example of quantitative research questions about learning styles

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Quantitative Research Questionnaire – Types & Examples

Published by Alvin Nicolas at August 20th, 2024 , Revised On October 24, 2024

Research is usually done to provide solutions to an ongoing problem. Wherever the researchers see a gap, they tend to launch research to enhance their knowledge and to provide solutions to the needs of others. If they want to research from a subjective point of view, they consider qualitative research. On the other hand, when they research from an objective point of view, they tend to consider quantitative research.

There’s a fine line between subjectivity and objectivity. Qualitative research, related to subjectivity, assesses individuals’ personal opinions and experiences, while quantitative research, associated with objectivity, collects numerical data to derive results. However, the best medium to collect data in quantitative research is a questionnaire.

Let’s discuss what a quantitative research questionnaire is, its types, methods of writing questions, and types of survey questions. By thoroughly understanding these key essential terms, you can efficiently create a professional and well-organised quantitative research questionnaire.

What is a Quantitative Research Questionnaire?

Quantitative research questionnaires are preferably used during quantitative research. They are a well-structured set of questions designed specifically to gather specific, close-ended participant responses. This allows the researchers to gather numerical data and obtain a deep understanding of a particular event or problem.

As you know, qualitative research questionnaires contain open-ended questions that allow the participants to express themselves freely, while quantitative research questionnaires contain close-ended and specific questions, such as multiple-choice and Likert scales, to assess individuals’ behaviour.

Quantitative research questionnaires are usually used in research in various fields, such as psychology, medicine, chemistry, and economics.

Let’s see how you can write quantitative research questions by going through some examples:

• How much do British people consume fast food per week?
• What is the percentage of students living in hostels in London?

Types of Quantitative Research Questions With Examples

After learning what a quantitative research questionnaire is and what quantitative research questions look like, it’s time to thoroughly discuss the different types of quantitative research questions to explore this topic more.

Dichotomous Questions

Dichotomous questions are those with a margin for only two possible answers. They are usually used when the answers are “Yes/No” or “True/False.” These questions significantly simplify the research process and help collect simple responses.

Example: Have you ever visited Istanbul?

Multiple Choice Questions

Multiple-choice questions have a list of possible answers for the participants to choose from. They help assess people’s general knowledge, and the data gathered by multiple-choice questions can be easily analysed.

Example: Which of the following is the capital of France?

Multiple Answer Questions

Multiple-answer questions are similar to multiple-choice questions. However, there are multiple answers for participants to choose from. They are used when the questions can’t have a single, specific answer.

Example: Which of the following movie genres are your favourite?

Likert Scale Questions

Likert scale questions are used when the preferences and emotions of the participants are measured from one extreme to another. The scales are usually applied to measure likelihood, frequency, satisfaction, and agreement. The Likert scale has only five options to choose from.

Example: How satisfied are you with your job?

Semantic Differential Questions

Similar to Likert scales, semantic differential questions are also used to measure the emotions and attitudes of participants. The only difference is that instead of using extreme options such as strongly agree and strongly disagree, opposites of a particular choice are given to reduce bias.

Example: Please rate the services of our company.

Rank Order Questions

Rank-order questions are usually used to measure the preferences and choices of the participants efficiently. In this, multiple choices are given, and participants are asked to rank them according to their perspective. This helps to create a good participant profile.

Example: Rank the given books according to your interest.

Matrix Questions

Matrix questions are similar to Likert scales. In Likert scales, participants’ responses are measured through separate questions, while in matrix questions, multiple questions are compiled in a single row to simplify the data collection method efficiently.

Example: Rate the following activities that you do in daily life.

How To Write Quantitative Research Questions?

Quantitative research questions allow researchers to gather empirical data to answer their research problems. As we have discussed the different types of quantitative research questions above, it’s time to learn how to write the perfect quantitative research questions for a questionnaire and streamline your research process.

Here are the steps to follow to write quantitative research questions efficiently.

Step 1: Determine the Research Goals

The first step in writing quantitative research questions is to determine your research goals. Determining and confirming your research goals significantly helps you understand what kind of questions you need to create and for what grade. Efficiently determining the research goals also reduces the need for further modifications in the questionnaire.

Step 2: Be Mindful About the Variables

There are two variables in the questions: independent and dependent. It is essential to decide what would be the dependent variable in your questions and what would be the independent. It significantly helps to understand where to emphasise and where not. It also reduces the probability of additional and vague questions.

Step 3: Choose the Right Type of Question

It is also important to determine the right type of questions to add to your questionnaire. Whether you want Likert scales, rank-order questions, or multiple-answer questions, choosing the right type of questions will help you measure individuals’ responses efficiently and accurately.

Step 4: Use Easy and Clear Language

Another thing to keep in mind while writing questions for a quantitative research questionnaire is to use easy and clear language. As you know, quantitative research is done to measure specific and simple responses in empirical form, and using easy and understandable language in questions makes a huge difference.

Step 5: Be Specific About The Topic

Always be mindful and specific about your topic. Avoid writing questions that divert from your topic because they can cause participants to lose interest. Use the basic terms of your selected topic and gradually go deep. Also, remember to align your topic and questions with your research objectives and goals.

Step 6: Appropriately Write Your Questions

When you have considered all the above-discussed things, it’s time to write your questions appropriately. Don’t just haste in writing. Think twice about the result of a question and then consider writing it in the questionnaire. Remember to be precise while writing. Avoid overwriting.

Step 7: Gather Feedback From Peers

When you have finished writing questions, gather feedback from your researcher peers. Write down all the suggestions and feedback given by your peers. Don’t panic over the criticism of your questions. Remember that it’s still time to make necessary changes to the questionnaire before launching your campaign.

Step 8: Refine and Finalise the Questions

After gathering peer feedback, make necessary and appropriate changes to your questions. Be mindful of your research goals and topic. Try to modify your questions according to them. Also, be mindful of the theme and colour scheme of the questionnaire that you decided on. After refining the questions, finalise your questionnaire.

Types of Survey Questionnaires in Quantitative Research

Quantitative research questionnaires have close-ended questions that allow the researchers to measure accurate and specific responses from the participants. They don’t contain open-ended questions like qualitative research, where the response is measured by interviews and focus groups. Good combinations of questions are used in the quantitative research survey .

However, here are the types of surveys in quantitative research:

Descriptive Survey

The descriptive survey is used to obtain information about a particular variable. It is used to associate a quantity and quantify research variables. The questions associated with descriptive surveys mostly start with “What is” and “How much”.

Example: A descriptive survey to measure how much money children spend to buy toys.

Comparative Survey

A comparative survey is used to establish a comparison between one or more dependable variables and two or more comparison groups. This survey aims to form a comparative relation between the variables under study. The structure of the question in a comparative survey is, “What is the difference in [dependable variable] between [two or more groups]?”.

Example: A comparative survey on the difference in political awareness between Eastern and Western citizens.

Relationship-Based Survey

Relationship-based survey is used to understand the relationship or association between two or more independent and dependent variables. Cause and effect between two or more variables is measured in the relationship-based survey. The structure of questions in a relationship-based survey is, “What is the relation [between or among] [independent variable] and [dependable variable]?”.

Example: What is the relationship between education and lifestyle in America?

Advantages & Disadvantages of Questionnaires in Quantitative Research

Quantitative research questionnaires are an excellent tool to collect data and information about the responses of individuals. Quantitative research comes with various advantages, but along with advantages, it also has its disadvantages. Check the table below to learn about the advantages and disadvantages of a quantitative research questionnaire.

Quantitative Research Questionnaire Example

Here is an example of a quantitative research questionnaire to help you get the idea and create an efficient and well-developed questionnaire for your research:

ii) What is your gender?

ii) Have you graduated?

iii) Are you employed?

iv) Are you married?

Part 2: Provide your honest response. 

Question 1: I have tried online shopping.

Question 2: I have good experience with online shopping.

Question 3: I have a bad experience with online shopping.

Question 4: I received my order on time. 

Question 5: I like physical shopping more. 

Frequently Asked Questions

What is a quantitative research questionnaire.

A quantitative research questionnaire is a well-structured set of questions designed specifically to gather specific and close-ended participant responses.

What is the difference between qualitative and quantitative research?

The difference between qualitative and quantitative research is subjectivity and objectivity. Subjectivity is associated with qualitative research, while objectivity is associated with quantitative research. 

What are the advantages of a quantitative research questionnaire?

• It is quick and efficient.
• There is less risk of research bias and subjectivity.
• It is particular and simple.

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A Quantitative Analysis of Student Learning Styles and Teacher Teachings Strategies in a Mexican Higher Education Institution

2012, learning

Research on learning processes has shown that students tend to learn in different ways and prefer to use different teaching resources. The understanding of learning styles can be used to identify, and implement, better teaching and learning strategies, in order to allow students to acquire new knowledge in a more effective and efficient way. In this study we analyze similarities and differences in learning styles among students enrolled in computing courses, in engineering and social sciences programs at the Instituto Tecnológico Autónomo de México (ITAM). In addition, we also analyze similarities and differences among the teaching strategies shown by their corresponding teachers. A comparative analysis on student learning profiles and course outcomes, allow us to suggest that, despite academic program differences, there are strong similarities among the students learning styles, as well as among the teaching styles of their professors. Seemingly, a consistent pattern of how these students learn also exists: Active, Sensitive, Visual and Sequential. At the end of the paper, we discuss how these findings might have significant implications in developing effective pedagogic strategies, as well as didactic multimedia based materials for each one of these academic programs.

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The Kolb model is widely used to analyze the learning style of engineering students; however, there are no published studies carried out in Portugal. This study was applied to students who had just began their courses at a Portuguese engineering school. This study object is to assess whether there is a clearly majority learning style; whether there are differences in the learning style among all students and between students in the industrial management course and those in the technological engineering courses. The results allow the pedagogical practice improvement in the classroom regarding the effectiveness increase of the teaching process. This study results also allow improving the way students approach the study, overcoming difficulties in understanding the topics studied, improving the construction of their skills and increasing the quality of the learning process.

Sustainability, 2021

In higher education it is important to consider learning styles of students to facilitate the teaching–learning process. The aims guiding the research were to describe the learning styles predominating among students in the field of the social sciences, to analyse the results with respect to gender, year of study, degree course and institution, and to perform correlation analysis between these variables. The data analyses were carried out with non-parametric statistics with a confidence level of 95%. The sample was composed of 636 students at the Universities of Huelva (UHU), Cádiz (UCA), and Pablo de Olavide of Seville (UPO), who completed the Honey–Alonso Learning Styles Questionnaire and reported sociodemographic and educational data. The results showed a significant preference for the Reflector style. Significant correlations were found in most variables highlighting that the courses showed an inverse correlation with the learning styles, the Activist, Theorist, and Pragmatist styles being less preferred as they progressed in the career. It is worth noting the significant direct correlation between Reflector, Theorist, and Pragmatist styles, but the Activist style inversely correlates with all three. As a complementary contribution, a proposal for intervention in classrooms with a sustainable perspective is offered. It is important to attend to the evolution in the preference of the learning styles that students acquire as they advance in higher education courses in order to facilitate a more optimal and sustainable teaching–learning process.

This paper address one of the fundamental concerns of educators, how do people learn? The answer to this question has implications for higher education in the broad sense, because the objective of every university teacher ought to be the ability to deliver a class where s/he not only shows the mastery of the subject matter but also can make learning happen. The challenge is to do it within a context of a mass higher education system, and be able to offer a quality education to an ever-increasing number of students. We now live in what might be called 'the knowledge society' which is characterised by constant change, so people must be prepared to update their knowledge by a life-long learning attitude. A teacher must try to understand the nature of learning, which is knowledge applied to something specific, and reflect upon the learning process of his/her students. However people learn in different ways, some researchers have attempted to classify learning. Teachers should be aware of the differences to try to match the different learning styles that may exist in a classroom. Some knowledge disciplines have learning styles that are more 'natural' -so to speakthan others, so by knowing the different available options used in different disciplines, such as design and management, a teacher may increase the success rate of delivering a class where the majority of willing students may learn.

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Quantitative Research: Examples of Research Questions and Solutions

Are you ready to embark on a journey into the world of quantitative research? Whether you’re a seasoned researcher or just beginning your academic journey, understanding how to formulate effective research questions is essential for conducting meaningful studies. In this blog post, we’ll explore examples of quantitative research questions across various disciplines and discuss how StatsCamp.org courses can provide the tools and support you need to overcome any challenges you may encounter along the way.

Understanding Quantitative Research Questions

Quantitative research involves collecting and analyzing numerical data to answer research questions and test hypotheses. These questions typically seek to understand the relationships between variables, predict outcomes, or compare groups. Let’s explore some examples of quantitative research questions across different fields:

• What is the relationship between class size and student academic performance?
• Does the use of technology in the classroom improve learning outcomes?
• How does parental involvement affect student achievement?
• What is the effect of a new drug treatment on reducing blood pressure?
• Is there a correlation between physical activity levels and the risk of cardiovascular disease?
• How does socioeconomic status influence access to healthcare services?
• What factors influence consumer purchasing behavior?
• Is there a relationship between advertising expenditure and sales revenue?
• How do demographic variables affect brand loyalty?

Stats Camp: Your Solution to Mastering Quantitative Research Methodologies

At StatsCamp.org, we understand that navigating the complexities of quantitative research can be daunting. That’s why we offer a range of courses designed to equip you with the knowledge and skills you need to excel in your research endeavors. Whether you’re interested in learning about regression analysis, experimental design, or structural equation modeling, our experienced instructors are here to guide you every step of the way.

Bringing Your Own Data

One of the unique features of StatsCamp.org is the opportunity to bring your own data to the learning process. Our instructors provide personalized guidance and support to help you analyze your data effectively and overcome any roadblocks you may encounter. Whether you’re struggling with data cleaning, model specification, or interpretation of results, our team is here to help you succeed.

Courses Offered at StatsCamp.org

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As you embark on your journey into quantitative research, remember that StatsCamp.org is here to support you every step of the way. Whether you’re formulating research questions, analyzing data, or interpreting results, our courses provide the knowledge and expertise you need to succeed. Join us today and unlock the power of quantitative research!

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A Study of Students' Learning Styles, Discipline Attitudes and Knowledge Acquisition in Technology-Enhanced Probability and Statistics Education

Nicolas christou, ivo d dinov.

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Email: [email protected]

Many modern technological advances have direct impact on the format, style and efficacy of delivery and consumption of educational content. For example, various novel communication and information technology tools and resources enable efficient, timely, interactive and graphical demonstrations of diverse scientific concepts. In this manuscript, we report on a meta-study of 3 controlled experiments of using the Statistics Online Computational Resources in probability and statistics courses. Web-accessible SOCR applets, demonstrations, simulations and virtual experiments were used in different courses as treatment and compared to matched control classes utilizing traditional pedagogical approaches. Qualitative and quantitative data we collected for all courses included Felder-Silverman-Soloman index of learning styles, background assessment, pre and post surveys of attitude towards the subject, end-point satisfaction survey, and varieties of quiz, laboratory and test scores. Our findings indicate that students' learning styles and attitudes towards a discipline may be important confounds of their final quantitative performance. The observed positive effects of integrating information technology with established pedagogical techniques may be valid across disciplines within the broader spectrum courses in the science education curriculum. The two critical components of improving science education via blended instruction include instructor training, and development of appropriate activities, simulations and interactive resources.

Keywords: statistics education, SOCR, blended instruction, IT teaching, evaluation and assessment, learning styles, applets

Introduction

Modern scientific, biomedical and humanitarian college curricula demand the integration of contemporary information technology tools with proven classical pedagogical approaches. This paradigm shift of blending established instructional instruments with novel technology-based instruction is fueled by the rapid advancement of technology, the ubiquitous use of the Internet in various aspects of life and by economic and social demands ( Ali, 2008 ; Dinov, 2008 ; Santos et al., 2008 ). The proliferation of diverse contemporary methods for teaching with technology is coupled with the need for scientific assessment of these new strategies for enhancing student motivation and improving quality of the learning process and extending the time of knowledge retention ( Dinov et al., 2008 ; Orlich et al., 2009 ).

The Statistics Online Computational Resource (SOCR) is a national center for statistical education and computing located at the University of California, Los Angles (UCLA). The goals of the SOCR project are to design, implement, validate and widely distribute new interactive tools and educational materials. SOCR efforts are focused on producing new and expanding existing Java applets, web-based course materials and interactive aids for technology enhanced instruction and statistical computing ( Che et al., 2009a ; Dinov, 2006 ; Dinov and Christou, 2009 ). Many SOCR resources are useful for instructors, students and researchers. All of these resources are freely available and anonymously accessible over the Internet ( www.SOCR.ucla.edu ).

SOCR is composed of four major components: computational libraries, interactive applets, hands-on activities and instructional resources. External programs typically use the SOCR libraries for statistical computing ( Ho et al., 2010 ) ( Sowell et al., 2010 ) ( Che et al., 2009b ). The interactive SOCR applets are further subdivided into six suites of tools: Distributions, Experiments, Analyses, Games, Modeler and Charts ( www.SOCR.ucla.edu ). Dynamic Wiki pages, http://wiki.stat.ucla.edu/socr/ , contain the hands-on activities and include a variety of specific instances of demonstrations of the SOCR applets. The SOCR instructional plans are collections composed of lecture notes, documentations, tutorials and guidelines about statistics education, e.g., the Probability and Statistics EBook: http://wiki.stat.ucla.edu/socr/index.php/EBook .

In this study, we investigate the effects of learning styles, teaching with technology, interactive simulations and quantitative measures of student performance in IT-blended probability and statistics classes. We also collected attitude data towards the subject at the beginning and the end of the quarter to determine the initial and final state of the students' perception of the subject of probability and statistics, as a quantitative discipline. The first specific research question we address is whether there is significant evidence that technology enhanced instruction facilitates knowledge retention, boosts motivation and improves student satisfaction (and if so, what is the practical size of the IT-instruction effect). The second question we tackle is whether there are learning-style specific effects that may influence the quantitative outcomes of traditional or IT-enhanced instruction.

The rapid technological advancements in recent years have led to the development of diverse tools and infrastructure of integrating science, education and technology. This in turn has expanded the variety of novel methods for learning and communication. Such recent studies ( Blasi and Alfonso, 2006 ; Dinov et al., 2008 ; Schochet, 2008 ) have demonstrated the power of this new paradigm of IT-based blended instruction. In the field of statistics educational research, there are a number of excellent examples of combining new pedagogical approaches with technological infrastructure that improve student motivation and enhance the learning process ( Kreijns et al., 2007 ; Meletiou-Mavrotheris et al., 2007 ).

The list of new technologies for delivering dynamic, linked, interactive and multidisciplinary learning content is large and quickly growing. Examples of such new IT resources include common web-places for course materials ( BlackBoard, 2008 ; Moodle, 2008 ), complete online courses ( UCLAX, 2008 ), Wikis ( SOCRWiki, 2008 ), interactive video streams ( LPB, 2008 ; YouTube, 2008 ), audio-visual classrooms, real-time educational blogs ( EduBlogs, 2008 ; TechEdBlogs, 2008 ), web-based resources for blended instruction ( WikiBooks, 2008 ), virtual office hours with instructors ( VOH, 2008 ), collaborative learning environments ( SAKAI, 2008 ), test-banks and exam-building tools ( MathNetTestBank, 2008 ) and resources for monitoring and assessment of learning ( ARTIST, 2008 ; WebWork, 2008 ).

The Felder-Silverman-Soloman Index of Learning Styles ( Felder, 1998 ; Felder, 2003 ) is a self-scoring instrument that assesses student learning preferences on a four dimensional scale – Sensing/Intuiting, Visual/Verbal, Active/Reflective and Sequential/Global. There are web-based and paper versions of the ILS, which may be utilized in various types of courses ( http://www.ncsu.edu/felder-public/ILSpage.html ).

The ILS allows instructors who assess the overall behavior of each class, adapt their teaching style to cover as much of the spectrum on each of the four dimensional axes as possible. Of course, this requires a commitment of time, resources and willingness to modify course curricula. Appropriate pedagogical utilization of the ILS may optimize and enhance the instructional process – i.e., targeted, enriched and stimulating learning environment may impact majority of students ( Felder, 1998 ). Each student completed the online ILS questionnaire consisting of 44 questions at the beginning of their Fall 2006 classes. Based on the answers they provided they received a score from −11 to 11 for each one of the four ILS categories: S1: Active-reflective (a score closer to +11 indicates that the student is more reflective than active); S2: Sensing-intuitive; S3: Visual-verbal; and S4: Global-sequential. We studied the overall students' quantitative performance against several independent variables (the four categories S1, S2, S3, S4, and students' attitudes towards probability and statistics).

There are different types of frameworks for describing learning styles of students, trainees and more generally – learners. Most of these define a learning style as some description of the perception, attitude and behavior on the part of the learner, which determines the individual's preferred way of acquiring new knowledge ( Cassidy, 2004 ; Honey and Mumford, 1982 ; Knowles and Smith, 2005 ; Sims, 1995 ). Individual learning styles are indirect reflections of various cognitive and psychological factors. A learning style typically indicates an individual's approach to responding to new learning stimuli. A very comprehensive and comparative review of many classical and contemporary models and theories of learning styles is available in ( Cassidy, 2004 ). This study provides a detailed description of the commonalities and differences of many learning style instruments based on their measurements, appropriate use and interpretation. It provides a broader appreciation of learning styles and discusses various instruments for measuring learning styles. One example of an interactive learning style survey is VARK ( VARK, 2008 ), which is a questionnaire that provides users with a profile of their learning preferences based on self-assessment of preferences to take-in or give-out information.

In the past, we have conducted several experiments where we studied student behaviors, learning preferences and comprehension based on IT enhanced curricula. One prior large-scale study ( Dinov et al., 2008 ) assessed the effectiveness of SOCR as an IT tool for enhancing undergraduate probability and statistics courses using different designs, and different classroom environments. We observed good outcomes in student satisfaction and use of technology in all three SOCR-treatment courses, compared to control sections exposed to classical instruction. In SOCR-treated courses, we found improved overall performance in the class, compared to matched traditional instruction. The treatment effect was very statistically significant, as the SOCR-treatment groups consistently performed better than the control group for all sections and across all assignments. The practical size of the observed IT-treatment effect was 1.5–3% improvement, modulated by a statistically significant p-value < 0.001 using a conservative non-parametric test. In this prior study, there were no statistically significant group differences in the overall quantitative assessment between the treatment and control groups, which could have been due to limited statistical power or lack of control for the learning styles. Yet, pooling the results across all courses involved in the experiment we saw a consistent trend of improvement in the SOCR treatment group ( Dinov et al., 2008 ).

In this manuscript, we report on the use of the ILS and various other categorical measurements to evaluate the associations between student performance, learning style and attitude towards the subject in several undergraduate probability and statistics courses.

Here we report on our findings of using the SOCR resources as instruments for IT-blended instruction in several courses. UCLA Institutional Review Board (IRB) approval was obtained to collect the appropriate data and conduct this study (IRB 05-396B/08-28-2006). Individual classes varied somewhat in their intrinsic designs, but generally, our courses included beginning of the quarter quizzes, ILS assessment, standard quarter-wide learning evaluation quantitative measures (exams, quizzes, homework, etc.), beginning and ending attitude towards the subject surveys. These common characteristics of our design are described below, and the course-specific design traits are presented in the Results section. At the end, Table 22 summarizes our study-design and research-findings in three probability and statistics courses.

Summary of our study-design and research-findings on the effects of learning styles, attitudes and technology-enhanced education in probability and statistics courses (see text).

Beginning of the quarter quiz

Each Fall 2006 course administered an entry subject-specific quiz (first day of classes) to assess background knowledge within the student population. The questions on these quizzes aimed at determining the appropriate elementary knowledge of probability and statistics, according to the course-specific prerequisites. These quizzes consisted of less than 10 questions and required about 15 minutes to complete. Example questions of an entry quiz are included in Box 1 (see Appendix). As these quizzes were given only to the treatment groups, see below, the results were only used to predict students' overall quantitative performance of their corresponding class. The goal of the quiz was to assess students' prior knowledge of probability and statistics. It was an attempt to find a predictor for students' future performance in the course. However, as shown later in the paper, the quiz scores did not show an association with the overall students' performance.

Box 1. Example questions of a beginning of the quarter quiz for assessing background student knowledge.

Learning Style Assessment

The ILS index is based on a model of learning, where students' learning styles are defined by their answers to four classes of questions:

Information processing: active (through engagement in physical activity and discussion), or reflective (through self-examination);

Type of preferential information perception: sensory (sights, sounds, physical sensations), or intuitive (possibilities, insights, hunches);

Preferred external information sensory channel: visual (pictures, diagrams, graphs, demonstrations), or auditory (words, sounds);

Understanding process: sequential (continual steps), or global (generative/holistic approach).

The ILS allows instructors who assess the overall behavior of each class, and perhaps adapt their teaching style to cover as much of the spectrum on each of the four dimensional axes as possible. Of course, this requires a commitment of time, resources and willingness to modify existent course curricula. If the ILS assessment is appropriately utilized in class, it is reasonable to assume that the instructional process is generally as optimal as possible – i.e., the learning environment is enriched and stimulating for most students in the class ( Felder, 1998 ).

Exam scores

All students enrolled in the traditional (control) or IT-enhanced (SOCR-treatment) groups took the same types of quantitative assessments including homework, laboratory assignments and exams. There were small variations between the gradebook allocations between the two separate studies/instructors, however, the control vs. treatment effects were only analyzed within class type and within each instructor. Instructors ensured that exams given to pairs of control-treatment courses were comparable and consistent (but not identical).

Pre, post and satisfaction Surveys

The pre- and post-attitude surveys were conducted at the beginning and the end of the quarter, respectfully. These were designed to inform us of students' mental position and emotion towards the subject of probability and statistics. The ordinal responses ranged from 1 (strongly disagree), through 4 (neither disagree nor agree) to 7 (strongly agree). These surveys took less than 10 minutes each. Box 2 (see Appendix) shows an example of our attitude questionnaire. Paired comparisons of these responses are indicative of potential alterations on the learners' philosophical and behavioral positions towards the educational discipline.

Box 2: The pre and post attitude survey.

Finally, we conducted a satisfaction survey (sat) at the end of the quarter that aimed at comparing the treatment and control classes to other similar courses. Box 3 (see Appendix) shows example questions that were included in this questionnaire. Responses on these 10-minute surveys could be used for comparing the treatment and control groups, as well as for evaluating the relation of the two pedagogical approaches to analogous types of classical or IT-based instruction.

Box 3: End-of-quarter satisfaction survey.

SOCR Treatment

The SOCR treatment involved three types of strategies blending traditional and technology-enhanced pedagogical strategies. First , demonstrations of interactive SOCR applets, simulations, virtual experiments and tools were shown during lecture (3 hrs/week). These demos were interleaved with the standard (control-type) instructional materials. Second , the discussions and laboratory classes (1–2 hrs/week), lead by teaching assistants, provided hands-on activities where students tested the applets and discussed of the probability and statistics concepts demonstrated by the corresponding interactive SOCR resources. Third , all homework assignments and projects required the use of the interactive SOCR web tools. In their assignments, students were asked to include and interpret snapshots of the final states of the appropriate SOCR applets or simulations they used to complete their projects. The control (traditional) treatment classes used classical instruction based on standard lecture/discussion format without hands-on technology demos or requirements for using web applets for completing papers and assignments. However, the controlled classes were shown graphs, results and computer outputs during class time.

Statistics 13 classes

In the Fall of 2006 we had two distinct SOCR-treatment Statistics 13 courses (Dinov and Christou), which were compared against classical instruction, by the same instructors (Fall 2005 and Winter 2006, respectively). The general description of the course and the section-specific results and findings for this sequence are discussed below.

Statistical Methods for the Life and Health Sciences (UCLA Stats 13) is an introductory course on statistical methods for the life and health sciences. Most enrolled students are bound for medical, graduate and professional schools after completing their undergraduate curricula. Brief outline of the course is available online at http://www.registrar.ucla.edu/archive/catalog/2005-07/catalog/catalog05-07-7-98.htm and the section-specific information is listed below. Each of the two sections in this study had about 90 students that received five hours of instruction a week – three lectures, one discussion and one laboratory. For discussion and laboratory, each section was split into three sub-sections conducted by teaching assistants. All students were assessed using the same gradebook schema and grade distribution. SOCR tools were used in lecture for demonstration, motivation and data analysis, as well as for projects, labs and homework.

Statistics 13.1 (Dinov)

The complete course description, coverage, assignments, class-notes, grading schema and all course related materials are available online at http://courses.stat.ucla.edu/06F/stat13_1 .

Table 1 (see Appendix for all tables ) depicts the distribution of the students in Stats 13.1 at the end of the quarter. The Fall 2006 (Fall 2005) classes started with a total enrollment of 90 students in the beginning of the quarter. There were significant differences between the treatment (2006) and control (2005) groups in the students' seniority-rankings ( p-value < 0.001 ), but no significant differences in the majors distributions.

Stats 13.1 student demographics (treatment and control groups). The values in table represent the enrollment in the Fall 2006 vs. (Fall 2005) classes, respectively.

A uniform grading schema was used in both the Fall 2005 (control) and the Fall 2006 (SOCR treatment) Stats 13 classes (Dinov). The lowest homework project was automatically dropped (only the top 7 homework scores counted). A standard letter-grade mapping was used based on quantitative overall average (e.g., 93%+ for A; 90-93% for A − ; 87–90% for B + ; 83–87% for B, etc.). Homework accounted for 20%, labs for 10%, midterm-exam for 30%, research term paper for 5% and the final exam for 35% of the final grade.

All students in the Fall 2006 Stats 13.1 class were exposed to SOCR enhanced instruction (treatment group) and all students in the Fall 2005 Stats 13.1 class (same instructor, Dinov) were subjected to the standard instructional curriculum using Stata ( STATA, 2008 ) (control group). Only the student demographics and the quantitative measures of learning (exam scores, homework, etc.) were comparable between the control and treatment groups. The ILS and attitude surveys were only available for the Fall 2006 class (treatment group).

Tables 2 , 3 , 4 , and 5 contain the results of the background quiz, the pre and post attitude surveys and the end-of-quarter satisfaction survey for Stats 13.1 (Dinov). There were no statistically significant differences in the responses to the 10 questions in the pre and post survey attitude effects ( Table 3 ). With exception of question e (considering statistics as a possible major/minor), the end-of-the-quarter satisfaction survey showed a consistent trend, Table 4 and Figure 1 , which may indicate an increase in the motivation and improved experiences in the treatment group (compared to unrelated other classes that did not use technology). The results of Part B of the final survey, Table 5 , did not indicate any significant trends or unexpected effects.

Results of the background knowledge quiz (see Box 1 ). Left side shows seven standard summary statistics, and the right side illustrates a histogram plot of all scores.

Results of the Pre vs. Post surveys (see Box 2 ).

Part A results of the satisfaction survey (see Box 3 ).

Part B results of the satisfaction survey (see Box 3 ).

Plot of the end-of-quarter satisfaction survey frequencies of responses to the 11 questions (see Box 3 and Table 4 ).

When comparing the quantitative learning outcomes, by type of assessment, between the treatment (Fall 2006) and control (Fall 2005) groups, we discovered mixed results. These mixed results may be partially accounted for by the fact that Statistics 13 has a required lab hour for the control group using Stata during the laboratories and lectures. This provided the control group with some exposure to technology-based instruction. The quantitative results from Dinov's Stats 13 classes are shown in Table 6 . For example, there was no statistical difference between the outcomes on the final exam, whereas there were significant differences between the treatment and the control groups in the overall and laboratory grades. The SOCR-treatment group (Fall 2006) had performed statistically significantly better than the control group, even though the practical size of the effect was within 3–5 percentage points. There were also trends of improvement on the midterm and homework scores for the treatment group; however these did not reach statistically significant levels.

Quantitative Results measuring student learning in the two Stats 13 classes (Dinov, Fall 2005, Fall 2006).

Figure 2 shows the results of the ILS assessment of the Stats 13.1 (Fall'06) SOCR-treatment section. These graphs represent the histograms for each of the 4 ILS categories. There is some evidence suggesting the majority of the SOCR treatment group students were more reflective (rather than active, S1), more intuitive (rather than sensing, S2), more verbal (rather than visual, S3), and more sequential (rather than global, S4). As the ILS survey was conducted in the beginning of classes, these self-identification results do not reflect the experience of the (treatment group) students during the quarter. There was significant evidence demonstrating the presence of positive S1, S2, S3 and S4 effects (test for proportion using Binomial distribution, with Ho: p <0 = p >0 , Ha: p <0 ≠ p >0 , p-values < 0.001 ).

Histogram plots of the 4-dimensional ILS responses for the Stats 13.1 treatment group (Fall'06).

The results of the (quantitative) performance regression on ILS, pre and post attitudes and satisfaction variable (see Methods section) are shown in Table 7 .

Regression results for the ILS effects on overall quantitative performance for the Stats 13.1 (Dinov).

Excluding the constant term, the only variable that was a significant predictor of overall performance at the 5% level was the initial (pre) attitude towards the discipline. The effect of the background quiz was borderline. None of the ILS spectrum variables played a significant role in explaining overall student performance. From these results, it appears as if the students' initial demeanor and affection for using technology was the only indicative factor on their overall quantitative performance in the Statistics 13 class.

Statistics 13.2 (Christou) Most students enrolled in Statistics 13 are bound for medical, graduate and professional schools. Table 8 shows a summary of the student populations enrolled in the control (Winter 2006) and SOCR-treatment (Fall 2006) groups. Again, there were significant differences between the treatment and control groups in the students' seniority-rankings (p-value < 0.001), but no significant differences in the majors distributions.

Stats 13.2 student demographics. The values in table represent the enrollment in the control (Winter 2006) and SOCR-treatment (Fall 2006) classes.

The final grade was computed based on three categories: homework, labs, and exams. The seven homework assignments accounted for 10% of the final grade and the six labs for another 10% of the final grade. There are five exams of which the first four were worth 15% each and the last was worth 20% of the final grade. A standard letter-grade mapping analogous to the Stats 13.1 study was used (see above).

Two classes of Statistics 13 (Winter 2006 and Fall 2006) served as control and treatment groups, respectively. The grading process was the same for both classes as described above. Both classes received three one-hour lectures per week plus one hour discussion time and one hour lab time per week. The control group was not exposed to any of the SOCR tools. The labs for the control group were done using the statistical software Stata ( STATA, 2008 ), while the treatment group used SOCR simulations and activities. The lectures of the SOCR treatment group frequently incorporated materials of SOCR. Besides these differences, everything else was kept the same for both groups.

Tables 9 , 10 , 11 , and 12 contain the results of the background quiz, the pre vs. post attitude surveys and the satisfaction surveys for Stats 13.2 (Christou). With some exceptions (e.g., Q9, t-test p-value=0.03), there were no significant longitudinal attitude effects as measured by the 10 questions in the pre and post attitude survey ( Table 10 ). Compared to Stats13.1, in this section (Stats 13.2) there was a different pattern of responses to the end-of-quarter survey. In this study, we observed a more consistent trend of increase in the motivation and improved experiences in the treatment group (compared to unrelated other classes that did not use technology), Table 11 and Figure 3 . Like in the Stats 13.1 study, the results of Part B of the final survey, Table 12 , did not indicate any significant trends or unexpected effects.

Results of the Pre vs. Post Attitude surveys (see Box 2 ).

Part A results of the satisfaction survey (see Box 3 and Figure 2 ).

Plot of the end-of-quarter satisfaction survey frequencies of responses to the 11 questions (see Box 3 and Table 11 ).

Table 13 shows the results of the quantitative assessment of the control and treatment groups. However, the overall performance again favors of the SOCR-treatment group. The results of exams 2 and 5, as well as the overall evaluation, provided strong evidence suggesting the treatment group performed better on these quantitative assessments.

Quantitative Results measuring student learning in the two Stats 13 classes (Christou, Winter 2006 vs. Fall 2006).

We now present the results of the analysis of the impact of the Index of Learning Styles (ILS) on students' learning (Stats 13.2, Christou). At the beginning of the course each student completed the online ILS questionnaire consisting of 44 questions. Based on the answers they provide, each student received a score from −11 to 11 for each one of the four categories. The results of the (quantitative) performance regression on ILS, pre and post attitudes and satisfaction (see Methods) are shown on Table 14 .

Regression results for the ILS effects on overall quantitative performance for Stats 13.2 (Christou).

The variables that were significant predictors of overall performance, at the 5% level, included the active-reflective (S1) and visual-verbal (S3) ILS measures and the attitude towards the discipline (post). The fact that the global-sequential and sensing-intuitive directions of the ILS spectrum did not play a significant role in explaining overall student performance makes the interpretation of the ILS results difficult. One possibility for explaining this observed effect is that an increase of the overall student performance directly correlates with both – a shift of the learners into the active (tendency to retain and understand information by doing or applying something active) and verbal (written or spoken word explanations) spectra of the ILS space. The effects of active-reflective (S1) are consistently negative, indicating that quantitative performance is inversely correlated with this ILS measure. That is, more active users (negative scale of the active-reflective axis) seem to do better on their qualitative examinations. The post-survey and the satisfaction survey also showed significant effects on predicting the students' quantitative performance in Stats 13.2.

Figure 4 shows the results of the ILS assessment of the Stats 13.2 (Fall'06, Christou) SOCR-treatment section. These graphs represent the histograms for each of the 4 ILS categories. As with the first study (Stats 13.1), there appears to be evidence suggesting the majority of the SOCR treatment group students were more reflective, intuitive, verbal and sequential , rather than active, sensing, visual and global. Again, as the ILS data was collected in the beginning of classes, these results do not reflect the experience of the (treatment group) students during the quarter. We observed significant evidence demonstrating the presence of positive (uni-directional) S1, S2, S3 and S4 effects ( p-values < 0.001 ).

Histogram plots of the 4-dimensional ILS responses for the Stats 13.2 treatment group (Fall'06).

Statistics 100A class. Introduction to Probability Theory (Stats 100A) is the first course in a three-course sequence. The other two are Introduction to Mathematical Statistics and Regression Analysis. Most enrolled students are from Mathematics, Economics, and Computer Science majors. A description of the course can be found at http://courses.stat.ucla.edu/index.php?term=05f&lecture=26330320 . The class meets 3 times a week with the instructor and once a week for a discussion with a teaching assistant.

Majority of the students enrolled in Statistics 100A were senior mathematics majors. Table 15 shows the student demographics for the control (Winter 2006) and SOCR-treatment (Fall 2006) classes. In this study, there were no significant differences between the treatment and control groups in the students' seniority-rankings or the majors distributions.

Stats 100A student demographics. The values in table represent the enrollment in the control (Fall 2005) and SOCR-treatment (Fall 2006) classes.

The final grade for the treatment group was computed based on three categories: homework, labs, and exams. The six homeworks accounted for 10% of the final grade and the five labs for another 10% of the final grade. There were five exams of which the first four were worth 15% each and the last was worth 20% of the final grade. The final grade for the control group was computed based on 2 categories – homeworks and exams. The homeworks accounted for 10% of the grade, and of the five exams three were worth 20% and the other two were worth 15%.

Two classes of Statistics 100A (Fall 2005 and Fall 2006) served as control and treatment groups, respectively. Both classes received three one-hour lectures per week plus one hour discussion time per week. The control group was not exposed to any of the SOCR tools or materials. The lectures of the treatment group integrated SOCR simulations, demonstrations and activities with the standard curriculum. In addition, the students of the treatment group were assigned SOCR labs, whereas students from the control group did not do assigned labs. Besides these differences, everything else was kept the same for both groups.

Tables 16 , 17 , 18 , and 19 contain the results of the background knowledge quiz, the pre vs. post attitude surveys, and the satisfaction survey for Stats 100A. Like in study 2 (Stats 13.2), some of the questions in the attitude survey showed statistically significant differences in the mean student responses between the pre and post surveys (e.g., Q1, t-test p-value <0.001), Table 17 . In this study we observed a more consistent trend of increase in the motivation and improved experiences in the treatment group (compared to unrelated other classes that did not use technology), Table 18 and Figure 5 . The results of Part B of the final survey, Table 19 , indicated some satisfaction with the use of SOCR materials and activities in the curriculum.

Results of the Pre vs. Post attitude surveys (see Box 2 ).

Plot of the end-of-quarter satisfaction survey frequencies of responses to the 11 questions (see Box 3 and Table 19 ).

In this study, the quantitative score comparison between the treatment and control groups show encouraging results. We compared the SOCR-treatment and control classes using two sample t-tests, Table 20 . Both of the Stats 100A courses (control and treatment) used the same grading style (exams and homework) and therefore are comparable.

Quantitative Results measuring student learning in the two Stats 100A classes (control, Fall 2005, and treatment, Fall 2006).

Figure 6 shows the results of the ILS assessment of the Stats 100A (Fall'06) SOCR-treatment section. These graphs represent the histograms for each of the 4 ILS categories. The same trend noticed in the 2 Stats 13 studies is observed here, although the shapes of the distributions are somewhat different. The majority of the SOCR treatment group students were again more reflective, intuitive, verbal and sequential , rather than active, sensing, visual and global. As in the previous 2 studies, we observed significant, albeit slightly weaker, evidence demonstrating the presence of positive (uni-directional) S1, S2, S3 and S4 effects ( p-values < 0.003 ).

Histogram plots of the 4-dimensional ILS responses for the Stats 100A treatment group (Fall'06).

We explored the overall students' performance with some independent variables (the four categories S1, S2, S3, S4, and students' attitudes towards the field of probability and statistics). The results of the (quantitative) performance regression on ILS, pre and post attitudes and satisfaction (see Methods) are shown on Table 21 . None of these variables represented significant predictors of the students' quantitative performance in Stats 100A.

Regression results for the ILS effects on overall quantitative performance for the Stats 100A (Christou).

General trends and analysis of results

Quantitative measurements in all three classes (two Stat 13 and one Stat 100A) showed that the treatment groups consistently outperformed the control groups. This was clear by the overall class performance, which includes all homework, labs, and exams (see Tables 6 , 13 , and 20 ). Undoubtedly, this illustrates that SOCR significantly affected students' performance for these three introductory Statistics classes at UCLA. We also received positive feedbacks from all three classes on the use of SOCR. As shown from the result of the end-of-quarter satisfaction survey, the majority of students indicated that technology helped them understand the main concepts of the course (see Tables 4 , 11 , and 18 , question a).

Especially for Stat 100A, which mostly includes students from Mathematical Sciences, we see an overall positive trend in their satisfaction survey. As seen in Table 19 , 97% of students indicated that SOCR made the class more interesting and 74% said SOCR made the class easier. This result demonstrates that SOCR has effects on students in the Mathematical Sciences field, and suggests that SOCR-embedded mathematics curricula improve student attitudes towards the class. Mathematics classes rarely include sections with hands-on processing and/or data exploration, like the SOCR tools and activities we used in our study. Our results illustrate that introduction of such pedagogical approaches in mathematics-oriented courses may improve motivation and enhance students' learning experiences.

In this study, we did not investigate the possible instructor-effects or the effects of the style of blending IT in the curriculum. The way in which instructors use technology in their courses can greatly affect students' experiences in the courses, and the outcome results may vary significantly based on the specific pedagogical utilization of technology. Future research studies should investigate more closely the effects of concrete implementations and use of technology in the classroom.

In this study, SOCR exposure in the treatment group included lecture and lab discussions; however the most striking differences between the treatment and control groups were the diametrically opposed laboratory sections. Thus, the magnitude of the observed differences between SOCR-treatment and control groups may have been in large aspects due to the laboratory sections conducted by teaching assistants. Laboratory assignments were written separately by each teaching assistant for their particular class. This could be another factor explaining differences in the findings between the two Statistics 13 courses (Dinov's and Christou's sections).

Table 22 depicts a summary of this meta-study. The columns in Table 22 show the data we had acquired for each of the 3 studies. Each row in this table contains references to appropriate figures and tables in the paper, and includes a brief annotation of the effect of this variable to discriminate the control and treatment groups or to predict the final quantitative outcomes (overall grades), as applicable. Notations : SSBGD= some significant between group differences (treatment vs. control), NSBGD= no significant between group differences, SUDE=significant uni-directional effects of the S1, S2, S3 & S4 ILS dimensions, NSPPD=no significant Pre vs. Post attitude differences (in treatment groups).

Novel communication and information technology tools provide the foundation for efficient, timely, interactive and graphical demonstrations of various scientific concepts in and out of the classroom. Now-a-days it is possible to conduct a complete investigative study using a web-browser and various interoperable tools for data collection, processing, visualization, analysis and interpretation. The SOCR resources provide a framework, where learners can use mouse clicks, copy and paste actions, and interactive web-based functions to go from data generation to data analysis and understanding within seconds, without demanding any special software, user-authentication or advanced hardware infrastructure.

Here, we reported on a meta-study of 3 controlled experiments of using SOCR resources vs. traditional pedagogical approaches. Qualitative and quantitative data we collected from all courses included Felder-Silverman-Soloman index of learning styles, quantitative background assessment, pre and post surveys of attitude towards the subject, end-point satisfaction survey, and varieties of examination, quiz and laboratory test scores. This study confirms the findings and significantly extends a previous report on the technology-driven improvement of the quantitative performance in probability and statistics courses ( Dinov et al., 2008 ). The results of the 10 pre- or post survey questions were not consistent between the 3 different classes (Cronbach's α = − 0.4495 ).

Students' learning styles and attitudes towards a discipline are important confounds of their final quantitative performance. We identified a marginal (within each study), yet very consistent (across all studies) effect of SOCR-treatment, which tends to increase student satisfaction (measured by post surveys) and improve quantitative performance (measured by standard assessment instruments). These observed positive effects of integrating information technology with established pedagogical techniques may also be valid across STEM disciplines ( Dinov, 2008 ; Dinov et al., 2008 ). The two critical components of improving science education via blended instruction include instructor-training and the development of appropriate curriculum- and audience-specific activities, simulations and interactive resources for data understanding. The beginning quiz taken by the treatment groups at the start of the courses was used to inform instructors about the students' level of understanding of basic concepts of probability and statistics. These results help instructors design activities specific to students' learning needs.

Simulations and virtual experiments provide powerful instructional tools that complement classical pedagogical approaches. Such tools are valuable in explaining difficult statistical concepts in probability and statistics classes. Utilizing visualization, graphical and computational simulation tools in teaching provides valuable complementary means of presenting concepts, properties and/or abstract ideas. In addition, such IT-based pedagogical instruments are appreciated and well received by students who normally operate in technological environments far exceeding these of their instructors. In our experiments, we saw effects of using SOCR simulation tools even when we did not completely stratify the student populations or control for all possible predictors (like age, major, learning style, background, attitude towards the subject, etc.) The effects we saw within each class provide marginal cues favoring technology-enhanced blended instruction. However, the results were very robust across all 3 studies and support other independent investigations ( Dinov et al., 2008 ; Kreijns et al., 2007 ). Our findings show that the students' learning styles can play important roles in their quantitative performance. Despite that, we would not blindly recommend that instructors employ technology-enhanced approaches to improve learning outcomes solely based on students' learning styles. There are advantages to broad spectrum training outside the domain of the students' preferred learning approach. For example, in multidisciplinary studies, active, visual or global learners may significantly benefit from exposure to reflective, verbal and/or sequential pedagogical styles.

Acknowledgements

This work was funded in part by NSF DUE grants 0716055 & 0442992, under the CCLI mechanism, and NIH Roadmap for Medical Research, NCBC Grant U54 RR021813. The SOCR resource is designed, developed and maintained by faculty and graduate students in the departments of Statistics, Computer Science, Laboratory of Neuro Imaging, Neurology and Biomedical Engineering at UCLA. All data-analyses and result-visualization was accomplished using SOCR Analyses, Modeler and Charts ( http://www.SOCR.ucla.edu ).

The help of our teaching assistants (Christopher Barr, Jackie Dacosta, Brandi Shantana and Judy Kong) was invaluable in the process of conducting this SOCR evaluation. We are also indebted to Juana Sanchez for her valuable feedback, PhuongThao Dinh for her insightful remarks, and Maykel Vosoughiazad for his help with the data processing. The authors are also indebted to the JOLT editors and reviewers for their constructive critiques and valuable recommendations.

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Research Project Guide

100 Research Questions Examples For Students

Explore 100 research questions examples for students to spark curiosity and guide your academic inquiries effectively.

Oct 31, 2024

Staring at a blank page and wondering how to start a research project can feel overwhelming. You know you need to get your head around the topic, but that vague “where do I go from here?” the feeling just keeps lingering. The good news is you’re not alone; chances are, you only need a good set of research questions to get you going. This guide will show you some examples of research questions that can help you conduct fast research and write efficiently. And if you’re looking for ways to streamline the process even more, Otio’s AI research and writing partner might be just what you need to get the job done.

Table Of Contents

What is a research question, how to find a good research question in 6 simple steps, types of research questions, supercharge your researching ability with otio — try otio for free today.

The Core of Your Research Project

A research question is the engine that drives your entire research project. It’s not just a question—it's what sets your study in motion and dictates its direction. By focusing your energy on crafting a solid research question, you pave the way for a more structured and meaningful investigation. The best research questions are clear and detailed enough that anyone can understand them without explanation. They’re also focused, allowing you to address them within whatever time constraints you’re working with.

Get Specific: Focus and Clarity Are Key

A good research question is laser-focused and doesn’t just wander around aimlessly. Consider you’re looking at a massive pile of data. Your research question is like a spotlight, illuminating only the information that matters to your study. It’s specific enough that you can answer it within the time you have, yet broad enough to be worth exploring in depth. And it’s not a yes-or-no question, but rather one that requires you to analyze and piece together different ideas before you can land an answer. 

The Art of Crafting: Keep It Short and Sweet

When you’re writing your research question, aim for brevity. A good question gets to the point without unnecessary fluff. The language should be straightforward to understand. This helps you stay on track and makes it easier for others to grasp what you’re trying to discover.

Be Argumentative: Invite Debate and Discussion

A practical research question invites debate and discussion. It doesn’t just reaffirm what we already know—it challenges existing ideas and proposes new ones. This is where things can get exciting as you explore uncharted territory and push the boundaries of what’s possible.

Guide the Entire Process

Your research question is like a compass guiding you through the entire research process. It helps you determine the research design and methodology, and it even plays a role in forming your hypothesis. By asking the right questions, you can gather valuable information that will ultimately lead you to your answer.

Why It’s So Important: Navigate with Purpose

Whether your project is qualitative or quantitative , a well-crafted research question provides a roadmap for both you and your audience. It ensures you avoid “all-about” papers that lack focus and direction. Instead, you can zero in on a specific thesis and build a compelling argument.

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2. Understand Your Assignment's Requirements

Before crafting a research question, you must grasp your assignment's requirements. Consider whether you need to test a proposition, evaluate data, or state and defend an argument. Review the assignment instructions and discuss them with your tutor or lecturer. Pinpointing the purpose will guide you in selecting an appropriate topic and framing your question effectively.

3. Picking a Research Topic That Excites You

Have you been given a list of topics, or are you free to choose? Clarify the guidelines with your tutor if needed. Choose a topic that genuinely interests you. Your enthusiasm will lead to deeper investment, creativity, and engaging and insightful assignments.

4. Conduct Initial Research to Inform Your Question

Before drafting your question, read key academic sources on your chosen topic. Focus on recently published works and influential texts. This stage is about familiarizing yourself with the primary debates and arguments in the field. Concentrate on the core ideas in introductions and conclusions—detailed note-taking can come later.

5. Narrow Your Focus for Depth

After some preliminary research, refine your topic to a specific issue or debate. Exploring one aspect in depth is more effective than skimming multiple areas. Consider subtopics, specific issues, and debates within the broader topic. Consider targeting a particular period, location, organization, or group. Focus on the points and arguments you want to make, and choose a subtopic or limitation that best supports this.

6. Crafting a Clear and Compelling Research Question

With your topic narrowed down, focus on writing your research question . This question should outline a straightforward task for you to complete. Keep in mind your assignment's purpose, which can vary across disciplines. Generally, good research questions require analysis. Questions starting with "how" and "why" are often more valuable than those starting with "what" or "describe." Consider using terms like critique, argue, examine, and evaluate to guide your inquiry.

Descriptive: What are the primary factors influencing crop yield in temperate climates?

Explanatory: Why do certain soil types yield higher grain production than others?

Exploratory: How might new organic farming techniques influence soil health over a decade?

Comparative: How do the growth rates differ between genetically modified and traditional corn crops?

Predictive: Based on current climate models, how will changing rain patterns impact wheat production in the next 20 years?

Animal Science

Descriptive: What are the common behavioral traits of domesticated cattle in grass-fed conditions?

Explanatory: Why do certain breeds of chickens have a higher egg production rate?

Exploratory: What potential benefits could arise from integrating tech wearables in livestock management?

Comparative: How does the milk yield differ between Holstein and Jersey cows when given the same diet?

Predictive: How might increasing global temperatures influence the reproductive cycles of swine?

Aquaculture

Descriptive: What are the most commonly farmed fish species in Southeast Asia?

Explanatory: Why do shrimp farms have a higher disease outbreak rate than fish farms?

Exploratory: How might innovative recirculating aquaculture systems revolutionize the industry's environmental impact?

Comparative: How do growth rates of salmon differ between open-net pens and land-based tanks?

Predictive: How will ocean acidification impact mollusk farming over the next three decades?

Descriptive: What tree species dominate the temperate rainforests of North America?

Explanatory: Why are certain tree species more resistant to pest infestations?

Exploratory: What are the benefits of integrating drone technology in forest health monitoring?

Comparative: How do deforestation rates compare between legally protected and unprotected areas in the Amazon?

Predictive: Given the increasing global demand for timber, how might tree populations in Siberia change in the next half-century?

Horticulture

Descriptive: What are the common characteristics of plants suitable for urban vertical farming?

Explanatory: Why do roses require specific pH levels in the soil for optimal growth?

Exploratory: What potential methods might promote year-round vegetable farming in colder regions?

Comparative: How does fruit yield differ between traditionally planted orchards and high-density planting systems?

Predictive: How might changing global temperatures affect wine grape production in traditional regions?

Soil Science

Descriptive: What are the main components of loamy soil?

Explanatory: Why does clay-rich soil retain more water compared to sandy soil?

Exploratory: How might biochar applications transform nutrient availability in degraded soils?

Comparative: How do nutrient levels vary between soils managed with organic versus inorganic fertilizers?

Predictive: Based on current farming practices, how will soil quality in the Midwest U.S. evolve over 30 years?

Architecture And Planning Examples 

Architectural design.

Descriptive: What are the dominant architectural styles of public buildings constructed in the 21st century?

Explanatory: Why do certain architectural elements from classical periods continue to influence modern designs?

Exploratory: How might sustainable materials revolutionize the future of architectural design?

Comparative: How do energy consumption levels differ between buildings with passive design elements and those without?

Predictive: Based on urbanization trends, how will the design of residential buildings evolve in the next two decades?

Landscape architecture

Descriptive: What are the primary components of a thriving urban park design?

Explanatory: Why do certain vegetation types promote more extraordinary biodiversity in urban settings?

Exploratory: What innovative techniques can restore and integrate wetlands into urban landscapes?

Comparative: How does visitor satisfaction vary between nature-inspired landscapes and more structured, geometric designs?

Predictive: With the effects of climate change, how might coastal landscape architecture adapt to rising sea levels over the coming century?

Urban Planning

Descriptive: What are the main components of a pedestrian-friendly city center?

Explanatory: Why do specific urban layouts promote more efficient traffic flow than others?

Exploratory: How might the integration of vertical farming impact urban food security and cityscape aesthetics?

Comparative: How do the air quality levels differ between cities with green belts and those without?

Predictive: How will urban planning strategies adjust to potentially reduced daily commutes based on increasing telecommuting trends?

Arts And Design Examples

Graphic design.

Descriptive: What are the prevailing typography trends in modern branding?

Explanatory: Why do certain color schemes evoke specific emotions or perceptions in consumers?

Exploratory: How is augmented reality reshaping the landscape of interactive graphic design?

Comparative: How do print and digital designs differ regarding elements and principles when targeting a young adult audience?

Predictive: Based on evolving digital platforms, what are potential future trends in web design aesthetics?

Industrial Design

Descriptive: What characterizes the ergonomic features of leading office chairs in the market?

Explanatory: Why have minimalist designs become more prevalent in consumer electronics over the past decade?

Exploratory: How might bio-inspired design influence the future of vehicles?

Comparative: How does user satisfaction differ between traditional versus modular product designs?

Predictive: Given the push towards sustainability, how will material selection evolve in the next decade of product design?

Multimedia arts

Descriptive: What techniques currently define the most popular virtual reality (VR) experiences?

Explanatory: Why do specific sound designs enhance immersion in video games more effectively than others?

Exploratory: How might holographic technologies revolutionize stage performances or public installations in the future?

Comparative: How do user engagement levels differ between 2D and 3D animations in educational platforms?

Predictive: With the rise of augmented reality (AR) wearables, what might be the next frontier in multimedia art installations?

Performing Arts

Descriptive: What styles of dance are currently predominant in global theater productions?

Explanatory: Why do certain rhythms or beats universally resonate with audiences across cultures?

Exploratory: How might digital avatars or AI entities play roles in future theatrical performances?

Comparative: How does audience reception differ between traditional plays and experimental, interactive performances?

Predictive: Considering global digitalization, how might virtual theaters redefine the experience of live performances in the future?

Visual Arts

Descriptive: What themes are prevalent in contemporary art exhibitions worldwide?

Explanatory: Why have mixed media installations become prominent in 21st-century art?

Exploratory: How is the intersection of technology and art opening new mediums or platforms for artists?

Comparative: How do traditional painting techniques, such as oil and watercolor, contrast in terms of texture and luminosity?

Predictive: With the evolution of digital art platforms, how might the definition and appreciation of "original" artworks change in the coming years?

Business and finance examples

Entrepreneurship

Descriptive: What do startups in the tech industry face the main challenges?

Explanatory: Why do some entrepreneurial ventures succeed while others fail within their first five years?

Exploratory: How are emerging digital platforms reshaping the entrepreneurial landscape?

Comparative: How do funding opportunities for entrepreneurs differ between North America and Europe?

Predictive: What sectors will see the most startup growth in the next decade?

Descriptive: What are the primary sources of external funding for large corporations?

Explanatory: Why did the stock market experience a significant drop in Q4 2022?

Exploratory: How might blockchain technology revolutionize the future of banking?

Comparative: How do the financial markets in developing countries compare to those in developed countries?

Predictive: Based on current economic indicators, what is the forecasted health of the global economy for the next five years?

Human Resources

Descriptive: What are the most sought-after employee benefits in the tech industry?

Explanatory: Why is there a high turnover rate in the retail sector?

Exploratory: How might the rise of remote work affect HR practices in the next decade?

Comparative: How do HR practices in multinational corporations differ from those in local companies?

Predictive: What skills will be in the highest demand in the workforce by 2030?

Descriptive: What are the core responsibilities of middle management in large manufacturing firms?

Explanatory: Why do some management strategies fail in diverse cultural environments?

Exploratory: How are companies adapting their management structures in response to the gig economy?

Comparative: How does the management style in Eastern companies compare with Western businesses?

Predictive: How might artificial intelligence reshape management practices in the next decade?

Descriptive: What are the most effective digital marketing channels for e-commerce businesses?

Explanatory: Why did a particular viral marketing campaign succeed in reaching a global audience?

Exploratory: How might virtual reality change the landscape of product advertising?

Comparative: How do marketing strategies differ between B2B and B2C sectors?

Predictive: What consumer behaviors will dominate online shopping trends in the next five years?

Operations Research

Descriptive: What are the primary optimization techniques used in supply chain management?

Explanatory: Why do certain optimization algorithms perform better in specific industries?

Exploratory: How can quantum computing impact the future of operations research?

Comparative: How does operations strategy differ between service and manufacturing industries?

Predictive: Based on current technological advancements, how might automation reshape supply chain strategies by 2035?"

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Qualitative Research Questions: Discovering the Unknown

1. exploratory questions: opening doors to new understandings.

Exploratory questions are designed to illuminate a topic without predetermined biases or expectations. They aim to uncover insights and gather foundational information. For instance, asking, "What are the experiences of first-time mothers navigating healthcare services in rural areas?" allows for organic discovery. Similarly, exploring "How do employees perceive the impact of remote work on their professional growth?" provides a platform for understanding diverse perspectives.

2. Predictive Questions: Peering into the Future

Predictive questions seek to understand future outcomes or intentions around a topic. They help in formulating expectations about what might happen next. For example, questions like "What motivates individuals to adopt eco-friendly practices in urban settings?" aim to identify potential trends. Similarly, asking, "What are the anticipated effects of social media on teenagers’ self-esteem over the next decade?" helps predict future implications based on current observations.

3. Interpretive Questions: Making Sense of Shared Experiences

Interpretive questions focus on understanding behaviors and experiences in their natural settings. They aim to comprehend how groups interpret and make sense of various phenomena. For example, "How do families experience and interpret the cultural significance of holiday traditions?" seeks to explore personal and collective interpretations. Questions like "In what ways do teachers adapt their methods to engage students in virtual classrooms?" investigate adaptive behaviors in changing environments.

Quantitative Research Questions: Testing the Hypothesis

1. descriptive questions: exploring the basics.

Descriptive questions are the most straightforward type of quantitative research question. They seek to explain the situation's who, what, when, where, and how. For instance, "What percentage of high school graduates go on to attend college in the United States?" provides an essential overview. Similarly, asking, "How often do patients in a specific age group visit their primary care physician annually?" reveals patterns in healthcare usage.

2. Comparative Questions: Drawing Meaningful Contrasts

Comparative questions are helpful when studying groups with dependent variables. They help compare one variable to another to identify significant differences. For example, "Is there a significant difference in job satisfaction between remote and on-site employees?" aims to highlight disparities. Similarly, asking, "How do stress levels differ between high school students and college students?" sets the stage for understanding comparative stress factors.

3. Relationship-based Questions: Exploring Influential Connections

Relationship-based questions examine whether one variable influences another. These questions are often used in experimental research to identify causal relationships. For instance, "Does the amount of screen time influence levels of physical activity in adolescents?" explores potential impacts. Similarly, asking, "Is there a correlation between income level and access to mental health services in urban areas?" seeks to identify influential connections.

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