Are you conducting market research? Qualitative research is an important first step in the market research process. In this guide, we’ll share 7 qualitative research methods for understanding your user.

Qualitative research is important for gaining a broad understanding of the underlying reasons and motivations behind consumer decisions.

We’ll share the qualitative research methods in just a moment, but before we dive in, let’s briefly discuss the basics.

What is Qualitative Market Research?

Qualitative market research is any research conducted using observation or unstructured questioning.

While quantitative research answers the what, where, when and who of decision making, qualitative research also answers the why and how.

Qualitative vs. Quantitative Research

The goal of qualitative research is to gain insights into the deeper motives behind consumer purchases.

The goal of the quantitative research, on the other hand, is to quantify and generalize the results so that the marketer can come to a final conclusion about the best course of action.

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Why use qualitative research as opposed to quantitative research?

Well, first of all, qualitative research should not be used instead of quantitative research. The two are complementary to each other.

Qualitative research in and of itself is not conclusive. However, it is used to…

  • Explain quantitative research results
  • Conduct market research when traditional surveys are not available (e.g. with embarrassing or “touchy” questions)
  • Conduct market research when more structured research is not possible

Qualitative research is a good first step to take when conducting your market research. Are you ready to learn how?

Great! Let’s dive into the 7 qualitative research techniques…

1. Individual Interviews

An individual interview can be conducted over the phone, Skype, or in person. The idea is to ask your ideal user (or an existing customer) a series of questions and follow-ups to learn what motivates them to buy a product like yours.

You should go into the interview with some questions prepared ahead of time, but don’t feel like you need to stick to a script. If they say something interesting, ask follow up questions that dig deeper. Really try to put yourself in their shoes, and try to figure out what makes them tick.

Here are a few initial questions you could ask:

  • What frustrates you in regard to [your topic]?
  • If I had a magic wand and could give you anything you want, what do you most desire?
  • What do you lose sleep over at night?
  • Have you bought [your type of product] before?
  • If so, what motivated you to buy it?

2. Focus Groups

Focus groups are generally conducted in-person. These groups are meant to provide a safe and comfortable environment for your users to talk about their thoughts and feelings surrounding your product.

focus-group

The advantage of using in-person focus groups is that you get to see the consumer’s verbal and non-verbal reaction to your product or advertising. The other advantage is that the different members of the group can bounce off each other’s thoughts and ideas, which means you’ll get even greater insights.

You can use focus groups to:

  • Test product usage or tasting
  • Explore the general concept of your product
  • Evaluate your advertising copy and imagery
  • Explore new packaging ideas

3. Observations or “Shop-Alongs”

An in-person observation of shopping behavior (or a “shop-along”) allows you to actually watch the consumer react to your product in-store. This way, you get to see their actual shopping behavior, as opposed to just what they would claim in a written survey.

shopper-eyetracking

One way that this is useful is by highlighting challenges that arise from shelf display issues, clutter, or out of stock issues. You may also interact with consumers to get deeper insights during the shopping process, to get feedback on a package design, for example.

4. In-Home Videos

In-home videos allow you to observe how users interact with your product in real life, at home.

The advantage of this method is that you get to observe user behavior in a natural, comfortable environment. This way, they can feel free to simply be themselves, and you’ll get a more realistic view of how your product is being used.

5. Lifestyle Immersion and Real World Dialogue

Lifestyle immersion is when you attend an event, such as a party or a family gathering. This allows you to get an uninterrupted view of your user’s attitudes and behaviors. This is another great way to get candid insight in a comfortable, familiar setting.

During these activities, observe your users having a dialogue with their friends. Listening in to real-world conversations is a really powerful way to get a deeper understanding of their desires, frustrations, and motivations.

6. Journal or Diary

Have your user (or potential user) keep a journal or a diary to document their experience with your topic or your product.

This can be handwritten or digital. Either way, it will allow you to capture your user’s actual voice, which is extremely valuable for marketing copy.

7. Online Focus Groups

Online focus groups are similar to in-person focus groups, except that they are more cost-efficient and allow you to reach more people.

Use social media to your advantage by creating communities of people who are interested in your topic, and fostering a conversation. Then, simply observe the dialogue. You’ll gain a lot of interesting insights!

How to Analyze Qualitative Data

At this point you may be wondering, how do you actually analyze qualitative data after you’ve gathered it?

Since qualitative data is unstructured, it can be tricky to draw conclusions from it, let alone present your findings. While it is not meant to be conclusive in and of itself, here are a few tips for analyzing qualitative research data

1. Summarize the Key Points

For interviews and focus groups, have the moderator write up some key points that they heard. For example: “Common concerns among participants in regard to our pizza were cheese overuse, greasiness, and bland sauce.”

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2. Code Responses

“Code” the unstructured data into something that can be summarized with tables or charts. For example, some coded responses to the question “When do you wear a watch?” might be 1 – never, 2 – once in a while, 3 – every day, etc.

3. Create a Word Cloud

Create a “word cloud” out of the keywords being used by the consumer. Just take your notes and put them into a word cloud generator, such as WordClouds.com. Then you’ll be able to easily spot the most prominent words.

i-have-a-dream-speech

That’s it! We shared 7 qualitative research methods that you can use to better understand your user or target customer.

Now it’s your turn. Go ahead and begin your market research by trying one of the techniques above.

 Source: This article was published on optinmonster.com By Mary Fernandez

Categorized in Research Methods

Much of the workings of the world today are controlled and powered by information, giving credence to that famous quote, “information is power”. Professionals, researchers, organizations, businesses, industries and even governments cannot function without information serving as “fuel” for decision-making, strategizing, gaining and storing knowledge.

But information is not something that is handed to anyone on a silver platter. It starts with a small raw fact or figure – or a set of raw facts and figures – that are not organized and, all too often, without meaning or context. These are called “data”. By itself, and in its raw form, data may seem useless.

Data will cease to be useless once it undergoes processing, where it will be organized, structured and given context through interpretation and analysis. Processing gives it meaning, effectively turning it into information that will eventually be of great use to those who need it. Collectively, all information will make up bodies of knowledge that will, in turn, benefit various users of this knowledge.

Without data, there won’t be any information. Therefore, no matter how data may seem random and useless, it is actually considered to be the most important and basic unit of any information structure or body of knowledge.

To that end, various approaches, tools and methodologies aimed at gathering or collecting data have been formulated.

THE MEANING OF DATA COLLECTION

Whether it is business, marketing, humanities, physical sciences, social sciences, or other fields of study or discipline, data plays a very important role, serving as their respective starting points. That is why, in all of these processes that involve the usage of information and knowledge, one of the very first steps is data collection.

Data collection is described as the “process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer queries, stated research questions, test hypotheses, and evaluate outcomes.”

Depending on the discipline or field, the nature of the information being sought, and the objective or goal of users, the methods of data collection will vary. The approach to applying the methods may also vary, customized to suit the purpose and prevailing circumstances, without compromising the integrity, accuracy and reliability of the data.

There are two main types of data that users find themselves working with – and having to collect.

  1. Quantitative Data. These are data that deal with quantities, values or numbers, making them measurable. Thus, they are usually expressed in numerical form, such as length, size, amount, price, and even duration. The use of statistics to generate and subsequently analyze this type of data add credence or credibility to it, so that quantitative data is overall seen as more reliable and objective.
  2. Qualitative Data. These data, on the other hand, deals with quality, so that they are descriptive rather than numerical in nature. Unlike quantitative data, they are generally not measurable, and are only gained mostly through observation. Narratives often make use of adjectives and other descriptive words to refer to data on appearance, color, texture, and other qualities.

In most cases, these two data types are used as preferences in choosing the method or tool to be used in data collection. As a matter of fact, data collection methods are classified into two, and they are based on these types of data. Thus, we can safely say that there are two major classifications or categories of data collection methods: the quantitative data collection methods and the qualitative data collection methods.

IMPORTANCE OF DATA COLLECTION

From the definition of “data collection” alone, it is already apparent why gathering data is important: to come up with answers, which come in the form of useful information, converted from data.

But for many, that still does not mean much.

Depending on the perspective of the user and the purpose of the information, there are many concrete benefits that can be gained from data gathering. In general terms, here are some of the reasons why data collection is very important. The first question that we will address is: “why should you collect data?”

Data collection aids in the search for answers and resolutions.

Learning and building knowledge is a natural inclination for human beings. Even at a very young age, we are in search for answers to a lot of things. Take a look at toddlers and small children, and they are the ones with so many questions, their curious spirit driving them to repeatedly ask whatever piques their interest.

A toddler curious about a white flower in the backyard will start collecting data. He will approach the flower in question and look at it closely, taking in the color, the soft feel of the petals against his skin, and even the mild scent that emanates from it. He will then run to his mother and pull her along until they got to where the flower is. In baby speak, he will ask what the flower’s name is, and the mother will reply, “It’s a flower, and it is called rose.”

It’s white. It’s soft. It smells good. And now the little boy even has a name for it. It’s called a rose. When his mother wasn’t looking, he reached for the rose by its stem and tried to pluck it. Suddenly, he felt a prickle in his fingers, followed by a sharp pain that made him yelp. When he looked down at his palm, he saw two puncture marks, and they are bleeding.

The little boy starts to cry, thinking how roses, no matter how pretty and good-smelling, are dangerous and can hurt you. This information will now be embedded in his mind, sure to become one of the most enduring pieces of information or tidbit of knowledge that he will know about the flower called “rose”.

The same goes in case of a marketing research, for example. A company wants to learn a few things about the market in order to come up with a marketing plan, or tweak an already existing marketing program. There’s no way that they will be able to do these things without collecting the relevant data.

Data collection facilitates and improves decision-making processes, and the quality of the decisions made.

Leaders cannot make decisive strategies without facts to support them. Planners cannot draw up plans and designs without a basis. Entrepreneurs could not possibly come up with a business idea – much less a viable business plan – out of nothing at all. Similarly, businesses won’t be able to formulate marketing plans, and implement strategies to increase profitability and growth, if they have no data to start from.

Without data, there won’t be anything to convert into useful information that will provide the basis for decisions. All that decision-makers are left with is their intuition and gut feeling, but even gut feeling and instinct have some basis on facts.

Decision-making processes become smoother, and decisions are definitely better, if there is data driving them. According to a survey by Helical IT, the success rate of decisions based on data gathered is higher by 79% than those made using pure intuition alone.

In business, one of the most important decisions that must be made is on resource allocation and usage. If they collect the relevant data, they will be able to make informed decisions on how to use business resources efficiently.

Data collection improves quality of expected results or output.

Just as having data will improve decision-making and the quality of the decisions, it will also improve the quality of the results or output expected from any endeavor or activity. For example, a manufacturer will be able to produce high quality products after designing them using reliable data gathered. Consumers will also find the claims of the company about the product to be more reliable because they know it has been developed after conducting significant amount of research.

Through collecting data, monitoring and tracking progress will also be facilitated. This gives a lot of room for flexibility, so response can be made accordingly and promptly. Adjustments can be made and improvements effected.

Now we move to the next question, and that is on the manner of collecting data. Why is there a need to be particular about how data is collected? Why does it have to be systematic, and not just done on the fly, using whatever makes the data gatherer comfortable? Why do you have to pick certain methodologies of data collection when you can simply be random with it?

  • Collecting data is expensive and resource-intensive. It will cost you money, time, and other resources. Thus, you have to make sure you make the most of it. You cannot afford to be random and haphazard about how you gather data when there are large amounts of investment at stake.
  • Data collection methods will help ensure the accuracy and integrity of data collected. It’s common sense, really. Using the right data collection method – and using it properly – will allow only high quality data to be gathered. In this context, high quality data refers to data that is free from errors and bias arising from subjectivity, thereby increasing their reliability. High quality and reliable data will then be processed, resulting to high quality information.

METHODS OF DATA COLLECTION

We’ll now take a look at the different methods or tools used to collect data, and some of their pros (+) and cons (-). You may notice some methods falling under both categories, which means that they can be used in gathering both types of data.

I. Qualitative Data Collection Methods

Exploratory in nature, these methods are mainly concerned at gaining insights and understanding on underlying reasons and motivations, so they tend to dig deeper. Since they cannot be quantified, measurability becomes an issue. This lack of measurability leads to the preference for methods or tools that are largely unstructured or, in some cases, maybe structured but only to a very small, limited extent.

Generally, qualitative methods are time-consuming and expensive to conduct, and so researchers try to lower the costs incurred by decreasing the sample size or number of respondents.

Face-to-Face Personal Interviews

This is considered to be the most common data collection instrument for qualitative research, primarily because of its personal approach. The interviewer will collect data directly from the subject (the interviewee), on a one-on-one and face-to-face interaction. This is ideal for when data to be obtained must be highly personalized.

The interview may be informal and unstructured – conversational, even – as if taking place between two casual to close friends. The questions asked are mostly unplanned and spontaneous, with the interviewer letting the flow of the interview dictate the next questions to be asked.

However, if the interviewer still wants the data to be standardized to a certain extent for easier analysis, he could conduct a semi-structured interview where he asks the same series of open-ended questions to all the respondents. But if they let the subject choose her answer from a set of options, what just took place is a closed, structured and fixed-response interview.

  • (+) This allows the interviewer to probe further, by asking follow-up questions and getting more information in the process.
  • (+) The data will be highly personalized (particularly when using the informal approach).
  • (-) This method is subject to certain limitations, such as language barriers, cultural differences, and geographical distances.
  • (-) The person conducting the interview must have very good interviewing skills in order to elicit responses.

Qualitative Surveys

  • Paper surveys or questionnaires. Questionnaires often utilize a structure comprised of short questions and, in the case of qualitative questionnaires, they are usually open-ended, with the respondents asked to provide detailed answers, in their own words. It’s almost like answering essay questions.
    • (+) Since questionnaires are designed to collect standardized data, they are ideal for use in large populations or sample sizes of respondents.
    • (+) The high amount of detail provided will aid analysis of data.
    • (-) On the other hand, the large number of respondents (and data), combined with the high level and amount of detail provided in the answers, will make data analysis quite tedious and time-consuming.
  • Web-based questionnaires. This is basically a web-based or internet-based survey, involving a questionnaire uploaded to a site, where the respondents will log into and accomplish electronically. Instead of a paper and a pen, they will be using a computer screen and the mouse.
    • (+) Data collection is definitely quicker. This is often due to the questions being shorter, requiring less detail than in, say, a personal interview or a paper questionnaire.
    • (+) It is also uncomplicated, since the respondents can be invited to answer the questionnaire by simply sending them an email containing the URL of the site where the online questionnaire is available for answering.
    • (-) There is a limitation on the respondents, since the only ones to be able to answer are those who own a computer, have internet connection, and know their way around answering online surverys.
    • (-) The lesser amount of detail provided means the researcher may end up with mostly surface data, and no depth or meaning, especially when the data is processed.

Focus Groups

Focus groups method is basically an interview method, but done in a group discussion setting. When the object of the data is behaviors and attitudes, particularly in social situations, and resources for one-on-one interviews are limited, using the focus group approach is highly recommended. Ideally, the focus group should have at least 3 people and a moderator to around 10 to 13 people maximum, plus a moderator.

Depending on the data being sought, the members of the group should have something in common. For example, a researcher conducting a study on the recovery of married mothers from alcoholism will choose women who are (1) married, (2) have kids, and (3) recovering alcoholics. Other parameters such as the age, employment status, and income bracketdo not have to be similar across the members of the focus group.

The topic that data will be collected about will be presented to the group, and the moderator will open the floor for a debate.

  • (+) There may be a small group of respondents, but the setup or framework of data being delivered and shared makes it possible to come up with a wide variety of answers.
  • (+) The data collector may also get highly detailed and descriptive data by using a focus group.
  • (-) Much of the success of the discussion within the focus group lies in the hands of the moderator. He must be highly capable and experienced in controlling these types of interactions.

Documental Revision

This method involves the use of previously existing and reliable documents and other sources of information as a source of data to be used in a new research or investigation. This is likened to how the data collector will go to a library and go over the books and other references for information relevant to what he is currently researching on.

  • (+) The researcher will gain better understanding of the field or subject being looked into, thanks to the reliable and high quality documents used as data sources.
  • (+) Taking a look into other documents or researches as a source will provide a glimpse of the subject being looked into from different perspectives or points of view, allowing comparisons and contrasts to be made.
  • (-) Unfortunately, this relies heavily on the quality of the document that will be used, and the ability of the data collector to choose the right and reliable documents. If he chooses wrong, then the quality of the data he will collect later on will be compromised.

Observation

In this method, the researcher takes a participatory stance, immersing himself in the setting where his respondents are, and generally taking a look at everything, while taking down notes.

Aside from note-taking, other documentation methods may be used, such as video and audio recording, photography, and the use of tangible items such as artifacts, mementoes, and other tools.

  • (+) The participatory nature may lead to the researcher getting more reliable information.
  • (+) Data is more reliable and representative of what is actually happening, since they took place and were observed under normal circumstances.
  • (-) The participation may end up influencing the opinions and attitudes of the researcher, so he will end up having difficulty being objective and impartial as soon as the data he is looking for comes in.
  • (-) Validity may arise due to the risk that the researcher’s participation may have an impact on the naturalness of the setting. The observed may become reactive to the idea of being watched and observed. If he planned to observe recovering alcoholic mothers in their natural environment (e.g. at their homes with their kids), their presence may cause the subjects to react differently, knowing that they are being observed. This may lead to the results becoming impaired.

Longitudinal studies

This is a research or data collection method that is performed repeatedly, on the same data sources, over an extended period of time. It is an observational research method that could even cover a span of years and, in some cases, even decades. The goal is to find correlations through an empirical or observational study of subjects with a common trait or characteristic.

An example of this is the Terman Study of the Gifted conducted by Lewis Terman at Stanford University. The study aimed to gather data on the characteristics of gifted children – and how they grow and develop – over their lifetime. Terman started in 1921, and it extended over the lifespan of the subjects, more than 1,500 boys and girls aged 3 to 19 years old, and with IQs higher than 135. To this day, this study is the world’s “oldest and longest-running” longitudinal study.

  • (+) This is ideal when seeking data meant to establish a variable’s pattern over a period of time, particularly over an extended period of time.
  • (+) As a method to find correlations, it is effective in finding connections and relationships of cause and effect.
  • (-) The long period may become a setback, considering how the probability of the subjects at the beginning of the research will still be complete 10, 20, or 30 years down the road is very low.
  • (-) Over the extended period, attitudes and opinions of the subjects are likely to change, which can lead to the dilution of data, reducing their reliability in the process.

Case Studies

In this qualitative method, data is gathered by taking a close look and an in-depth analysis of a “case study” or “case studies” – the unit or units of research that may be an individual, a group of individuals, or an entire organization. This methodology’s versatility is demonstrated in how it can be used to analyze both simple and complex subjects.

However, the strength of a case study as a data collection method is attributed to how it utilizes other data collection methods, and captures more variables than when a single methodology is used. In analyzing the case study, the researcher may employ other methods such as interviewing, floating questionnaires, or conducting group discussions in order to gather data.

  • (+) It is flexible and versatile, analyzing both simple and complex units and occurrence, even over a long period of time.
  • (+) Case studies provide in-depth and detailed information, thanks to how it captures as many variables as it can.
  • (-) Reliability of the data may be put at risk when the case study or studies chosen are not representative of the sample or population.

II. Quantitative Data Collection Methods

Data can be readily quantified and generated into numerical form, which will then be converted and processed into useful information mathematically. The result is often in the form of statistics that is meaningful and, therefore, useful. Unlike qualitative methods, these quantitative techniques usually make use of larger sample sizes because its measurable nature makes that possible and easier.

Quantitative Surveys

Unlike the open-ended questions asked in qualitative questionnaires, quantitative paper surveys pose closed questions, with the answer options provided. The respondents will only have to choose their answer among the choices provided on the questionnaire.

  • (+) Similarly, these are ideal for use when surveying large numbers of respondents.
  • (+) The standardized nature of questionnaires enable researchers to make generalizations out of the results.
  • (-) This can be very limiting to the respondents, since it is possible that his actual answer to the question may not be in the list of options provided on the questionnaire.
  • (-) While data analysis is still possible, it will be restricted by the lack of details.

Interviews

Personal one-on-one interviews may also be used for gathering quantitative data. In collecting quantitative data, the interview is more structured than when gathering qualitative data, comprised of a prepared set of standard questions.

These interviews can take the following forms:

  • Face-to-face interviews: Much like when conducting interviews to gather qualitative data, this can also yield quantitative data when standard questions are asked.
    • (+) The face-to-face setup allows the researcher to make clarifications on any answer given by the interviewee.
    • (-) This can be quite a challenge when dealing with a large sample size or group of interviewees. If the plan is to interview everyone, it is bound to take a lot of time, not to mention a significant amount of money.
  • Telephone and/or online, web-based interviews. Conducting interviews over the telephone is no longer a new concept. Rapidly rising to take the place of telephone interviews is the video interview via internet connection and web-based applications, such as Skype.
    • (+) The net for data collection may be cast wider, since there is no need to travel through distances to get the data. All it takes is to pick up the phone and dial a number, or connect to the internet and log on to Skype for a video call or video conference.
    • (-) Quality of the data may be questionable, especially in terms of impartiality. The net may be cast wide, but it will only be targeting a specific group of subjects: those with telephones and internet connections and are knowledgeable about using such technologies.
  • Computer-assisted interviews. This is called CAPI, or Computer-Assisted Personal Interviewing where, in a face-to-face interview, the data obtained from the interviewee will be entered directly into a database through the use of a computer.
    • (+) The direct input of data saves a lot of time and other resources in converting them into information later on, because the processing will take place immediately after the data has been obtained from the source and entered into the database.
    • (-) The use of computers, databases and related devices and technologies does not come cheap. It also requires a certain degree of being tech-savvy on the part of the data gatherer.

Quantitative Observation

This is straightforward enough. Data may be collected through systematic observation by, say, counting the number of users present and currently accessing services in a specific area, or the number of services being used within a designated vicinity.

When quantitative data is being sought, the approach is naturalistic observation, which mostly involves using the senses and keen observation skills to get data about the “what”, and not really about the “why” and “how”.

  • (+) It is a quite simple way of collecting data, and not as expensive as the other methods.
  • (-) The problem is that senses are not infallible. Unwittingly, the observer may have an unconscious grasp on his senses, and how they perceive situations and people around. Bias on the part of the observer is very possible.

Experiments

Have you ever wondered where clinical trials fall? They are considered to be a form of experiment, and are quantitative in nature. These methods involve manipulation of an independent variable, while maintaining varying degrees of control over other variables, most likely the dependent ones. Usually, this is employed to obtain data that will be used later on for analysis of relationships and correlations.

Quantitative researches often make use of experiments to gather data, and the types of experiments are:

  • Laboratory experiments. This is your typical scientific experiment setup, taking place within a confined, closed and controlled environment (the laboratory), with the data collector being able to have strict control over all the variables. This level of control also implies that he can fully and deliberately manipulate the independent variable.
  • Field experiments. This takes place in a natural environment, “on field” where, although the data collector may not be in full control of the variables, he is still able to do so up to a certain extent. Manipulation is still possible, although not as deliberate as in a laboratory setting.
  • Natural experiments. This time, the data collector has no control over the independent variable whatsoever, which means it cannot be manipulated. Therefore, what can only be done is to gather data by letting the independent variable occur naturally, and observe its effects.

You can probably name several other data collection methods, but the ones discussed are the most commonly used approaches. At the end of the day, the choice of a collection method is only 50% of the whole process. The correct usage of these methods will also have a bearing on the quality and integrity of the data being sought.

Source: This article was published cleverism.com By Anastasia

Categorized in Research Methods

How does quantitative information differ from qualitative information, and how can you develop the skills to gather, analyze and interpret different types of research and data in today's marketplace? Better yet, how can you use both of these data sets to your advantage in a real job in the real world?

Quantitative information is objective and comprised of numerical, measurable data. Qualitative information is subjective and based on observation and interpretation.

Both of these types of data are vital in today's business decision-making, and the ability to work with them will help you build bridges between what you learn in the classroom and the workplace, putting your career on the fast track. Skills in working with data are essential in nearly every field, and most particularly in careers related to marketing, finance, business and the broad spectrum of jobs in the science, technology, engineering and math (STEM) fields.



When you master the skills to analyze both quantitative and qualitative data, you'll have a powerful arsenal of diverse yet related abilities to help secure advancement in your current job and be more competitive when seeking new opportunities.

How Do You Define Quantitative Skills?

Quantitative skills are objective, numerical and measurable. Quantitative data analytics rely on mathematical and statistical research methods and can be used to solve business problems or to measure long-term trends. With quantitative data analysis skills, you'll be able to understand and interpret data and findings related to budgeting, mathematics, statistical analysis, probability, software applications, operations management and other areas of business strategy and management.

Some common examples of how you might create or gather or create quantitative data include surveys, statistical compilations and accounting records.

Some Examples of Qualitative Research

Qualitative analysis does not focus upon numbers or numerical data, but instead concentrates on in-depth, observational research. These analytic skills are subjective and harder to accurately assess or measure. Qualitative analysis might focus on compiling and interpreting information to draw conclusions, assess critical thinking or design more effective business systems.

Some examples of qualitative research include observation in a clinical laboratory setting or in simulated role-playing situations; focus groups where people discuss an issue or product; structured or unstructured interviews; short questionnaires requiring narrative answers or even multiple choice checkboxes; literature reviews (such as written reports, media coverage, journals); and audio/video taped archives.

Source material and methods used to collect, analyze and interpret raw material may vary widely in a qualitative research study. While a structured data analysis is crucial before arriving at final conclusions and recommendations, a qualitative research study gathers information from observation and open-ended interviewing rather than relying strictly on the by-the-numbers methods commonly used to define a quantitative study.

Combining Quantitative Skills and Qualitative Research on the Job

The ability to analyze both quantitative and qualitative data will give you a competitive edge in a wide variety of careers. When you are able to offer both types of skills to an employer, you'll have an advantage since both skill sets are essential in most data related jobs today.



"Many of our STEM program degrees allow the two skill sets to intersect in a significant way, such as in game development, information technology, math, environmental and geoscience, data analytics, management information systems, cyber security and computer science" said Dr. Gwendolyn Britton, executive director of STEM programs at Southern New Hampshire University (SNHU). "Quantitative and qualitative skills are both important in today's marketplace because so much information is being tossed out at us all the time that it's sometimes hard to make sense of it all. I don't just mean data and numbers - I mean information in the form of opinions, tweets, Facebook posts, images, you name it, information is flowing everywhere all the time. We need to be able to figure out what to do with it all and then make informed decisions or solve problems based on all the information.

The Benefits

If you can measure data and keep within a budget using your scientific and mathematics skills - and you're also able to design or lead strong dynamic teams, you'll have an advantage over other job applicants who are only proficient in one skill set or the other. Or, if you are working in a human services setting, by combining both quantitative and qualitative skill sets, you will bring a range of people skills and data analytic skills from your psychology or sociology coursework background - and be able to balance a multi-million dollar budget or analyze raw data reports too. As a financial analyst, strong skills in problem solving, data analysis, research and math are required, but you also need to be able to work independently and as part of a team.

Collaboration, communications and management skills are essentials as you advance in any career and aspire to higher levels of responsibility, even if you started out thinking you wanted to focus on the numbers alone or that you didn't want to work with numbers at all.

Bottom line? To advance in your career by using a blend of quantitative data and qualitative analysis, you can't just live in spreadsheets. As SNHU Career advisor Cait Glennen observes, "One of my students is using her MS in Data Analytics as a fraud analyst for a major credit card company. Her degree has taught her about how numbers tell a story, and she now uses her grasp of both quantitative data and qualitative analysis to determine if the story has taken a wrong turn into fraudulent and illegal activities."



Glennen adds that many students who pursued a degree in mathematics now use their skills in business to be "amazing problem solvers. Business as a whole is moving towards quantitative data and qualitative analysis and employers are seeking people who have a strong grasp on data and its interpretation. Graduates with these skill sets tend to work in roles where they are interpreting and manipulating existing data in order to provide concrete business insights versus just working with the databases themselves."

Author : Melissa Page

Source : http://www.snhu.edu/about-us/news-and-events/2016/12/data-analysis-skills

Categorized in Online Research

Qualitative plus Quantitative industry search. what method is best for market research? in order to identify distinctions for qualitative plus quantitative search techniques, learn the examples here which I have given by way of tales.

Rapidresponse Benefits Shops

Not so long ago there was a very prosperous shop, named “Rapidresponse shop”. At one time the managing staff started to be concerned that Rapidresponse shop was not becoming favored by as many ladies as males – and that Rapidresponse was shedding an important portion of the market place.

Investigation Goal

An investigation task was created to know exactly how ladies sensed regarding purchasing from Rapidresponse shop and also the reason why. It had been determined that this investigation must be qualitative and the specified strategy would be In-depth-Interviews. The notion was that these ladies may be less likely to want to speak about their feelings regarding Rapidresponse shops in a team, so one-on-one interviews made good sense.

Qualitative Research

More than four dozen present or prospective women clients were paid to come into the main premises to talk about the usage of grocery stores as a whole, and particularly Rapidresponse convenience stores. The outcomes had been very astonishing to the managing staff. The main qualitative results included the following:

Ladies thought of convenience stores to be mainly created for males, without thinking of women,

The restrooms in grocery stores were thought to-be the dirtiest that could be noticed in a town – “gross” was the most typical definition – and that belief penetrated anything that women felt regarding grocery stores as a whole

Rapidresponse ended up being viewed as one of the worst of convenience shops “type of the spot for any male purchase gasoline, have a six pack of affordable alcohol and also tobacco, yet not the type of spot I would like to head”.

Quantitative Research

As soon as the administration staff got an awareness of what problems they encountered with women clients, they felt that they required to know how generally these types of opinions were held. Now they required to obtain a few hard data, which implied that they required to perform a quantitative industry search. The research goals for this stage of research had been:

Learn how women clients of Rapidresponse are different from those that do not frequently go to these shops.Discover whether a repair of Rapidresponse may attract every team to go to the shops more often or perhaps anyway based on if the responder presently avoided Rapidresponce completely.

Concerning the quantitative stage of research they chose to perform 300 phone interviews with a mixture of women participants. The prerequisites to sign up in this stage of research had been: 50 percent of the participants claimed that they had utilized Rapidresponce at least seven times in the past 12 months, and the other 50 percent confessed to deliberately steering clear of Rapidresponse entirely, even though they did utilize other brands of convenience shops. The most important results from the quantitative stage revealed that:

More than 76% of all women Rapidresponse clients were ladies below thirty years old, with no kids, whilst females with kids along with higher earnings were five times not expected to buy at Rapidresponse.

The great report ended up being that of the women who didn’t right now utilize Rapidresponse, sixty four percent mentioned that if these shops were to modify their colors, wash their restrooms boost their health and female products, that they would be prepared to use Rapidresponse once again.

Their two levels of research provided the Rapidresponse administration staff an excellent comprehension of where they presently stood with feminine clients and also the reason why. The quantitative research furthermore revealed that those females who were not presently utilizing their shops might if they transformed their methods.

The choice now was to determine if getting a lot more middle aged ladies as clients was worth the price of upgrading their shops and also investing additional money to ensure that they’re neat and clean.

Source:  https://www.thesequitur.com/which-market-research-method-is-for-you-1286983/

Categorized in Market Research

The majority of links are considered to be commercial in nature, according to new research.Dan Petrovic, aka @DejanSEO, has just published the results of a quantitative study of 2,000 web users in the US and Australia. It was set up to discover perceptions about why web publishers link out.

Accordingly to the research, more than 40% of users think that outbound links from one web page to another are there because they generate revenue for the publisher.

‘Marketing Advertising & Revenue’ was seen to be the number one reason why a link exists, with almost a third of users expecting there to be some kind of commercial arrangement in place.

‘Promotion, Relationship & Sponsorship’ was chosen by a further 9% of respondents. Money, money, money.Meanwhile, just one in five people recognised links as organic citations to help stand up the information on a web page.

outbound links study 2016

All in all, the analysis of the results found that more than half of links exist for commercial reasons, with only 34% seen to be non-commercial.

Classifying the different types of link

I really like Dan’s classification of links, which now straddles 10 distinct areas (though there is a good amount of cross-over). They are as follows:

Attribution

Citation

Definition

Expansion

Identification

Example

Action

Relationship

Proof

Promotion

Further details on each of these link types can be found here. For example, you might file that outbound link under ‘expansion’, because it’s there for further reading and insight into this topic.Dan makes the point that since many of these link types overlap, it can be hard to spot the true intent as to why a link exists.

Some links that look natural – and which are genuinely useful – might actually be there because of some business or personal relationship. That doesn’t automatically make them sketchy. It’s just human nature.

Of course Google doesn’t necessarily see it that way. Many people fear the dreaded manual penalty and go the extra mile to neuter links, even when they have perfectly valid reasons to point visitors to their friends and siblings.

Dan says:

“I see a lot of websites nofollow links to their partner websites, sister companies and various other forms of affiliation because they were told to do so by their SEO or even someone in Google’s webspam team.

“This sort of madness has to stop. If commercially-driven links exist on the web organically then they’re organic in nature and shouldn’t be treated as ‘clean-up material’ nor should those links be penalty-yielding.”

Hear, hear..

I’d love to know the gap between how users perceive links and the actual reasons why the author / publisher put them in place. Presumably it is quite large…

Source:  https://searchenginewatch.com/2016/06/13/web-users-think-most-outbound-links-are-commercial/

Categorized in Online Research

Should you use the Internet for quantitative survey research?

To paraphrase a long-distance carrier’s commercials: if you haven’t done Internet survey research – you will. Because there are some very powerful reasons why you should consider using the Internet for quantitative survey research. 

First, there is the speed with which a questionnaire can be created, distributed to respondents, and the data returned. Since printing, mailing, and data keying delays are eliminated, you can have data in hand within hours of writing a questionnaire. Data are obtained in electronic form, so statistical analysis programs can be programmed to process standard questionnaires and return statistical summaries and charts automatically. 

A second reason to consider Internet surveys is cost. Printing, mailing, keying, and interviewer costs are eliminated, and the incremental costs of each respondent are typically low, so studies with large numbers of respondents can be done at substantial savings compared to mail or telephone surveys. Of course, there are some offsetting costs of preparing and distributing an Internet questionnaire. These costs range widely, according to the type of Internet interviewing used. Figure 1 shows some typical comparative costs of mail, telephone, and Internet (Web) survey research. The cost curves are based on a 5-page questionnaire, with a 35% return rate for mail and a 7-minute duration for telephone interviewing. As the figure shows, the Internet survey is always cheaper by a substantial margin than a telephone survey, is only slightly more expensive than a mail survey for surveys with fewer than about 500 respondents, and becomes increasingly less expensive than mail for more than 500 respondents.


Figure 1


An often overlooked benefit of Internet survey research is the ease with which an Internet survey can be quickly modified. For example, early data returns may suggest additional questions that should be asked. Changing or adding questions on-the-fly would be nearly impossible with a mail questionnaire and difficult with a telephone questionnaire, but can be achieved in a matter of minutes with some Internet survey systems. 

Internet questionnaires delivered with the World Wide Web (WWW) have some unique advantages. They can be made visually pleasing with attractive fonts and graphics. The graphical and hypertext features of the WWW can be used to present products for reaction, or to explain service offerings. For respondents with current versions of Netscape or Internet Explorer, the two most popular web browsers, audio and video can be added to the questionnaire. This multimedia ability of Web-delivered questionnaires is unique. 

Appropriate Populations for Internet Survey Research 

Not all populations are candidates for Internet survey research. The general consumer population is often a poor fit, because fewer than 10% of the U.S. households regularly use Internet services (although more are connected, many are infrequent users). There is also a potential problem in the general population with reluctance to use computers, as well as some fear of the intentions of those who use the Internet to ask questions. This fear has been fanned by sensational media accounts of "cyberstalkers" and con artists who prey on Internet users.

However, there are some exceptions to this broad statement. For example, computer products purchasers and users of Internet services are both ideal populations. Both populations are likely to have very high connectivity (100% in the case of Internet services), and neither are likely to have high levels of cyberphobia. Consumers who have purchased products or services using the Internet are not likely to be fearful of Internet surveys. Web-delivered questionnaires can be made part of the purchase transaction (for customer satisfaction studies, for example), with attendant high levels of motivation and participation from the respondents.

Business and professional users of Internet services are also an excellent population to reach with Internet surveys. Over 80% of businesses are currently estimated to have Internet connections, with the number expected to reach 90% by next year. Business users are likely to have experience with the Internet and to recognize its convenience in replying to questionnaires. In business-to-business research, product and service demonstrations are often crucial. Web-delivered questionnaires, with their ability to weave text and audio-visual demonstrations into the questionnaire, are an excellent way to reach a business population.

Internet questionnaires can frequently be used to supplement traditional methods of collecting questionnaire data. The portion of the target population that uses the Internet can be reached cheaply and quickly with Internet questionnaires, while those not connected can be reached by mail or telephone. Supplementing traditional survey methods provides some immediate cost savings, as well as a migration path toward fuller Internet interviewing in the future as the connectivity of the general population increases. 

Internet Samples

Internet samples fall into three categories: unrestrictedscreened, and recruited.

In an unrestricted sample, anyone on the Internet who desires may complete the questionnaire. These samples may have poor representativeness due to self-selection of the respondents. The rate of participation (completion rate in traditional survey terms) is generally low. Unrestricted samples do have utility in applications like point-of-sale surveys for Web commerce, web site user profiles, ‘bingo card’ -like customer interest surveys, or recruitment of potential focus group members.

Screened samples adjust for the unrepresentativeness of the self-selected respondents by imposing quotas based on some desired sample characteristics. These are often demographic characteristics such as gender, income, and geographic region, or product-related criteria such as past purchase behavior, job responsibilities, or current product use. The applications for screened samples are generally similar to those for unrestricted samples.

Screened sample questionnaires typically use a branching or skip pattern for asking screening questions to determine whether or not the full questionnaire should be presented to a respondent. Some Web survey systems can make immediate market segment calculations that assign a respondent to a particular segment based on screening questions, then select the appropriate questionnaire to match the respondent’s segment.

Alternatively, some Internet research providers maintain a "panel house" that recruits respondents who fill out a preliminary classification questionnaire. This information is used to classify respondents into demographic segments. Clients specify the desired segments, and the respondents who match the desired demographics are permitted to fill out the questionnaires of all clients who specify that segment. This approach is somewhat less flexible than using tailored screening questions that are unique to the survey being conducted, and also raises questions about the representativeness of respondents who are willing to spend the time to fill out many different questionnaires for different clients.

Recruited samples are used for targeted populations in surveys that require more control over the make-up of the sample. Respondents are recruited by telephone, mail, e-mail, or in person. After qualification, they are sent the questionnaire by e-mail, or are directed to a web site that contains a link to the questionnaire. At web sites, passwords are normally used to restrict access to the questionnaire to the recruited sample members. Since the makeup of the sample is known, completions can be monitored, and follow-up messages can be sent to those who do not complete the questionnaire, in order to improve the participation rate.

Recruited samples are ideal in applications that already have a database from which to recruit the sample. For example, a good application would be a survey that used a customer database to recruit respondents for a purchaser satisfaction study. Another application might be the construction of a consumer panel for tracking research. The convenience of filling out a short Internet survey as compared to a paper diary that must be mailed back should increase the participation rate and the accuracy of the answers. 

Different Methods of Conducting Internet Surveys

E-mail Questionnaires. The questionnaire is prepared like a simple e-mail message, and is sent to a list of known e-mail addresses. The respondent fills in the answers, and e-mails the form plus replies back to the research organization. A computer program is typically used to prepare the questionnaire, the e-mail address list, and to extract the data from the replies.

E-mail questionnaires are simple to construct and fast to distribute. By showing up in the respondent’s e-mailbox, they demand immediate attention.

However, they are generally limited to plain text, although graphics can be sent as e-mail attachments that are decoded separately from the questionnaire text. Many standard questionnaire lay-out techniques, such as creating grids of questions and scale responses, cannot be done in a visually attractive way in e-mail. There is no check for validity of data until the whole questionnaire is returned, so there is virtually no opportunity to request that the respondent reenter bad data. The respondent may damage the questionnaire text in the process of responding, making automatic data extraction impossible and requiring hand coding of damaged responses. In addition, all question skips are carried out by the respondent, who is given a set of instructions embedded in the text ("If you replied ‘yes’ to this question, skip to Question 23"). This can result in illegal skip patterns, which may require more hand recoding, or result in missing data or rejected questionnaires.

Converted CATI systems. A software translator program takes questionnaires programmed in the CATI vendor’s questionnaire construction language and translates them for distribution over the Web. The web server may be located in the research supplier’s facility, or time may be rented from a service bureau that has the CATI system installed. The web server is linked to a database that receives the respondents’ replies and stores them.

Converted CATI systems have the good sample and quota management typical of CATI programs. They also inherit the ability to set up complex skip patterns for screening and to adapt to respondents’ replies. They can do data verification at the time of entry, and request reentry of illegal data immediately. Converted CATI systems provide quick migration to Internet interviewing for current users of a particular CATI system and permit reuse of existing programmed questionnaires. In some systems, progress of the Internet survey can be monitored while data is being collected, with some intermediate data extracts available for a fee (daily summaries, for example).

On the negative side, the CATI systems on which these Internet survey products are based were designed for a telephone interviewer working from a computer screen. Respondent screen formatting is somewhat limited as a result. In addition, the CATI languages frequently do not take advantage of the Web’s ability to present graphics and audio-visual material. The researcher is locked into a single CATI system provider’s technology, which is only a small disadvantage if the researcher is already using that CATI system, but a larger one if the researcher is not. Finally, the converted CATI systems are expensive to purchase and use.

Converted Disk-By-Mail Systems. These are similar to converted CATI systems. Disk-by-mail systems provide a questionnaire construction tool that creates a program file on a floppy disk that the respondent subsequently runs on a personal computer. The program presents the questions on the computer screen and records the answers on the program floppy disk, which is then mailed back to the research organization. The converted disk-by-mail system adapts the questionnaire for presentation via the Web, and provides a data management program to record the answers provided by the respondents.

Converted disk-by-mail systems have the same skip pattern management and data verification advantages of converted CATI systems, with the addition of more flexible questionnaire construction tools that include graphical and audio/visual material. However, they inherit the limitations on quota management of the disk-by-mail approach, which is designed to present a single questionnaire to a single respondent. They typically require that the user manage his/her own web site and install and maintain the software on that site.

Web CGI programs. In this approach to Internet survey research, each questionnaire is programmed directly in HTML (the presentation language used by the WWW) using a computer script language such as PERL or a programming language such as Visual Basic. The programmed questionnaire is placed on a Web server at the client’s location or on a server located in a service bureau. The program uses the Common Gateway Interface (CGI) of the WWW to place respondents’ replies into a data base. Data base queries can be programmed to give periodic reports of the data to-date, including statistical analyses.

The CGI programming approach is the most flexible of all. Complex question skips and data verification and reentry can be achieved, and programming languages can use the full capability of Web. Since all questionnaires are custom programmed, Web CGI programs are not tied to a proprietary CATI language, or a single technology vendor. Database operations and queries can be programmed to adapt to virtually any special reporting need of the researcher.

This flexibility comes with a cost, however. Since questionnaires and data base operations are essentially custom computer programs that must be created and debugged by highly-trained programmers, they are expensive. The computer languages contain no special tools for tasks like screening, quota management and question skip pattern management, so programming these features in each questionnaire further increases the cost.

The CGI program must be placed on a web server system to distribute the questionnaires and collect the data. This can be the research client’s web server, or a server provided by the research supplier. If the survey is placed on the client’s web server, time for programming and debugging can be difficult to schedule. Large corporate sites often require several administrative approvals before any modifications of the site can be made, and technical staff are frequently leery of allowing an outside programmer to place a program on their site.

Web Survey Systems. These are software systems specifically designed for Web questionnaire construction and delivery. In essence, they combine the survey administration tools of a CATI system with the flexibility of CGI programming. They consist of an integrated questionnaire designer, web server, data base, and data delivery program, designed for use by non-programmers.

In a typical use, the questionnaire is constructed with an easy-to-use questionnaire editor using a visual interface, then automatically transmitted to the Web server system. The Web server distributes questionnaire and files responses in a database. The user can query the server at any time via the Web for completion statistics, descriptive statistics on responses, and graphical displays of data. Data can be downloaded from the server at any time for analysis at the researcher’s location. The questionnaire construction and data display programs reside on the user’s computer system, while the Web server is located in a survey technology provider’s office.

Web survey systems include tools that allow non-programmers to create complex questionnaires that are visually appealing. The complexity of skip patterns and data verification that can be achieved approaches that of the CGI programming approach. Users do not have to maintain a Web site or data base, so there is less disruption of clients’ web sites and computing facilities. Sample quota control is as good as that provided by converted CATI systems. In addition, tools to personalize questionnaires with data base information (like inserting the respondent’s name in a questionnaire delivered to a restricted sample respondent) and to add graphics and sound without programming are often included.

Web survey systems typically have a lower cost per completed interview than converted CATI, converted disk-by-mail, or CGI programs, although they are more expensive than e-mail surveys for small surveys (under 500 respondents). The lower cost results from the efficiencies of using software tools designed specifically for Web use, and from the cost-sharing of Internet access costs and hardware costs that a central server system provides.

Like converted CATI, converted disk-by-mail, and CGI programming, Web survey systems use the more passive Web retrieval for questionnaires. E-mail, although it has many limitations, is more immediately attention-demanding. Also, for current users of CATI systems, migration of existing questionnaires to Web survey systems is more difficult than migration to a converted CATI system. Questionnaires must be manually cut and pasted into the Web survey questionnaire constructor. 

Conclusions

Internet survey research is not appropriate for all populations and all projects, but for many applications it provides definite advantages. For populations already using the Internet, or for "early adopter" populations, quantitative survey research on the Internet can give faster results at a lower cost than traditional methods. Internet questionnaires can be used to supplement traditional quantitative data collection methods as a way of reducing the overall cost of a project or as the beginning of a migration to all-Internet surveys in the future.

The kind of Internet survey technology to use for a project depends on the circumstances of survey. The following grid (Table 1) summarizes the strengths and weaknesses of each.

  E-Mail Converted
CATI
Converted 
Disk-By-Mail
Web CGI Programs Web Survey Systems
Ease of creation / modification Excellent Fair Good Poor Excellent
Ease of Access to Preliminary Data Poor Fair Good Excellent Excellent
Sample Quota Control Poor Excellent Fair Excellent Excellent
Data Validity Checks Poor Good Good Excellent Excellent
Demand of Respondent’s Attention Excellent Good Good Good Good
Personalization of Questionnaires Fair Fair Poor Excellent Excellent
Conversion of Existing Questionnaires Fair Excellent Good Good Good
Expertise Required by Questionnaire Creator Low High Moderate Very High Moderate
Cost per completion Inexpensive Expensive Expensive Very Expensive Moderate to Inexpensive

 Source:

http://www.unt.edu/rss/class/survey/watt.htm

Categorized in Market Research

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