Determining who will receive your survey is a component of which of the following research stages?

Study design depends greatly on the nature of the research question. In other words, knowing what kind of information the study should collect is a first step in determining how the study will be carried out (also known as the methodology).

Let’s say we want to investigate the relationship between daily walking and cholesterol levels in the body. One of the first things we’d have to determine is the type of study that will tell us the most about that relationship. Do we want to compare cholesterol levels among different populations of walkers and non-walkers at the same point in time? Or, do we want to measure cholesterol levels in a single population of daily walkers over an extended period of time?

The first approach is typical of a cross-sectional study. The second requires a longitudinal study. To make our choice, we need to know more about the benefits and purpose of each study type.

Cross-sectional study

Both the cross-sectional and the longitudinal studies are observational studies. This means that researchers record information about their subjects without manipulating the study environment. In our study, we would simply measure the cholesterol levels of daily walkers and non-walkers along with any other characteristics that might be of interest to us. We would not influence non-walkers to take up that activity, or advise daily walkers to modify their behaviour. In short, we’d try not to interfere.

The defining feature of a cross-sectional study is that it can compare different population groups at a single point in time. Think of it in terms of taking a snapshot. Findings are drawn from whatever fits into the frame.

To return to our example, we might choose to measure cholesterol levels in daily walkers across two age groups, over 40 and under 40, and compare these to cholesterol levels among non-walkers in the same age groups. We might even create subgroups for gender. However, we would not consider past or future cholesterol levels, for these would fall outside the frame. We would look only at cholesterol levels at one point in time.

The benefit of a cross-sectional study design is that it allows researchers to compare many different variables at the same time. We could, for example, look at age, gender, income and educational level in relation to walking and cholesterol levels, with little or no additional cost.

However, cross-sectional studies may not provide definite information about cause-and-effect relationships. This is because such studies offer a snapshot of a single moment in time; they do not consider what happens before or after the snapshot is taken. Therefore, we can’t know for sure if our daily walkers had low cholesterol levels before taking up their exercise regimes, or if the behaviour of daily walking helped to reduce cholesterol levels that previously were high.

Longitudinal study

A longitudinal study, like a cross-sectional one, is observational. So, once again, researchers do not interfere with their subjects. However, in a longitudinal study, researchers conduct several observations of the same subjects over a period of time, sometimes lasting many years.

The benefit of a longitudinal study is that researchers are able to detect developments or changes in the characteristics of the target population at both the group and the individual level. The key here is that longitudinal studies extend beyond a single moment in time. As a result, they can establish sequences of events.

To return to our example, we might choose to look at the change in cholesterol levels among women over 40 who walk daily for a period of 20 years. The longitudinal study design would account for cholesterol levels at the onset of a walking regime and as the walking behaviour continued over time. Therefore, a longitudinal study is more likely to suggest cause-and-effect relationships than a cross-sectional study by virtue of its scope.

In general, the research should drive the design. But sometimes, the progression of the research helps determine which design is most appropriate. Cross-sectional studies can be done more quickly than longitudinal studies. That’s why researchers might start with a cross-sectional study to first establish whether there are links or associations between certain variables. Then they would set up a longitudinal study to study cause and effect.

Source: At Work, Issue 81, Summer 2015: Institute for Work & Health, Toronto

This column updates a previous column describing the same term, originally published in 2009.

You can conduct your own marketing research. Follow these steps, add your own flair, knowledge and creativity, and you’ll have bespoke research to be proud of.

Marketing research is the term used to cover the concept, development, placement and evolution of your product or service, its growing customer base and its branding – starting with brand awareness, and progressing to (everyone hopes) brand equity. Like any research, it needs a robust process to be credible and useful.

Marketing research uses four essential key factors known as the ‘marketing mix’, or the Four Ps of Marketing:

• Product (goods or service)
• Price (how much the customer pays)
• Place (where the product is marketed)
• Promotion (such as advertising and PR)

These four factors need to work in harmony for a product or service to be successful in its marketplace.

The marketing research process – an overview

A typical marketing research process is as follows:

• Identify an issue, discuss alternatives and set out research objectives
• Develop a research program
• Choose a sample
• Gather information
• Gather data
• Organize and analyze information and data
• Present findings
• Make research-based decisions
• Take action based on insights

Step 1: Defining the marketing research problem

Defining a problem is the first step in the research process. In many ways, research starts with a problem facing management. This problem needs to be understood, the cause diagnosed, and solutions developed.

However, most management problems are not always easy to research, so they must first be translated into research problems. Once you approach the problem from a research angle, you can find a solution. For example, “sales are not growing” is a management problem, but translated into a research problem, it becomes “why are sales not growing?” We can look at the expectations and experiences of several groups: potential customers, first-time buyers, and repeat purchasers. We can question whether the lack of sales is due to:

• Poor expectations that lead to a general lack of desire to buy, or
• Poor performance experience and a lack of desire to repurchase.

This, then, is the difference between a management problem and a research problem. Solving management problems focuses on actions: Do we advertise more? Do we change our advertising message? Do we change an under-performing product configuration? And if so, how?

Defining research problems, on the other hand, focus on the whys and hows, providing the insights you need to solve your management problem.

Step 2: Developing a research program: method of inquiry

The scientific method is the standard for investigation. It provides an opportunity for you to use existing knowledge as a starting point, and proceed impartially.

The scientific method includes the following steps:

• Define a problem
• Develop a hypothesis
• Make predictions based on the hypothesis
• Devise a test of the hypothesis
• Conduct the test
• Analyze the results

This terminology is similar to the stages in the research process. However, there are subtle differences in the way the steps are performed:

• the scientific research method is objective and fact-based, using quantitative research and impartial analysis
• the marketing research process can be subjective, using opinion and qualitative research, as well as personal judgment as you collect and analyze data

Step 3: Developing a research program: research method

As well as selecting a method of inquiry (objective or subjective), you must select a research method. There are two primary methodologies that can be used to answer any research question:

• Experimental research: gives you the advantage of controlling extraneous variables and manipulating one or more variables that influence the process being implemented.
• Non-experimental research: allows observation but not intervention – all you do is observe and report on your findings.

Step 4: Developing a research program: research design

Research design is a plan or framework for conducting marketing research and collecting data. It is defined as the specific methods and procedures you use to get the information you need.

There are three core types of marketing research designs: exploratory, descriptive, and causal. A thorough marketing research process incorporates elements of all of them.

Exploratory marketing research

This is a starting point for research. It’s used to reveal facts and opinions about a particular topic, and gain insight into the main points of an issue. Exploratory research is too much of a blunt instrument to base conclusive business decisions on, but it gives the foundation for more targeted study. You can use secondary research materials such as trade publications, books, journals and magazines and primary research using qualitative metrics, that can include open text surveys, interviews and focus groups.

Descriptive marketing research

This helps define the business problem or issue so that companies can make decisions, take action and monitor progress. Descriptive research is naturally quantitative – it needs to be measured and analyzed statistically, using more targeted surveys and questionnaires. You can use it to capture demographic information, evaluate a product or service for market, and monitor a target audience’s opinion and behaviors. Insights from descriptive research can inform conclusions about the market landscape and the product’s place in it.

Causal marketing research

This is useful to explore the cause and effect relationship between two or more variables. Like descriptive research, it uses quantitative methods, but it doesn’t merely report findings; it uses experiments to predict and test theories about a product or market. For example, researchers may change product packaging design or material, and measure what happens to sales as a result.

Step 5: Choose your sample

Your marketing research project will rarely examine an entire population. It’s more practical to use a sample - a smaller but accurate representation of the greater population. To design your sample, you’ll need to answer these questions:

• Which base population is the sample to be selected from? Once you’ve established who your relevant population is (your research design process will have revealed this), you have a base for your sample. This will allow you to make inferences about a larger population.
• What is the method (process) for sample selection? There are two methods of selecting a sample from a population:

1. Probability sampling: This relies on a random sampling of everyone within the larger population.

2. Non-probability sampling: This is based in part on the investigator’s judgment, and often uses convenience samples, or by other sampling methods that do not rely on probability.

• What is your sample size? This important step involves cost and accuracy decisions. Larger samples generally reduce sampling error and increase accuracy, but also increase costs. Find out your perfect sample size with our calculator.

Step 6: Gather data

Your research design will develop as you select techniques to use. There are many channels for collecting data, and it’s helpful to differentiate it into O-data (Operational) and X-data (Experience):

• O-data is your business’s hard numbers like costs, accounting, and sales. It tells you what has happened, but not why.
• X-data gives you insights into the thoughts and emotions of the people involved: employees, customers, brand advocates.

When you combine O-data with X-data, you’ll be able to build a more complete picture about success and failure - you’ll know why. Maybe you’ve seen a drop in sales (O-data) for a particular product. Maybe customer service was lacking, the product was out of stock, or advertisements weren’t impactful or different enough: X-data will reveal the reason why those sales dropped. So, while differentiating these two data sets is important, when they are combined, and work with each other, the insights become powerful.

With mobile technology, it has become easier than ever to collect data. Survey research has come a long way since market researchers conducted face-to-face, postal, or telephone surveys. You can run research through:

• Email
• SMS
• Slack
• WhatsApp
• Social media (polls and listening)

Another way to collect data is by observation. Observing a customer’s or company’s past or present behavior can predict future purchasing decisions. Data collection techniques for predicting past behavior can include market segmentation, customer journey mapping and brand tracking.

Regardless of how you collect data, the process introduces another essential element to your research project: the importance of clear and constant communication.

And of course, to analyze information from survey or observation techniques, you must record your results. Gone are the days of spreadsheets. Feedback from surveys and listening channels can automatically feed into AI-powered analytics engines and produce results, in real-time, on dashboards.

Step 7: Analysis and interpretation

The words ‘statistical analysis methods’ aren’t usually guaranteed to set a room alight with excitement, but when you understand what they can do, the problems they can solve and the insights they can uncover, they seem a whole lot more compelling.

Statistical tests and data processing tools can reveal:

• Whether data trends you see are meaningful or are just chance results
• Your results in the context of other information you have
• Whether one thing affecting your business is more significant than others
• What your next research area should be
• Insights that lead to meaningful changes

There are several types of statistical analysis tools used for surveys. You should make sure that the ones you choose:

• Work on any platform - mobile, desktop, tablet etc.
• Integrate with your existing systems
• Are easy to use with user-friendly interfaces, straightforward menus, and automated data analysis
• Incorporate statistical analysis so you don’t just
  process and present your data, but refine it, and generate insights and predictions.

Here are some of the most common tools:

• Benchmarking: a way of taking outside factors into account so that you can adjust the parameters of your research. It ‘levels the playing field’ – so that your data and results are more meaningful in context. And gives you a more precise understanding of what’s happening.
• Regression analysis: this is used for working out the relationship between two (or more) variables. It is useful for identifying the precise impact of a change in an independent variable.
• T-test is used for comparing two data groups which have different mean values. For example, do women and men have different mean heights?
• Analysis of variance (ANOVA) Similar to the T-test, ANOVA is a way of testing the differences between three or more independent groups to see if they’re statistically significant.
• Cluster analysis: This organizes items into groups, or clusters, based on how closely associated they are.
• Factor analysis: This is a way of condensing many variables into just a few, so that your research data is less unwieldy to work with.
• Conjoint analysis: this will help you understand and predict why people make the choices they do. It asks people to make trade-offs when making decisions, just as they do in the real world, then analyzes the results to give the most popular outcome.
• Crosstab analysis: this is a quantitative market research tool used to analyze ‘categorical data’ - variables that are different and mutually exclusive, such as: ‘men’ and ‘women’, or ‘under 30’ and ‘over 30’.
• Text analysis and sentiment analysis: Analyzing human language and emotions is a rapidly-developing form of data processing, assigning positive, negative or neutral sentiment to customer messages and feedback.

Stats IQ can perform the most complicated statistical tests at the touch of a button using our online survey software, or data from other sources. Learn more about Stats iQ now.

Step 8: The marketing research results

Your marketing research process culminates in the research results. These should provide all the information the stakeholders and decision-makers need to understand the project.

The results will include:

• all your information
• a description of your research process
• the results
• conclusions
• recommended courses of action

They should also be presented in a form, language and graphics that are easy to understand, with a balance between completeness and conciseness, neither leaving important information out or allowing it to get so technical that it overwhelms the readers.

Traditionally, you would prepare two written reports:

• a technical report, discussing the methods, underlying assumptions and the detailed findings of the research project
• a summary report, that summarizes the research process and presents the findings and conclusions simply.

There are now more engaging ways to present your findings than the traditional PowerPoint presentations, graphs, and face-to-face reports:

• Live, interactive dashboards for sharing the most important information, as well as tracking a project in real time.
• Results-reports visualizations – tables or graphs with data visuals on a shareable slide deck
• Online presentation technology, such as Prezi
• Visual storytelling with infographics
• A single-page executive summary with key insights
• A single-page stat sheet with the top-line stats

You can also make these results shareable so that decision-makers have all the information at their fingertips.

Step 9 Turn your insights into action

Insights are one thing, but they’re worth very little unless they inform immediate, positive action. Here are a few examples of how you can do this:

• Stop customers leaving – negative sentiment among VIP customers gets picked up; the customer service team contacts the customers, resolves their issues, and avoids churn.
• Act on important employee concerns – you can set certain topics, such as safety, or diversity and inclusion to trigger an automated notification or Slack message to HR. They can rapidly act to rectify the issue.
• Address product issues – maybe deliveries are late, maybe too many products are faulty. When product feedback gets picked up through Smart Conversations, messages can be triggered to the delivery or product teams to jump on the problems immediately.
• Improve your marketing effectiveness - Understand how your marketing is being received by potential customers, so you can find ways to better meet their needs
• Grow your brand - Understand exactly what consumers are looking for, so you can make sure that you’re meeting their expectations

What is the first step a researcher should take when designing a questionnaire?

What type of research focuses on how individuals create meaning in their everyday lives?

Which of the following are types of descriptive research?

Which of the following is are true about survey research?