’s representative of all high school students in the country.

To make the research more manageable, and to draw more precise conclusions, you could focus on a narrower population—for example, 9th-grade students in low-income areas of New York.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling. The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling Non-probability sampling

Sample is selected using random methods

Mainly used in quantitative research

Allows you to make strong statistical inferences about the population

Sample selected in a non-random way

Used in both qualitative and quantitative research

Easier to achieve, but more risk of research bias

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Step 4: Choose your data collection methods

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Questionnaires Interviews

More common in quantitative research

May be distributed online, by phone, by mail or in person

Usually offer closed questions with limited options

Consistent data can be collected from many people

More common in qualitative research

Conducted by researcher in person, by phone or online

Usually allow participants to answer in their own words

Ideas can be explored in-depth with a smaller group (e.g., focus group)

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Quantitative observation Qualitative observation

Systematically counting or measuring

Taking detailed notes and writing rich descriptions

All relevant observations can be recorded

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

Field Examples of data collection methods

Media & communication Collecting a sample of texts (e.g., speeches, articles, or social media posts) for data on cultural norms and narratives

Psychology Using technologies like neuroimaging, eye-tracking, or computer-based tasks to collect data on things like attention, emotional response, or reaction time

Education Using tests or assignments to collect data on knowledge and skills

Physical sciences Using scientific instruments to collect data on things like weight, blood pressure, or chemical composition

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

Step 5: Plan your data collection procedures

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.


Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations, which events or actions will you count?

ExampleTo measure student participation in an online course, you could record the number of times students ask and answer questions.

If you’re using surveys, which questions will you ask and what range of responses will be offered?

ExampleTo measure teachers’ satisfaction with online learning tools, you could create a questionnaire with a 5-point rating scale.

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

Reliability Validity

Does your measure capture the same concept consistently over time?

Does it produce the same results in different contexts?

Do all questions measure the exact same concept?

Do your measurement materials test all aspects of the concept? (content validity)

Does it correlate with different measures of the same concept? (criterion validity)

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

How many participants do you need for an adequate sample size?

What inclusion and exclusion criteria will you use to identify eligible participants?

How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability).

Step 6: Decide on your data analysis strategies

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis. With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics, you can summarize your sample data in terms of:

The distribution of the data (e.g., the frequency of each score on a test)

The central tendency of the data (e.g., the mean to describe the average score)

The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics, you can:

Make estimates about the population based on your sample data.

Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis.

Approach Characteristics

Thematic analysis

Focuses on the content of the data

Involves coding and organizing the data to identify key themes

Discourse analysis

Focuses on putting the data in context

Involves analyzing different levels of communication (language, structure, tone, etc)

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

Other interesting articles

If you want to know more about the research process, methodology, research bias, or statistics, make sure to check out some of our other articles with explanations and examples.

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Research Ethics: Meaning And Application

Research is an ongoing, exacting process. In fields like the humanities, social sciences, biology, and medicine, the researcher and the subject of their study are in close contact. The science of humanity as a whole is called anthropology. The researcher and the researcher (interviewer-interviewee, scientist-subject) share ecosystems, histories, and occasionally ethnic and linguistic identities in most fields of the discipline. Researchers frequently hold positions of authority in the area, which increases the likelihood that they may project their preconceptions and stigmas onto the individuals they encounter with.

Research Ethics Meaning

The Nuremberg trials, which took place in 1945–1946 and saw the prosecution of numerous Nazi scientists who had done cruel experiments on disabled people, homosexuals, and other persecuted groups, contributed to the development of the area of research ethics. The world was appalled when these scientists’ cruel tactics were revealed. This demonstrated the lengths scholars would go to in their ostensible quest for knowledge—torture. This served as a wake-up call to the scientific community, highlighting the necessity of establishing ethical guidelines that researchers must abide by when conducting their study.

The Nuremberg Code (1947), which established the guidelines for human testing, contained these regulations. Its fundamental tenet is that no human subject may be subjected to an experiment without that subject’s agreement. Any study participant should not suffer any injury during an experiment that has the potential to cause them harm or even death. Any associated risk with a study is only acceptable because the predicted rewards from the investigation offset it. All other factors are superseded by the study subject’s safety and consent.

All the following ethical principles and regulations for research ethics are founded on the Nuremberg Code. Many professional groups and research councils worldwide, including India, have embraced several of these standards. The Ethical Guidelines for Biomedical Research on Human Participants (2006), published by the Indian Council of Medical Research, and the Ethics Guidelines for Social Science Research in Health (2000), published by the National Committee for Social Science Research in Health are two important ethical guidelines for research in India.

Theoretical Approach

There are two theoretical perspectives to comprehend ethics and its application to social sciences. Theoretically, consequentialism or utilitarianism holds that an action’s morality or immorality may be determined by its repercussions. As long as the goal is achieved, any forms of experimentation and inquiry are legitimate. According to this viewpoint, it is OK to test novel medications or treatments on humans without understanding how they would affect their bodies.

They contend that if it benefits “experimental participants,” it will also assist millions of other people. However, if “subjects” suffer or even pass away during the trial, it is determined that the experiment must be stopped, saving millions in both financial and human costs. This philosophical approach’s ethics are based on cost-benefit calculations.

Human experimentation is any experiment performed on a living individual, not for therapeutic purposes but to learn how it would affect him. Examples include administering growth hormones to young children to observe the effects, administering low doses of insulin to healthy individuals as a control group, administering trial medications to patients to determine their potential for healing, administering electric shocks to individuals to gauge their capacity for endurance, etc. Several instances of “live ethics in research human subjects” being treated inhumanely during social and medical research.

The most notorious instance is that of Nazi Germany when war captives were put through cruel examinations and torture in the name of medical research. These included “incompatible unsterile blood transfusions, (e.g., giving Rh positive or Rh negative blood to an Rh-positive individual, or giving blood from blood type A to inmates, etc.)” injections of harmful chemicals, forced sterilization of women because they are mentally ill and would likely produce mentally ill offspring disrupting the population’s gene pool, and performing surgeries without anesthesia. Holocaust survivors and others subjected to these experiments had psychological effects for the rest of their lives. This emphasized the necessity of voluntary involvement and informed consent in all types of study.

Principles of Research Ethics

Some of the principles that are crucial to conducting ethical research are −

Honesty − Honesty ensures that the researchers truthfully share crucial details of the study with respondents, colleagues, and authorities.

Objectivity − This principle helps avoid any biases influencing the study.

Integrity − Integrity helps to maintain consistency of actions throughout the conduction of the study.

Carefulness − Carefulness helps avoid any errors committed during the study and rectify the errors made.

Openness − This principle ensures that the researcher is open to criticism and new ideas that may help improve the study.

Transparency − This principle helps ensure that all necessary information is accurately disclosed to evaluate the research adequately.

Accountability − Accountability ensures that the researcher holds responsibility regarding all concerns of the study.

Originality − This principle helps ensure that the study is free from plagiarism and that proper credits are given to the sources used in the study.

Confidentiality − This principle helps to ensure that all sensitive information is safeguarded and the participants’ responses stay only with the researcher.

Protecting of rights − This principle helps protect the rights of humans and ensures that no animal is harmed during the conduction of the study.

Legal Consideration − This principle helps researchers ensure that all legalities are followed during the study.

Each research participant must be made aware of the following by the researcher −

The purpose of the study.

The participants have free will to participate in the study.

The respondents can withdraw from the study at any point.

The possible benefits and risks involved in the study.

Confidentiality of the study will be maintained.

Ethical Failure in Research

Ethical failure occurs when participants are mistreated by the researcher or their rights or dignity are violated during the conduction of the study. Research misconduct is another ethical failure in which the researcher falsifies data, manipulates the results, misinterprets the results, or commits academic fraud. Such actions are carried out intentionally and may waste the resources and funding involved in the study. Another type of ethical failure is plagiarism, which indicates that the study is not original and that someone else’s work is copied, either intentionally or unintentionally. Ethical failure also occurs when harm is caused to the participants.

Psychological harm is caused when triggering questions are asked, causing anxiety or shame.

Social harm involves social stigma, stereotypes, public embarrassment, and social risks.

Physical harm occurs when any injury is caused during the research conduction.

Legal harm is caused to respondents when privacy is breached or the confidentiality of participants is not maintained.

Application of Principles of Ethics

Assessment − Identifying the research problems effectively, chalking out the goals of the study

Voluntary Participation − Not forcing individuals to become a part of the study and letting them become a part of the research by their will.

Rights of the participant − The participants should be told that they can choose not to share particular information and choose to withdraw from the study at any given point.

Beneficence − Ensuring that the benefit of the participants is maximized.

Confidentiality − The privacy and anonymity of the participants should be maintained.

Non-discrimination −  Avoiding participants’ discrimination on race, religion, sex, and creed.

Advantages of Research Ethics

Research ethics increase the trust between researchers and the respondents.

Ethical principles help protect the respondents’ rights, welfare, and dignity.

The respondents can hold researchers accountable for answers.

Research helps in promoting social as well as moral values.

Research ethics help avoid errors, publish original research and veracity, and better understand the study.

Ethical values adopted in research help increase cooperation, answerability, impartiality, and mutual respect.

Respondents are likely to trust the researcher, and the research study follows ethics and increases the trust and reliability of the study.

Limitations of Research Ethics

Research involving experimental drugs or equipment such as x-ray machines can cause physical harm to the participants.

Some research can cause psychological harm to individuals as some questions may trigger anxiety and bring back memories of certain traumatic events.

Tribal or other backward groups may face stigmatization and discrimination during the study.

The data collected may be released unintentionally, violating the rights and confidentiality of the participants.

How can studies be made ethical?

The following steps can be followed to make a research ethical −

Collect accurate facts.

Map out the ethical concerns.

Respondents should recognize their responsibilities.

Respecting the privacy and confidentiality of participants.

Resolving the ethical issues once recognized.


Principles of ethics are essential to be followed by researchers to ensure that fair research is conducted, which is free from plagiarism, and the rights of respondents are not violated. Ethical research does not violate legal norms and ensures that no harm is caused to the respondents intentionally or unintentionally.

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