2. American women
Question: | How often do British university students use Facebook each week? |
Variable: | Weekly Facebook usage |
Group: | British university students |
Question: | How often do male and female British university students upload photos and comment on other users' photos on Facebook each week? |
Variable: | 1. Weekly photo uploads on Facebook 2. Weekly comments on other users? photos on Facebook |
Group: | 1. Male, British university students 2. Female, British university students |
Question: | What are the most important factors that influence the career choices of Australian university students? |
Variable: | Factors influencing career choices |
Group: | Australian university students |
In each of these example descriptive research questions, we are quantifying the variables we are interested in. However, the units that we used to quantify these variables will differ depending on what is being measured. For example, in the questions above, we are interested in frequencies (also known as counts ), such as the number of calories, photos uploaded, or comments on other users? photos. In the case of the final question, What are the most important factors that influence the career choices of Australian university students? , we are interested in the number of times each factor (e.g., salary and benefits, career prospects, physical working conditions, etc.) was ranked on a scale of 1 to 10 (with 1 = least important and 10 = most important). We may then choose to examine this data by presenting the frequencies , as well as using a measure of central tendency and a measure of spread [see the section on Data Analysis to learn more about these and other statistical tests].
However, it is also common when using descriptive research questions to measure percentages and proportions , so we have included some example descriptive research questions below that illustrate this.
Question: | What percentage of American men and women exceed their daily calorific allowance? |
Variable: | Daily calorific intake |
Group: | 1. American men 2. American women |
Question: | What proportion of British male and female university students use the top 5 social networks? |
Variable: | Use of top 5 social networks (i.e. Facebook, MySpace, Twitter, LinkedIn, and Classmates) |
Group: | 1. Male, British university students 2. Female, British university students |
In terms of the first descriptive research question about daily calorific intake , we are not necessarily interested in frequencies , or using a measure of central tendency or measure of spread , but instead want understand what percentage of American men and women exceed their daily calorific allowance . In this respect, this descriptive research question differs from the earlier question that asked: How many calories do American men and women consume per day? Whilst this question simply wants to measure the total number of calories (i.e., the How many calories part that starts the question); in this case, the question aims to measure excess ; that is, what percentage of these two groups (i.e., American men and American women) exceed their daily calorific allowance, which is different for males (around 2500 calories per day) and females (around 2000 calories per day).
If you are performing a piece of descriptive , quantitative research for your dissertation, you are likely to need to set quite a number of descriptive research questions . However, if you are using an experimental or quasi-experimental research design , or a more involved relationship-based research design , you are more likely to use just one or two descriptive research questions as a means to providing background to the topic you are studying, helping to give additional context for comparative research questions and/or relationship-based research questions that follow.
Comparative research questions aim to examine the differences between two or more groups on one or more dependent variables (although often just a single dependent variable). Such questions typically start by asking "What is the difference in?" a particular dependent variable (e.g., daily calorific intake) between two or more groups (e.g., American men and American women). Examples of comparative research questions include:
Question: | What is the difference in the daily calorific intake of American men and women? |
Dependent variable: | Daily calorific intake |
Groups: | 1. American men 2. American women |
Question: | What is the difference in the weekly photo uploads on Facebook between British male and female university students? |
Dependent variable: | Weekly photo uploads on Facebook |
Groups: | 1. Male, British university students 2. Female, British university students |
Question: | What are the differences in usage behaviour on Facebook between British male and female university students? |
Dependent variable: | Usage behaviour on Facebook (e.g. logins, weekly photo uploads, status changes, commenting on other users' photos, app usage, etc.) |
Group: | 1. Male, British university students 2. Female, British university students |
Question: | What are the differences in perceptions towards Internet banking security between adolescents and pensioners? |
Dependent variable: | Perceptions towards Internet banking security |
Groups: | 1. Adolescents 2. Pensioners |
Question: | What are the differences in attitudes towards music piracy when pirated music is freely distributed or purchased? |
Dependent variable: | Attitudes towards music piracy |
Groups: | 1. Freely distributed pirated music 2. Purchased pirated music |
Groups reflect different categories of the independent variable you are measuring (e.g., American men and women = "gender"; Australian undergraduate and graduate students = "educational level"; pirated music that is freely distributed and pirated music that is purchased = "method of illegal music acquisition").
Comparative research questions also differ in terms of their relative complexity , by which we are referring to how many items/measures make up the dependent variable or how many dependent variables are investigated. Indeed, the examples highlight the difference between very simple comparative research questions where the dependent variable involves just a single measure/item (e.g., daily calorific intake) and potentially more complex questions where the dependent variable is made up of multiple items (e.g., Facebook usage behaviour including a wide range of items, such as logins, weekly photo uploads, status changes, etc.); or where each of these items should be written out as dependent variables.
Overall, whilst the dependent variable(s) highlight what you are interested in studying (e.g., attitudes towards music piracy, perceptions towards Internet banking security), comparative research questions are particularly appropriate if your dissertation aims to examine the differences between two or more groups (e.g., men and women, adolescents and pensioners, managers and non-managers, etc.).
Whilst we refer to this type of quantitative research question as a relationship-based research question, the word relationship should be treated simply as a useful way of describing the fact that these types of quantitative research question are interested in the causal relationships , associations , trends and/or interactions amongst two or more variables on one or more groups. We have to be careful when using the word relationship because in statistics, it refers to a particular type of research design, namely experimental research designs where it is possible to measure the cause and effect between two or more variables; that is, it is possible to say that variable A (e.g., study time) was responsible for an increase in variable B (e.g., exam scores). However, at the undergraduate and even master's level, dissertations rarely involve experimental research designs , but rather quasi-experimental and relationship-based research designs [see the section on Quantitative research designs ]. This means that you cannot often find causal relationships between variables, but only associations or trends .
However, when we write a relationship-based research question , we do not have to make this distinction between causal relationships, associations, trends and interactions (i.e., it is just something that you should keep in the back of your mind). Instead, we typically start a relationship-based quantitative research question, "What is the relationship?" , usually followed by the words, "between or amongst" , then list the independent variables (e.g., gender) and dependent variables (e.g., attitudes towards music piracy), "amongst or between" the group(s) you are focusing on. Examples of relationship-based research questions are:
Question: | What is the relationship between gender and attitudes towards music piracy amongst adolescents? |
Dependent variable: | Attitudes towards music piracy |
Independent variable: | Gender |
Group: | Adolescents |
Question: | What is the relationship between study time and exam scores amongst university students? |
Dependent variable: | Exam scores |
Independent variable: | Study time |
Group: | University students |
Question: | What is the relationship amongst career prospects, salary and benefits, and physical working conditions on job satisfaction between managers and non-managers? |
Dependent variable: | Job satisfaction |
Independent variable: | 1. Career prospects 2. Salary and benefits 3. Physical working conditions |
Group: | 1. Managers 2. Non-managers |
As the examples above highlight, relationship-based research questions are appropriate to set when we are interested in the relationship, association, trend, or interaction between one or more dependent (e.g., exam scores) and independent (e.g., study time) variables, whether on one or more groups (e.g., university students).
The quantitative research design that we select subsequently determines whether we look for relationships , associations , trends or interactions . To learn how to structure (i.e., write out) each of these three types of quantitative research question (i.e., descriptive, comparative, relationship-based research questions), see the article: How to structure quantitative research questions .
Table of contents, introduction.
How are statistical research questions for quantitative analysis written? This article provides five examples of statistical research questions that will allow statistical analysis to take place.
In quantitative research projects, writing statistical research questions requires a good understanding and the ability to discern the type of data that you will analyze. This knowledge is elemental in framing research questions that shall guide you in identifying the appropriate statistical test to use in your research.
Thus, before writing your statistical research questions and reading the examples in this article, read first the article that enumerates the four types of measurement scales . Knowing the four types of measurement scales will enable you to appreciate the formulation or structuring of research questions.
In writing the statistical research questions, I provide a topic that shows the variables of the study, the study description, and a link to the original scientific article to give you a glimpse of the real-world examples.
A study was conducted to determine the relationship between physical fitness and academic achievement. The subjects of the study include school children in urban schools.
Is there a significant relationship between physical fitness and academic achievement?
To allow statistical analysis to take place, there is a need to define what is physical fitness, as well as academic achievement. The researchers measured physical fitness in terms of the number of physical fitness tests that the students passed during their physical education class. It’s simply counting the ‘number of PE tests passed.’
On the other hand, the researchers measured academic achievement in terms of a passing score in Mathematics and English. The variable is the number of passing scores in both Mathematics and English.
Given the statistical research question, the appropriate statistical test can be applied to determine the relationship. A Pearson correlation coefficient test will test the significance and degree of the relationship. But the more sophisticated higher level statistical test can be applied if there is a need to correlate with other variables.
In the particular study mentioned, the researchers used multivariate logistic regression analyses to assess the probability of passing the tests, controlling for students’ weight status, ethnicity, gender, grade, and socioeconomic status. For the novice researcher, this requires further study of multivariate (or many variables) statistical tests. You may study it on your own.
Most of what I discuss in the statistics articles I wrote came from self-study. It’s easier to understand concepts now as there are a lot of resource materials available online. Videos and ebooks from places like Youtube, Veoh, The Internet Archives, among others, provide free educational materials. Online education will be the norm of the future. I describe this situation in my post about Education 4.0 .
This study attempted to correlate climate conditions with the decision of people in Ecuador to consume bottled water, including the volume consumed. Specifically, the researchers investigated if the increase in average ambient temperature affects the consumption of bottled water.
Is there a significant relationship between average temperature and amount of bottled water consumed?
Now, it’s easy to identify the statistical test to analyze the relationship between the two variables. You may refer to my previous post titled Parametric Statistics: Four Widely Used Parametric Tests and When to Use Them . Using the figure supplied in that article, the appropriate test to use is, again, Pearson’s Correlation Coefficient.
Source: Zapata (2021)
Statistical research question no. 3.
Note that this study on COVID-19 looked into three variables, namely 1) number of unique employees working in skilled nursing homes, 2) number of weekly confirmed cases among residents and staff, and 3) number of weekly COVID-19 deaths among residents.
We call the variable number of unique employees the independent variable , and the other two variables ( number of weekly confirmed cases among residents and staff and number of weekly COVID-19 deaths among residents ) as the dependent variables .
A simple Pearson test may be used to correlate one variable with another variable. But the study used multiple variables. Hence, they produced regression models that show how multiple variables affect the outcome. Some of the variables in the study may be redundant, meaning, those variables may represent the same attribute of a population. Stepwise multiple regression models take care of those redundancies. Using this statistical test requires further study and experience.
Scientific evidence has shown that surrounding greenness has multiple health-related benefits. Health benefits include better cognitive functioning or better intellectual activity such as thinking, reasoning, or remembering things. These findings, however, are not well understood. A study, therefore, analyzed the relationship between surrounding greenness and memory performance, with stress as a mediating variable.
As this article is behind a paywall and we cannot see the full article, we can content ourselves with the knowledge that three major variables were explored in this study. These are 1) exposure to and use of natural environments, 2) stress, and 3) memory performance.
As you become more familiar and well-versed in identifying the variables you would like to investigate in your study, reading studies like this requires reading the method or methodology section. This section will tell you how the researchers measured the variables of their study. Knowing how those variables are quantified can help you design your research and formulate the appropriate statistical research questions.
This recent finding is an interesting read and is available online. Just click on the link I provide as the source below. The study sought to determine if income plays a role in people’s happiness across three age groups: young (18-30 years), middle (31-64 years), and old (65 or older). The literature review suggests that income has a positive effect on an individual’s sense of happiness. That’s because more money increases opportunities to fulfill dreams and buy more goods and services.
If you click on the link to the full text of the paper on pages 10 and 11, you will read that the researcher measured happiness using a 10-point scale. The scale was categorized into three namely, 1) unhappy, 2) happy, and 3) very happy.
An investigation was conducted to determine if the size of nursing home staff and the number of COVID-19 cases are correlated. Specifically, they looked into the number of unique employees working daily, and the outcomes include weekly counts of confirmed COVID-19 cases among residents and staff and weekly COVID-19 deaths among residents.
Is there a significant relationship between income and happiness?
I do hope that upon reaching this part of the article, you are now well familiar on how to write statistical research questions. Practice makes perfect.
Lega, C., Gidlow, C., Jones, M., Ellis, N., & Hurst, G. (2021). The relationship between surrounding greenness, stress and memory. Urban Forestry & Urban Greening , 59 , 126974.
Måseide, H. (2021). Income and Happiness: Does the relationship vary with age?
© P. A. Regoniel 12 October 2021 | Updated 08 January 2024
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Educational resources and simple solutions for your research journey
A sound and effective research question is a key element that must be identified and pinned down before researchers can even begin their research study or work. A strong research question lays the foundation for your entire study, guiding your investigation and shaping your findings. Hence, it is critical that researchers spend considerable time assessing and refining the research question based on in-depth reading and comprehensive literature review. In this article, we will discuss how to write a strong research question and provide you with some good examples of research questions across various disciplines.
Table of Contents
A research question plays a crucial role in driving scientific inquiry, setting the direction and purpose of your study, and guiding your entire research process. By formulating a clear and focused research question, you lay the foundation for your investigation, ensuring that your research remains on track and aligned with your objectives so you can make meaningful contribution to the existing body of knowledge. A well-crafted research question also helps you define the scope of your study and identify the appropriate methodologies and data collection techniques to employ.
A good research question possesses several key components that contribute to the quality and impact of your study. Apart from providing a clear framework to generate meaningful results, a well-defined research question allows other researchers to understand the purpose and significance of your work. So, when working on your research question, incorporate the following elements:
A first step that will help save time and effort is knowing what your aims are and thinking about a few problem statements on the area or aspect one wants to study or do research on. Contemplating these statements as one undertakes more progressive reading can help the researcher in reassessing and fine-tuning the research question. This can be done over time as they read and learn more about the research topic, along with a broad literature review and parallel discussions with peer researchers and supervisors. In some cases, a researcher can have more than one research question if the research being undertaken is a PhD thesis or dissertation, but try not to cover multiple concerns on a topic.
A strong research question must be researchable, original, complex, and relevant. Here are five simple steps that can make the entire process easier.
Remember to adapt your research question to suit your purpose, whether it’s exploratory, descriptive, comparative, experimental, qualitative, or quantitative. Embrace the iterative nature of the research process, continually evaluating and refining your question as you progress. Here are some good examples of research questions across various disciplines.
Exploratory research question examples
Descriptive research question examples
Comparative research question examples
Experimental research question examples
Qualitative research question examples
Quantitative research question examples
With these simple guidelines and inspiring examples of research questions, you are equipped to embark on your research journey with confidence and purpose. Here’s wishing you all the best for your future endeavors!
References:
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Types of quantitative survey questions with examples, how to design quantitative survey questions.
Quantitative survey questions are defined as objective questions used to gain detailed insights from respondents about a survey research topic. The answers received for these quantitative survey questions are analyzed and a research report is generated on the basis of this
data . These questions form the core of a survey and are used to gather numerical data to determine statistical results.
The primary stage before conducting an online survey will be to decide the objective of the survey. Every research should have an answer to this integral question: “What are the expected results of your survey?”. Once the answer to this question is figured out, the secondary stage will be deciding the type of required data: quantitative or qualitative data .
LEARN ABOUT: Survey Mistakes And How to Avoid
Deciding the data type indicates the type of information required from the research process. While qualitative data provides detailed information about the subject, quantitative data will provide effective and precise information.
Quantitative survey questions are thus, channels for collecting quantitative data . Feedback received to quantitative survey questions is related to, measured by or measuring a “quantity” or a statistic and not the “quality” of the parameter.
Learn more: Survey Questions
Quantitative survey questions should be such that they offer respondents a medium to answer accurately. On the basis of this factor, quantitative survey questions are divided into three types:
1. Descriptive Survey Questions: Descriptive survey questions are used to gain information about a variable or multiple variables to associate a quantity to the variable.
It is the simplest type of quantitative survey questions and helps researchers in quantifying the variables by surveying a large sample of their target market.
LEARN ABOUT: Survey Sample Sizes
Most widely implemented descriptive analysis questions start with “What is this..”, “How much..”, “What is the percentage of..” and such similar questions. A popular example of a descriptive survey is an exit poll as it contains a question: “What is the percentage of candidate X winning this election?” or in a demographic segmentation survey: “How many people between the age of 18-25 exercise daily?”
Learn more: Demographic Survey Questions
Other examples of descriptive survey questions are:
In every example mentioned above, researchers should focus on quantifying the variable. The only factor that changes is the parameter of measurement. Every example mentions a different quantitative sample question which needs to be measured by different parameters.
LEARN ABOUT: Testimonial Questions
The answers for descriptive survey questions are definitional for the research topic and they quantify the topics of analysis. Usually, a descriptive research will require a long list of descriptive questions but experimental research or relationship-based research will be effective with a couple of descriptive survey questions.
Learn more: Quantitative Market Research & Descriptive Research vs Correlational Research
2. Comparative Survey Questions: Comparative survey questions are used to establish a comparison between two or more groups on the basis of one or more dependable variables. These quantitative survey questions begin with “What is the difference in” [dependable variable] between [two or more groups]?. This question will be enough to realize that the main objective of comparative questions is to form a comparative relationship between the groups under consideration.
LEARN ABOUT: Structured Question & Structured Questionnaire
Comparative survey question examples:
The various groups mentioned in the above-mentioned options indicate independent variables (Mexican people or country of students). These independent variables could be based on gender questions , ethnicity or education. It is the dependable variable that determines the complexity of comparative survey questions.
LEARN ABOUT: Average Order Value
3. Relationship Survey Questions: Relationship survey questions are used to understand the association, trends and causal comparative research relationship between two or more variables. When discussing research topics, the term relationship/causal survey questions should be carefully used since it is a widely used type of research design , i.e., experimental research – where the cause and effect between two or more variables. These questions start with “What is the relationship” [between or amongst] followed by a string of independent [gender or ethnicity] and dependent variables [career, political beliefs etc.]?
Learn more: What is Research?
There are four critical steps to follow while designing quantitative survey questions:
1. Select the type of quantitative survey question: The objective of the research is reflected in the chosen type of quantitative survey question. For the respondents to have a clear understanding of the survey, researchers should select the desired type of quantitative survey question.
2. Recognize the filtered dependent and independent variables along with the target group/s: Irrespective of the type of selected quantitative survey question (descriptive, comparative or relationship based), researchers should decide on the dependent and independent variables and also the target audiences .
LEARN ABOUT: Product Survey Questions
There are four levels of measurement variables – one of which can be chosen for creating quantitative survey questions. Nominal variables indicate the names of variables, Ordinal variables indicate names and order of variables, Interval variables indicate name, order and an established interval between ordered variables and Ratio variables indicate the name, order, an established interval and also an absolute zero value.
A variable can not only be calculated but also can be manipulated and controlled. For descriptive survey questions, there can be multiple variables for which questions can be formed. In the other two types of quantitative survey questions (comparative and relationship-based), dependent and independent variables are to be decided. Independent variables are those which are manipulated in order to observe the change in the dependent variables.
Learn more: Quantitative Observation
3. Choose the right structure according to the decided type of quantitative survey question: As discussed in the previous section, appropriate structures have to be chosen to create quantitative survey questions. The intention of creating these survey questions should align with the structure of the question.
LEARN ABOUT: Level of Analysis
This structure indicates – 1) Variables 2) Groups and 3) Order in which the variables and groups should appear in the question.
4. Note the roadblocks you are trying to solve in order to create a thorough survey question: Analyze the ease of reading these questions once the right structure is in place. Will the respondents be able to easily understand the questions? – Ensure this factor before finalizing the quantitative survey questions.
Learn more:
You can use QuestionPro for survey questions like income survey questions , Quantitative survey questions, Ethnicity survey questions, and life survey questions.
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The “Golden Thread” Explained Simply (+ Examples)
By: David Phair (PhD) and Alexandra Shaeffer (PhD) | June 2022
The research aims , objectives and research questions (collectively called the “golden thread”) are arguably the most important thing you need to get right when you’re crafting a research proposal , dissertation or thesis . We receive questions almost every day about this “holy trinity” of research and there’s certainly a lot of confusion out there, so we’ve crafted this post to help you navigate your way through the fog.
The golden thread simply refers to the collective research aims , research objectives , and research questions for any given project (i.e., a dissertation, thesis, or research paper ). These three elements are bundled together because it’s extremely important that they align with each other, and that the entire research project aligns with them.
Importantly, the golden thread needs to weave its way through the entirety of any research project , from start to end. In other words, it needs to be very clearly defined right at the beginning of the project (the topic ideation and proposal stage) and it needs to inform almost every decision throughout the rest of the project. For example, your research design and methodology will be heavily influenced by the golden thread (we’ll explain this in more detail later), as well as your literature review.
The research aims, objectives and research questions (the golden thread) define the focus and scope ( the delimitations ) of your research project. In other words, they help ringfence your dissertation or thesis to a relatively narrow domain, so that you can “go deep” and really dig into a specific problem or opportunity. They also help keep you on track , as they act as a litmus test for relevance. In other words, if you’re ever unsure whether to include something in your document, simply ask yourself the question, “does this contribute toward my research aims, objectives or questions?”. If it doesn’t, chances are you can drop it.
Alright, enough of the fluffy, conceptual stuff. Let’s get down to business and look at what exactly the research aims, objectives and questions are and outline a few examples to bring these concepts to life.
Simply put, the research aim(s) is a statement that reflects the broad overarching goal (s) of the research project. Research aims are fairly high-level (low resolution) as they outline the general direction of the research and what it’s trying to achieve .
True to the name, research aims usually start with the wording “this research aims to…”, “this research seeks to…”, and so on. For example:
“This research aims to explore employee experiences of digital transformation in retail HR.” “This study sets out to assess the interaction between student support and self-care on well-being in engineering graduate students”
As you can see, these research aims provide a high-level description of what the study is about and what it seeks to achieve. They’re not hyper-specific or action-oriented, but they’re clear about what the study’s focus is and what is being investigated.
The research objectives take the research aims and make them more practical and actionable . In other words, the research objectives showcase the steps that the researcher will take to achieve the research aims.
The research objectives need to be far more specific (higher resolution) and actionable than the research aims. In fact, it’s always a good idea to craft your research objectives using the “SMART” criteria. In other words, they should be specific, measurable, achievable, relevant and time-bound”.
Let’s look at two examples of research objectives. We’ll stick with the topic and research aims we mentioned previously.
For the digital transformation topic:
To observe the retail HR employees throughout the digital transformation. To assess employee perceptions of digital transformation in retail HR. To identify the barriers and facilitators of digital transformation in retail HR.
And for the student wellness topic:
To determine whether student self-care predicts the well-being score of engineering graduate students. To determine whether student support predicts the well-being score of engineering students. To assess the interaction between student self-care and student support when predicting well-being in engineering graduate students.
As you can see, these research objectives clearly align with the previously mentioned research aims and effectively translate the low-resolution aims into (comparatively) higher-resolution objectives and action points . They give the research project a clear focus and present something that resembles a research-based “to-do” list.
Finally, we arrive at the all-important research questions. The research questions are, as the name suggests, the key questions that your study will seek to answer . Simply put, they are the core purpose of your dissertation, thesis, or research project. You’ll present them at the beginning of your document (either in the introduction chapter or literature review chapter) and you’ll answer them at the end of your document (typically in the discussion and conclusion chapters).
The research questions will be the driving force throughout the research process. For example, in the literature review chapter, you’ll assess the relevance of any given resource based on whether it helps you move towards answering your research questions. Similarly, your methodology and research design will be heavily influenced by the nature of your research questions. For instance, research questions that are exploratory in nature will usually make use of a qualitative approach, whereas questions that relate to measurement or relationship testing will make use of a quantitative approach.
Let’s look at some examples of research questions to make this more tangible.
Again, we’ll stick with the research aims and research objectives we mentioned previously.
For the digital transformation topic (which would be qualitative in nature):
How do employees perceive digital transformation in retail HR? What are the barriers and facilitators of digital transformation in retail HR?
And for the student wellness topic (which would be quantitative in nature):
Does student self-care predict the well-being scores of engineering graduate students? Does student support predict the well-being scores of engineering students? Do student self-care and student support interact when predicting well-being in engineering graduate students?
You’ll probably notice that there’s quite a formulaic approach to this. In other words, the research questions are basically the research objectives “converted” into question format. While that is true most of the time, it’s not always the case. For example, the first research objective for the digital transformation topic was more or less a step on the path toward the other objectives, and as such, it didn’t warrant its own research question.
So, don’t rush your research questions and sloppily reword your objectives as questions. Carefully think about what exactly you’re trying to achieve (i.e. your research aim) and the objectives you’ve set out, then craft a set of well-aligned research questions . Also, keep in mind that this can be a somewhat iterative process , where you go back and tweak research objectives and aims to ensure tight alignment throughout the golden thread.
Alignment is the keyword here and we have to stress its importance . Simply put, you need to make sure that there is a very tight alignment between all three pieces of the golden thread. If your research aims and research questions don’t align, for example, your project will be pulling in different directions and will lack focus . This is a common problem students face and can cause many headaches (and tears), so be warned.
Take the time to carefully craft your research aims, objectives and research questions before you run off down the research path. Ideally, get your research supervisor/advisor to review and comment on your golden thread before you invest significant time into your project, and certainly before you start collecting data .
In this post, we unpacked the golden thread of research, consisting of the research aims , research objectives and research questions . You can jump back to any section using the links below.
As always, feel free to leave a comment below – we always love to hear from you. Also, if you’re interested in 1-on-1 support, take a look at our private coaching service here.
This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...
Thank you very much for your great effort put. As an Undergraduate taking Demographic Research & Methodology, I’ve been trying so hard to understand clearly what is a Research Question, Research Aim and the Objectives in a research and the relationship between them etc. But as for now I’m thankful that you’ve solved my problem.
Well appreciated. This has helped me greatly in doing my dissertation.
An so delighted with this wonderful information thank you a lot.
so impressive i have benefited a lot looking forward to learn more on research.
I am very happy to have carefully gone through this well researched article.
Infact,I used to be phobia about anything research, because of my poor understanding of the concepts.
Now,I get to know that my research question is the same as my research objective(s) rephrased in question format.
I please I would need a follow up on the subject,as I intends to join the team of researchers. Thanks once again.
Thanks so much. This was really helpful.
I know you pepole have tried to break things into more understandable and easy format. And God bless you. Keep it up
i found this document so useful towards my study in research methods. thanks so much.
This is my 2nd read topic in your course and I should commend the simplified explanations of each part. I’m beginning to understand and absorb the use of each part of a dissertation/thesis. I’ll keep on reading your free course and might be able to avail the training course! Kudos!
Thank you! Better put that my lecture and helped to easily understand the basics which I feel often get brushed over when beginning dissertation work.
This is quite helpful. I like how the Golden thread has been explained and the needed alignment.
This is quite helpful. I really appreciate!
The article made it simple for researcher students to differentiate between three concepts.
Very innovative and educational in approach to conducting research.
I am very impressed with all these terminology, as I am a fresh student for post graduate, I am highly guided and I promised to continue making consultation when the need arise. Thanks a lot.
A very helpful piece. thanks, I really appreciate it .
Very well explained, and it might be helpful to many people like me.
Wish i had found this (and other) resource(s) at the beginning of my PhD journey… not in my writing up year… 😩 Anyways… just a quick question as i’m having some issues ordering my “golden thread”…. does it matter in what order you mention them? i.e., is it always first aims, then objectives, and finally the questions? or can you first mention the research questions and then the aims and objectives?
Thank you for a very simple explanation that builds upon the concepts in a very logical manner. Just prior to this, I read the research hypothesis article, which was equally very good. This met my primary objective.
My secondary objective was to understand the difference between research questions and research hypothesis, and in which context to use which one. However, I am still not clear on this. Can you kindly please guide?
In research, a research question is a clear and specific inquiry that the researcher wants to answer, while a research hypothesis is a tentative statement or prediction about the relationship between variables or the expected outcome of the study. Research questions are broader and guide the overall study, while hypotheses are specific and testable statements used in quantitative research. Research questions identify the problem, while hypotheses provide a focus for testing in the study.
Exactly what I need in this research journey, I look forward to more of your coaching videos.
This helped a lot. Thanks so much for the effort put into explaining it.
What data source in writing dissertation/Thesis requires?
What is data source covers when writing dessertation/thesis
This is quite useful thanks
I’m excited and thankful. I got so much value which will help me progress in my thesis.
where are the locations of the reserch statement, research objective and research question in a reserach paper? Can you write an ouline that defines their places in the researh paper?
Very helpful and important tips on Aims, Objectives and Questions.
Thank you so much for making research aim, research objectives and research question so clear. This will be helpful to me as i continue with my thesis.
Thanks much for this content. I learned a lot. And I am inspired to learn more. I am still struggling with my preparation for dissertation outline/proposal. But I consistently follow contents and tutorials and the new FB of GRAD Coach. Hope to really become confident in writing my dissertation and successfully defend it.
As a researcher and lecturer, I find splitting research goals into research aims, objectives, and questions is unnecessarily bureaucratic and confusing for students. For most biomedical research projects, including ‘real research’, 1-3 research questions will suffice (numbers may differ by discipline).
Awesome! Very important resources and presented in an informative way to easily understand the golden thread. Indeed, thank you so much.
Well explained
The blog article on research aims, objectives, and questions by Grad Coach is a clear and insightful guide that aligns with my experiences in academic research. The article effectively breaks down the often complex concepts of research aims and objectives, providing a straightforward and accessible explanation. Drawing from my own research endeavors, I appreciate the practical tips offered, such as the need for specificity and clarity when formulating research questions. The article serves as a valuable resource for students and researchers, offering a concise roadmap for crafting well-defined research goals and objectives. Whether you’re a novice or an experienced researcher, this article provides practical insights that contribute to the foundational aspects of a successful research endeavor.
A great thanks for you. it is really amazing explanation. I grasp a lot and one step up to research knowledge.
I really found these tips helpful. Thank you very much Grad Coach.
I found this article helpful. Thanks for sharing this.
thank you so much, the explanation and examples are really helpful
This is a well researched and superbly written article for learners of research methods at all levels in the research topic from conceptualization to research findings and conclusions. I highly recommend this material to university graduate students. As an instructor of advanced research methods for PhD students, I have confirmed that I was giving the right guidelines for the degree they are undertaking.
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Learning objectives.
The type of research you are conducting will impact the research question that you ask. Probably the easiest questions to think of are quantitative descriptive questions. For example, “What is the average student debt load of MSW students?” is a descriptive question—and an important one. We aren’t trying to build a causal relationship here. We’re simply trying to describe how much debt MSW students carry. Quantitative descriptive questions like this one are helpful in social work practice as part of community scans, in which human service agencies survey the various needs of the community they serve. If the scan reveals that the community requires more services related to housing, child care, or day treatment for people with disabilities, a nonprofit office can use the community scan to create new programs that meet a defined community need.
Quantitative descriptive questions will often ask for percentage, count the number of instances of a phenomenon, or determine an average. Descriptive questions may only include one variable, such as ours about debt load, or they may include multiple variables. Because these are descriptive questions, we cannot investigate causal relationships between variables. To do that, we need to use a quantitative explanatory question.
Most studies you read in the academic literature will be quantitative and explanatory. Why is that? Explanatory research tries to build something called nomothetic causal explanations.Matthew DeCarlo says “com[ing]up with a broad, sweeping explanation that is universally true for all people” is the hallmark of nomothetic causal relationships (DeCarlo, 2018, chapter 7.2, para 5 ). They are generalizable across space and time, so they are applicable to a wide audience. The editorial board of a journal wants to make sure their content will be useful to as many people as possible, so it’s not surprising that quantitative research dominates the academic literature.
Structurally, quantitative explanatory questions must contain an independent variable and dependent variable. Questions should ask about the relation between these variables. A standard format for an explanatory quantitative research question is: “What is the relation between [independent variable] and [dependent variable] for [target population]?” You should play with the wording for your research question, revising it as you see fit. The goal is to make the research question reflect what you really want to know in your study.
Let’s take a look at a few more examples of possible research questions and consider the relative strengths and weaknesses of each. Table 4.1 does just that. While reading the table, keep in mind that it only includes some of the most relevant strengths and weaknesses of each question. Certainly each question may have additional strengths and weaknesses not noted in the table.
What are the internal and external effects/problems associated with children witnessing domestic violence? | Written as a question | Not clearly focused | How does witnessing domestic violence impact a child’s romantic relationships in adulthood? |
Considers relation among multiple concepts | Not specific and clear about the concepts it addresses | ||
Contains a population | |||
What causes foster children who are transitioning to adulthood to become homeless, jobless, pregnant, unhealthy, etc.? | Considers relation among multiple concepts | Concepts are not specific and clear | What is the relationship between sexual orientation or gender identity and homelessness for late adolescents in foster care? |
Contains a population | |||
Not written as a yes/no question | |||
How does income inequality predict ambivalence in the Stereo Content Model using major U.S. cities as target populations? | Written as a question | Unclear wording | How does income inequality affect ambivalence in high-density urban areas? |
Considers relation among multiple concepts | Population is unclear | ||
Why are mental health rates higher in white foster children then African Americans and other races? | Written as a question | Concepts are not clear | How does race impact rates of mental health diagnosis for children in foster care? |
Not written as a yes/no question | Does not contain a target population |
A good research question should also be specific and clear about the concepts it addresses. A group of students investigating gender and household tasks knows what they mean by “household tasks.” You likely also have an impression of what “household tasks” means. But are your definition and the students’ definition the same? A participant in their study may think that managing finances and performing home maintenance are household tasks, but the researcher may be interested in other tasks like childcare or cleaning. The only way to ensure your study stays focused and clear is to be specific about what you mean by a concept. The student in our example could pick a specific household task that was interesting to them or that the literature indicated was important—for example, childcare. Or, the student could have a broader view of household tasks, one that encompasses childcare, food preparation, financial management, home repair, and care for relatives. Any option is probably okay, as long as the researchers are clear on what they mean by “household tasks.”
Table 4.2 contains some “watch words” that indicate you may need to be more specific about the concepts in your research question.
Factors, Causes, Effects, Outcomes | What causes or effects are you interested in? What causes and effects are important, based on the literature in your topic area? Try to choose one or a handful that you consider to be the most important. |
Effective, Effectiveness, Useful, Efficient | Effective at doing what? Effectiveness is meaningless on its own. What outcome should the program or intervention have? Reduced symptoms of a mental health issue? Better socialization? |
Etc., and so forth | Get more specific. You need to know enough about your topic to clearly address the concepts within it. Don’t assume that your reader understands what you mean by “and so forth.” |
It can be challenging in social work research to be this specific, particularly when you are just starting out your investigation of the topic. If you’ve only read one or two articles on the topic, it can be hard to know what you are interested in studying. Broad questions like “What are the causes of chronic homelessness, and what can be done to prevent it?” are common at the beginning stages of a research project. However, social work research demands that you examine the literature on the topic and refine your question over time to be more specific and clear before you begin your study. Perhaps you want to study the effect of a specific anti-homelessness program that you found in the literature. Maybe there is a particular model to fighting homelessness, like Housing First or transitional housing that you want to investigate further. You may want to focus on a potential cause of homelessness such as LGBTQ discrimination that you find interesting or relevant to your practice. As you can see, the possibilities for making your question more specific are almost infinite.
In exploratory research, the researcher doesn’t quite know the lay of the land yet. If someone is proposing to conduct an exploratory quantitative project, the watch words highlighted in Table 4.2 are not problematic at all. In fact, questions such as “What factors influence the removal of children in child welfare cases?” are good because they will explore a variety of factors or causes. In this question, the independent variable is less clearly written, but the dependent variable, family preservation outcomes, is quite clearly written. The inverse can also be true. If we were to ask, “What outcomes are associated with family preservation services in child welfare?”, we would have a clear independent variable, family preservation services, but an unclear dependent variable, outcomes. Because we are only conducting exploratory research on a topic, we may not have an idea of what concepts may comprise our “outcomes” or “factors.” Only after interacting with our participants will we be able to understand which concepts are important.
Ask by terimakasih0 cc-0.
Guidebook for Social Work Literature Reviews and Research Questions Copyright © 2020 by Rebecca Mauldin and Matthew DeCarlo is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.
This guide will introduce the types of data analysis used in quantitative research, then discuss relevant examples and applications in the finance industry.
Table of Contents
What is quantitative data analysis and what is it for .
Quantitative data analysis is the process of interpreting meaning and extracting insights from numerical data , which involves mathematical calculations and statistical reviews to uncover patterns, trends, and relationships between variables.
Beyond academic and statistical research, this approach is particularly useful in the finance industry. Financial data, such as stock prices, interest rates, and economic indicators, can all be quantified with statistics and metrics to offer crucial insights for informed investment decisions. To illustrate this, here are some examples of what quantitative data is usually used for:
Ultimately, quantitative data analysis helps investors navigate the complex financial landscape and pursue profitable opportunities.
Although quantitative data analysis is a powerful tool, it cannot be used to provide context for your research, so this is where qualitative analysis comes in. Qualitative analysis is another common research method that focuses on collecting and analyzing non-numerical data , like text, images, or audio recordings to gain a deeper understanding of experiences, opinions, and motivations. Here’s a table summarizing its key differences between quantitative data analysis:
Types of Data Used | Numerical data: numbers, percentages, etc. | Non-numerical data: text, images, audio, narratives, etc |
Perspective | More objective and less prone to bias | More subjective as it may be influenced by the researcher’s interpretation |
Data Collection | Closed-ended questions, surveys, polls | Open-ended questions, interviews, observations |
Data Analysis | Statistical methods, numbers, graphs, charts | Categorization, thematic analysis, verbal communication |
Focus | and | and |
Best Use Case | Measuring trends, comparing groups, testing hypotheses | Understanding user experience, exploring consumer motivations, uncovering new ideas |
Due to their characteristics, quantitative analysis allows you to measure and compare large datasets; while qualitative analysis helps you understand the context behind the data. In some cases, researchers might even use both methods together for a more comprehensive understanding, but we’ll mainly focus on quantitative analysis for this article.
Once you have your data collected, you have to use descriptive statistics or inferential statistics analysis to draw summaries and conclusions from your raw numbers.
As its name suggests, the purpose of descriptive statistics is to describe your sample . It provides the groundwork for understanding your data by focusing on the details and characteristics of the specific group you’ve collected data from.
On the other hand, inferential statistics act as bridges that connect your sample data to the broader population you’re truly interested in, helping you to draw conclusions in your research. Moreover, choosing the right inferential technique for your specific data and research questions is dependent on the initial insights from descriptive statistics, so both of these methods usually go hand-in-hand.
With sophisticated descriptive statistics, you can detect potential errors in your data by highlighting inconsistencies and outliers that might otherwise go unnoticed. Additionally, the characteristics revealed by descriptive statistics will help determine which inferential techniques are suitable for further analysis.
One of the key statistical tests used for descriptive statistics is central tendency . It consists of mean, median, and mode, telling you where most of your data points cluster:
Another statistic to test in descriptive analysis is the measures of dispersion , which involves range and standard deviation, revealing how spread out your data is relative to the central tendency measures:
The shape of the distribution will then be measured through skewness.
While the core measures mentioned above are fundamental, there are additional descriptive statistics used in specific contexts, including percentiles and interquartile range.
Let’s illustrate these concepts with a real-world example. Imagine a financial advisor analyzing a client’s portfolio. They have data on the client’s various holdings, including stock prices over the past year. With descriptive statistics they can obtain the following information:
By calculating these descriptive statistics, the advisor gains a quick understanding of the client’s portfolio performance and risk distribution. For instance, they could use correlation analysis to see if certain stock prices tend to move together, helping them identify expansion opportunities within the portfolio.
While descriptive statistics provide a foundational understanding, they should be followed by inferential analysis to uncover deeper insights that are crucial for making investment decisions.
Inferential statistics analysis is particularly useful for hypothesis testing , as you can formulate predictions about group differences or potential relationships between variables , then use statistical tests to see if your sample data supports those hypotheses.
However, the power of inferential statistics hinges on one crucial factor: sample representativeness . If your sample doesn’t accurately reflect the population, your predictions won’t be very reliable.
Here are some of the commonly used tests for inferential statistics in commerce and finance, which can also be integrated to most analysis software:
If you’re a financial analyst studying the historical performance of a particular stock, here are some predictions you can make with inferential statistics:
Understanding these inferential analysis techniques can help you uncover potential relationships and group differences that might not be readily apparent from descriptive statistics alone. Nonetheless, it’s important to remember that each technique has its own set of assumptions and limitations . Some methods are designed for parametric data with a normal distribution, while others are suitable for non-parametric data.
Now that we have discussed the types of data analysis techniques used in quantitative research, here’s a quick guide to help you choose the right method and grasp the essential steps of quantitative data analysis.
Choosing between all these quantitative analysis methods may seem like a complicated task, but if you consider the 2 following factors, you can definitely choose the right technique:
The data used in quantitative analysis can be categorized into two types, discrete data and continuous data, based on how they’re measured. They can also be further differentiated by their measurement scale. The four main types of measurement scales include: nominal, ordinal, interval or ratio. Understanding the distinctions between them is essential for choosing the appropriate statistical methods to interpret the results of your quantitative data analysis accurately.
Discrete data , which is also known as attribute data, represents whole numbers that can be easily counted and separated into distinct categories. It is often visualized using bar charts or pie charts, making it easy to see the frequency of each value. In the financial world, examples of discrete quantitative data include:
Discrete data usually use nominal or ordinal measurement scales, which can be then quantified to calculate their mode or median. Here are some examples:
Conversely, continuous data can take on any value and fluctuate over time. It is usually visualized using line graphs, effectively showcasing how the values can change within a specific time frame. Examples of continuous data in the financial industry include:
Source: Freepik
The measurement scale for continuous data is usually interval or ratio . Here is breakdown of their differences:
You also need to make sure that the analysis method aligns with your specific research questions. If you merely want to focus on understanding the characteristics of your data set, descriptive statistics might be all you need; if you need to analyze the connection between variables, then you have to include inferential statistics as well.
Step 1: data collection .
Depending on your research question, you might choose to conduct surveys or interviews. Distributing online or paper surveys can reach a broad audience, while interviews allow for deeper exploration of specific topics. You can also choose to source existing datasets from government agencies or industry reports.
Raw data might contain errors, inconsistencies, or missing values, so data cleaning has to be done meticulously to ensure accuracy and consistency. This might involve removing duplicates, correcting typos, and handling missing information.
Furthermore, you should also identify the nature of your variables and assign them appropriate measurement scales , it could be nominal, ordinal, interval or ratio. This is important because it determines the types of descriptive statistics and analysis methods you can employ later. Once you categorize your data based on these measurement scales, you can arrange the data of each category in a proper order and organize it in a format that is convenient for you.
Based on the measurement scales of your variables, calculate relevant descriptive statistics to summarize your data. This might include measures of central tendency (mean, median, mode) and dispersion (range, standard deviation, variance). With these statistics, you can identify the pattern within your raw data.
Then, these patterns can be analyzed further with inferential methods to test out the hypotheses you have developed. You may choose any of the statistical tests mentioned above, as long as they are compatible with the characteristics of your data.
Now that you have the results from your statistical analysis, you may draw conclusions based on the findings and incorporate them into your business strategies. Additionally, you should also transform your findings into clear and shareable information to facilitate discussion among stakeholders. Visualization techniques like tables, charts, or graphs can make complex data more digestible so that you can communicate your findings efficiently.
We’ve compiled some commonly used quantitative data analysis tools and software. Choosing the right one depends on your experience level, project needs, and budget. Here’s a brief comparison:
Easiest | Beginners & basic analysis | One-time purchase with Microsoft Office Suite | |
Easy | Social scientists & researchers | Paid commercial license | |
Easy | Students & researchers | Paid commercial license or student discounts | |
Moderate | Businesses & advanced research | Paid commercial license | |
Moderate | Researchers & statisticians | Paid commercial license | |
Moderate (Coding optional) | Programmers & data scientists | Free & Open-Source | |
Steep (Coding required) | Experienced users & programmers | Free & Open-Source | |
Steep (Coding required) | Scientists & engineers | Paid commercial license | |
Steep (Coding required) | Scientists & engineers | Paid commercial license |
So how does this all affect the finance industry? Quantitative finance (or quant finance) has become a growing trend, with the quant fund market valued at $16,008.69 billion in 2023. This value is expected to increase at the compound annual growth rate of 10.09% and reach $31,365.94 billion by 2031, signifying its expanding role in the industry.
Quant finance is the process of using massive financial data and mathematical models to identify market behavior, financial trends, movements, and economic indicators, so that they can predict future trends.These calculated probabilities can be leveraged to find potential investment opportunities and maximize returns while minimizing risks.
There are several common quantitative strategies, each offering unique approaches to help stakeholders navigate the market:
This strategy aims for high returns with low volatility. It employs sophisticated algorithms to identify minuscule price discrepancies across the market, then capitalize on them at lightning speed, often generating short-term profits. However, its reliance on market efficiency makes it vulnerable to sudden market shifts, posing a risk of disrupting the calculations.
This strategy identifies and invests in assets based on factors like value, momentum, or quality. By analyzing these factors in quantitative databases , investors can construct portfolios designed to outperform the broader market. Overall, this method offers diversification and potentially higher returns than passive investing, but its success relies on the historical validity of these factors, which can evolve over time.
This approach prioritizes portfolio balance above all else. Instead of allocating assets based on their market value, risk parity distributes them based on their risk contribution to achieve a desired level of overall portfolio risk, regardless of individual asset volatility. Although it is efficient in managing risks while potentially offering positive returns, it is important to note that this strategy’s complex calculations can be sensitive to unexpected market events.
Quant analysts are beginning to incorporate these cutting-edge technologies into their strategies. Machine learning algorithms can act as data sifters, identifying complex patterns within massive datasets; whereas AI goes a step further, leveraging these insights to make investment decisions, essentially mimicking human-like decision-making with added adaptability. Despite the hefty development and implementation costs, its superior risk-adjusted returns and uncovering hidden patterns make this strategy a valuable asset.
Advantages of quantitative data analysis, minimum bias for reliable results.
Quantitative data analysis relies on objective, numerical data. This minimizes bias and human error, allowing stakeholders to make investment decisions without emotional intuitions that can cloud judgment. In turn, this offers reliable and consistent results for investment strategies.
Quantitative analysis generates precise numerical results through statistical methods. This allows accurate comparisons between investment options and even predictions of future market behavior, helping investors make informed decisions about where to allocate their capital while managing potential risks.
By analyzing large datasets and identifying patterns, stakeholders can generalize the findings from quantitative analysis into broader populations, applying them to a wider range of investments for better portfolio construction and risk management
Quantitative research is more suited to analyze large datasets efficiently, letting companies save valuable time and resources. The softwares used for quantitative analysis can automate the process of sifting through extensive financial data, facilitating quicker decision-making in the fast-paced financial environment.
Limited scope .
By focusing on numerical data, quantitative analysis may provide a limited scope, as it can’t capture qualitative context such as emotions, motivations, or cultural factors. Although quantitative analysis provides a strong starting point, neglecting qualitative factors can lead to incomplete insights in the financial industry, impacting areas like customer relationship management and targeted marketing strategies.
Breaking down complex phenomena into numerical data could cause analysts to overlook the richness of the data, leading to the issue of oversimplification. Stakeholders who fail to understand the complexity of economic factors or market trends could face flawed investment decisions and missed opportunities.
In conclusion, quantitative data analysis offers a deeper insight into market trends and patterns, empowering you to make well-informed financial decisions. However, collecting comprehensive data and analyzing them can be a complex task that may divert resources from core investment activity.
As a reliable provider, TEJ understands these concerns. Our TEJ Quantitative Investment Database offers high-quality financial and economic data for rigorous quantitative analysis. This data captures the true market conditions at specific points in time, enabling accurate backtesting of investment strategies.
Furthermore, TEJ offers diverse data sets that go beyond basic stock prices, encompassing various financial metrics, company risk attributes, and even broker trading information, all designed to empower your analysis and strategy development. Save resources and unlock the full potential of quantitative finance with TEJ’s data solutions today!
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What’s the best way to gather data that doesn’t leave you second-guessing?
If you’re dealing with research, you know how important it is to get solid, reliable data.
That’s where quantitative observation steps in.
In this article, we’ll look into everything you need to know about quantitative observation.
We’ll cover what it is, how it’s different from qualitative observation, and why it’s so widely used across various fields like education, healthcare, and marketing.
By the end, you’ll see why this method is a go-to for researchers who need precise, measurable results:
Quantitative observation is a research method that involves collecting and analyzing numerical data about people, objects, or events. It’s often used to measure specific variables, such as frequency, duration, or intensity. Quantitative observation can be conducted in various settings, including laboratories, classrooms, and public places.
When it comes to research, you’ll often hear about two main types of observations: quantitative and qualitative .
Both have their place, but they’re pretty different in what they focus on and how they’re used.
Let’s break it down.
Quantitative observations are all about numbers. If you can count it, measure it, or express it in figures, it falls into the quantitative camp.
Think of things like:
This type of observation gives you hard data that you can analyze and compare.
On the other hand, qualitative observations focus on descriptions. They’re about the qualities of what you’re observing.
For example, instead of saying, “The car is going 60 mph,” you’d say, “The car is moving quickly.” It’s more about what something is like than how much there is of it.
Quantitative observations are usually more objective. The data you gather isn’t influenced by opinions or feelings – it’s just numbers . This makes it reliable when you’re looking for facts that can be backed up by statistical analysis.
Qualitative observations, however, are more subjective.
They depend on the observer’s perspective and interpretation. Two people might describe the same event differently, which can make this type of observation more varied and rich, but also less consistent.
When you gather quantitative data, you’re looking for specific measurements.
This might include things like:
It’s precise and can be used in graphs, charts, and statistical models.
Qualitative data, though, is more about the details that don’t fit into neat little boxes.
It includes things like colors, textures, feelings, and experiences. This data is harder to measure, but it adds depth and context to your research.
Quantitative observation methods are usually standardized. You use the same tools and processes each time to make sure your data is consistent. This is great for making comparisons across different studies or groups.
Qualitative observation, in contrast, is more flexible. It allows you to explore your subject in a more open-ended way, which can lead to new insights and understanding that you might miss with a more rigid approach.
So, whether you’re counting heads or describing feelings, both quantitative and qualitative observations play important roles in research. Each brings something valuable to the table, helping you see the full picture.
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Quantitative observation has attractive advantages, and the most important ones are:
When you’re collecting quantitative observation data, you’re gathering facts that are clear-cut and free from personal bias.
This makes the data objective and reliable, which is a big deal in scientific research.
With these numbers in hand, you can engage in statistical analysis, where patterns and relationships start to emerge.
The beauty of this approach is that it strips away guesswork, leaving you with solid evidence that can back up your findings.
Unlike qualitative observation, which leans on descriptions, quantitative observations give you something concrete to work with.
When it comes to measuring and comparing variables, quantitative research is the tool of choice.
Quantitative observation methods focus on capturing exact values – whether it’s the height of a plant, the number of customers, or the temperature of a liquid.
This precision is key in the research process because it lets you compare different factors head-to-head.
With standardized observation techniques, the data you gather is consistent and reliable across the board.
It doesn’t matter if you’re working on a big project or just trying to understand a small detail, quantitative observations help you keep everything measured and comparable.
In scientific research, testing hypotheses is a key part of the job.
Quantitative observation research plays a huge role here.
Thanks to gathering quantitative data through systematic observation, you can put your ideas to the test.
The numbers you collect can either support your hypothesis or show you where things aren’t adding up.
Plus, as you gather more data, you start to see patterns and trends that weren’t obvious at first.
This is where quantitative and qualitative observation work hand in hand.
The hard numbers from quantitative research point you in the right direction, while qualitative observations add the context you need to understand the bigger picture.
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What is nominal data?
Quantitative observation can be used in a variety of fields, including:
Imagine a store tracking how many customers stop to look at a new product display or how long they spend browsing a particular aisle.
These numbers tell a story about what catches people’s attention and what doesn’t.
For instance, a study published in the International Journal of Advertising explored the effectiveness of retail window displays as part of the marketing mix.
The researchers worked with Boots the Chemists and Nottingham Business School to measure how window display design influences consumer-buying behavior.
They found that connecting buying behavior to specific marketing elements, like window displays, made sales forecasting more predictable.
If a lot of people are lingering by a new clothing line but not buying, it might suggest they’re interested but need a nudge, maybe a sale or better positioning.
This kind of data helps businesses tweak their strategies to match customer behavior.
In education, teachers often use quantitative observation to see how students are engaging with their lessons.
For example, a study presented in the Journal of Educational Psychology introduced the Behavioral Engagement Related to Instruction (BERI) protocol.
This protocol was specifically designed for large university classrooms to measure student engagement levels through quantitative observation data.
The BERI protocol involves tracking student behaviors in real-time, offering teachers immediate feedback on how well students are engaging with the material.
For instance, if students are actively participating in discussions or focusing on tasks during lectures, the data collected can show high levels of engagement.
On the other hand, if students appear distracted or disengaged, the data can highlight areas where the teaching method might need adjustment.
These numbers help educators identify which teaching strategies are working and which might need a different approach. If the protocol shows that students are more engaged during interactive lessons compared to traditional lectures, it indicates a need to incorporate more interactive elements into the curriculum.
This kind of targeted feedback helps instructors refine their methods to improve student learning outcomes.
Psychologists use quantitative observation to dig into the details of human behavior.
For example, a well-known study in the field of memory research conducted by Ebbinghaus in the late 19th century focused on how quickly people forget information.
In this study, participants were asked to memorize lists of nonsense syllables, and then their recall was tested at different time intervals.
The researchers measured how many syllables participants could remember after varying lengths of time, such as immediately after learning, after a few hours, and after several days.
The numbers collected from these tests helped to map out the “forgetting curve,” which shows that memory retention decreases sharply soon after learning but then levels off over time.
This type of quantitative data is often used in psychology, as it helps researchers understand how memory works and how factors like stress or fatigue might impact recall.
In sociology, quantitative observation helps researchers understand broader social trends.
A notable study published in the American Political Science Review examined voting behavior across various neighborhoods in a large metropolitan area.
The researchers collected quantitative data on voter turnout by tracking the number of people who participated in elections in different districts over several election cycles.
The study revealed that neighborhoods with lower voter turnout often had higher levels of economic disadvantage, lower educational attainment, and less access to transportation.
These patterns were not immediately obvious without the data. By analyzing the numbers, sociologists were able to identify the social factors that contributed to lower voting rates.
This type of research helps sociologists understand the underlying reasons for such trends and suggests potential interventions.
For instance, the findings might prompt community programs aimed at increasing voter education or improving access to polling stations.
Quantitative observation in sociology is essential for uncovering these hidden patterns and driving efforts to address social inequalities.
In healthcare, quantitative observation is useful for evaluating the effectiveness of medical treatments.
A well-known example is the clinical trial of the drug Streptomycin in the treatment of tuberculosis, conducted in the late 1940s.
This was one of the first randomized controlled trials (RCTs) in medical history, which set the standard for future clinical research.
In this study, researchers quantitatively observed and recorded the number of patients who showed improvement in their tuberculosis symptoms after taking Streptomycin compared to those who received a placebo.
The results showed a statistically significant improvement in the recovery rates among those treated with the drug, confirming its effectiveness.
This study provided clear evidence of the drug’s efficacy, shaping the future of tuberculosis treatment and demonstrating the power of quantitative observation in healthcare.
Thanks to systematically tracking patient outcomes, healthcare professionals were able to make informed decisions about adopting Streptomycin as a standard treatment.
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SurveyLab is a tool that takes quantitative observation to the next level.
If you’re looking to gather precise data and gain deep insights, this platform has you covered.
With SurveyLab, you can create online tests that score automatically and make data collection straightforward.
It doesn’t matter if you’re measuring customer satisfaction, employee engagement, or any other metric, the platform’s scoring mechanism helps you keep everything in check.
But SurveyLab isn’t just about gathering data – it’s about making sense of it.
The combination of scoring, metrics, data collection, and data analysis tools means you can conduct quantitative observations that lead to real, actionable insights.
It’s like having a full toolkit at your disposal, ready to help you make informed decisions based on solid data.
Ready to see how SurveyLab can change your quantitative observation efforts?
Try it today and access the insights that will drive your success.
And for more educational content, check our blog out .
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Quantitative research questions are the best way to collect quantifiable data. But how do you write such questions? Read this blog to get all your answers.
Learn the secrets of quantitative research with examples of quantitative research questions. How to formulate clear and concise inquiries.
Once you've read our guide on how to write a research question, you can use these examples to craft your own. ... Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.
What is a quantitative research question? Quantitative market research questions tell you the what, how, when, and where of a subject. From trendspotting to identifying patterns or establishing averages- using quantitative data is a clear and effective way to start solving business problems.
Learn all about quantitative research surveys, including types of quantitative survey questions, question formats, and quantitative question examples.
In this article, we have gathered 100+ quantitative research questions, form field examples, and explained how to create one.
In this post, we'll review what a quantitative research question is, cover the types of quantitative research questions, share examples of quantitative research questions across various fields, and highlight tips for creating a quantitative research survey.
Quantitative research questions typically fall into several categories, each serving a specific purpose within a study: Descriptive Questions: Aim to describe characteristics, frequencies, or trends within a population or sample. For example, "What percentage of customers prefer product A over product B?".
How to structure quantitative research questions There is no "one best way" to structure a quantitative research question. However, to create a well-structured quantitative research question, we recommend an approach that is based on four steps: (1) Choosing the type of quantitative research question you are trying to create (i.e., descriptive, comparative or relationship-based); (2 ...
Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.
See what quality research questions look like across multiple topic areas, including psychology, business, computer science and more.
Research Questions Tutorial Research Questions and Hypothesis Learn how to ask precise research questions and formulate clear hypotheses, including how to construct different types of questions and hypotheses, address the differences between correlation and causation, provide clear definitions for terms, and understand when to accept or reject a hypothesis. This is the second lesson in the ...
A good research question is essential to guide your research paper, dissertation, or thesis. All research questions should be: Focused on a single problem or issue. Researchable using primary and/or secondary sources. Feasible to answer within the timeframe and practical constraints. Specific enough to answer thoroughly.
Generally, in quantitative studies, reviewers expect hypotheses rather than research questions. However, both research questions and hypotheses serve different purposes and can be beneficial when used together.
Quantitative research questions are the gateway to unlocking a world of data-driven insights. Central to effective research, these questions help us quantify variables, compare groups, and establish relationships in a structured, objective manner. Definition: At their core, quantitative research questions seek measurable, numeric answers.
Quantitative Research Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions. This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection ...
The quantitative research design that we select subsequently determines whether we look for relationships, associations, trends or interactions. To learn how to structure (i.e., write out) each of these three types of quantitative research question (i.e., descriptive, comparative, relationship-based research questions), see the article: How to ...
In quantitative research projects, writing statistical research questions requires a good understanding and the ability to discern the type of data that you will analyze.
A well-written research question is a key element that must be identified and pinned down before researchers can even begin their research study or work. Read this article to learn how to write a strong research question with some good examples of research questions across disciplines.
Quantitative survey questions are defined as observational questions used to gain detailed insights from respondents about a survey research topic. Learn about quantitative survey question definition, types and examples.
Learn about research questions, objectives & aims (aka the "golden thread") Plain-language with loads of examples.
Quantitative descriptive questions The type of research you are conducting will impact the research question that you ask. Probably the easiest questions to think of are quantitative descriptive questions. For example, "What is the average student debt load of MSW students?" is a descriptive question—and an important one.
Although quantitative data analysis is a powerful tool, it cannot be used to provide context for your research, so this is where qualitative analysis comes in. Qualitative analysis is another common research method that focuses on collecting and analyzing non-numerical data, like text, images, or audio recordings to gain a deeper understanding ...
source. Psychology: studying human behavior and cognition. Psychologists use quantitative observation to dig into the details of human behavior. For example, a well-known study in the field of memory research conducted by Ebbinghaus in the late 19th century focused on how quickly people forget information.
Sampling in qualitative research has a different meaning than it does in quantitative research. Qualitative sampling, you are looking to find a group of individuals or a culture or a social organization in which you can get rich description of the load experience of either the question under inquiry or the culture or social organization under ...