Research methods is a term used to refer to the ways that information is collected, analyzed, and interpreted.
The purpose of research methods is to obtain data about what people think, feel, or do in order to learn more about a specific topic.
A variety of research academic methods are available for use in different fields such as psychology, sociology, public health, business administration, etc.
Research can be conducted through qualitative, quantitative, or mixed-methods approaches.
Qualitative research utilizes in-depth interviews and participant observation which offers insights into subjective perceptions of the world.
Quantitative studies are based on numeric measurements to examine attitudes, opinions, and behaviors by collecting statistical data from large groups of respondents.
There are also mixed methods that combine both qualitative and quantitative approaches providing an opportunity to draw conclusions from both perspectives while also including other methodologies such as document analysis.
Data collected using various research methods can then be summarized and organized into various forms of presentations (such as graphs) to communicate results.
Types of research methods in academic research
Research methods can be grouped into three categories depending on their purpose:
- Descriptive research uses data to describe phenomena and seeks to identify patterns;
- Predictive research is used to predict future events;
- Explanatory research seeks to understand past events through empirical evidence.
Let’s take a closer look at each category:
Descriptive research methods
This type of research examines current situations by observing people’s thoughts, feelings, attitudes, and behaviors.
It provides facts without attempting to make predictions or draw conclusions about cause-and-effect relationships.
Common tools of descriptive research include surveys, interviews, participant observation, and case studies.
To analyze this data we would use descriptive statistics (measures of central tendency) such as mean, median, mode, and standard deviation or inferential statistics (predicting or generalizing about populations) such as correlation analysis and multivariate analysis to investigate the relationships between variables.
Descriptive qualitative research includes field notes, observations, and interviews.
Quantitative research includes measures of central tendency such as mean, median, mode, and standard deviation or inferential statistics like correlation analysis and multivariate analysis to investigate the relationships between variables.
Predictive research methods
Predictive research attempts to predict what is likely to happen in the future by measuring relevant variables.
The main purpose of predictive research is not only to describe and explain past or present events, but also to estimate, forecast, or extrapolate future behavior.
Predictive research focuses on causal relationships between independent variables (the causes) and dependent variables (the effects). It can be broken down into three subcategories experimental, correlational, and observational.
This type of predictive research involves conducting controlled experiments to test hypotheses.
One might create an experiment to test the hypothesis that watching violent media makes people more aggressive.
Subjects are randomly assigned to watch either violent or non-violent media and then have their levels of aggression tested before and after watching the media.
Subjects who watch violent media should have higher levels of aggression than those who watched non-violent media.
This predictive research technique evaluates the strength of association (correlation) between two or more factors.
For example, there has been a lot of research done on the relationship between time spent watching TV and obesity.
Correlational research tries to establish a link between different variables. It does not necessarily try to establish causation, and often predicts the existence of hidden variables.
The gold standard of research is the double-blind, randomized, placebo-controlled trial.
This type of study requires participants to be divided into two groups.
Neither group knows which drug they are taking (placebo or real) and neither researcher knows which group is receiving the placebo or real drug. The researcher will administer a series of tests to the participants in both groups and compare the data.
If there is a significant difference between the groups, they assume that whatever they administered caused the difference in the outcome.
Observational predictive research
This predictive research method is used to study patterns in groups of people.
Although it can be useful, it is more susceptible to bias.
For example, you might observe that obese people tend to get heart attacks, so you might come up with a hypothesis that obesity leads to heart attacks. You could then do a study to see if the same thing happens to your population.
However, observational research cannot establish causality, because other variables may be involved.
These other variables may be known (e.g., people who smoke a lot may also drink a lot) or unknown (e.g., some people may be genetically predisposed to certain diseases).
Explanatory research methods
This type of research method is aimed at finding out the reasons behind a particular phenomenon. In order to explore potential explanations, researchers generally employ one of the following types of research:
- Exploratory research
- Comparative research
- Causal research
This type of research attempts to identify and measure the direct effect of an intervention.
A good example would be testing whether eating cereal for breakfast as opposed to skipping breakfast increases children’s cognitive performance during math tests.
Researchers would split children into two groups, giving half cereal for breakfast and half nothing for breakfast.
They would then administer a math test before the school day starts and another right when school ends (without telling them when they had eaten cereal), comparing how well each group did in each case.
If the group that ate cereal did better, they would conclude that eating cereal for breakfast improves children’s cognitive performance.
The downside to this type of research is that the cause may be correlated, rather than caused.
Children who eat cereal for breakfast may also have a habit of reading or doing homework while they eat.
Therefore, these habits (reading and doing homework) would be the actual cause of increased performance in the group that eats cereal for breakfast.
Additionally, it is possible that the cereal itself may not be the cause. Perhaps a healthy breakfast (cereal plus fruit) causes children to perform better, and the cereal just happened to be what they chose to eat.
This type of research is exploratory. It is usually conducted by asking a number of questions to investigate a topic, rather than looking for any specific result. This type of research is particularly useful for getting a sense of the scope and complexity of a problem.
Exploratory research is typically done by asking a number of questions to investigate a topic, rather than looking for any specific result.
This type of research is particularly useful for getting a sense of the scope and complexity of a problem.
It can provide insights into the difficulties in solving a problem, which are important considerations for policymakers and funders.
Sometimes this type of research does produce findings that will lead to causal research on the same topic later on.
An example of this type of research is focus groups, where participants are asked to discuss their opinions on a topic.
Another example would be systematic review and meta-analysis, where multiple studies are analyzed together.
Mixed methods research
This type of research includes quantitative and qualitative research.
Quantitative data is collected using surveys, questionnaires, interviews, online communities, experiments, and observations.
Qualitative data includes unstructured or semi-structured interviews, observation without recording equipment, focus groups, and ethnographic fieldwork.
This type of research is useful for an in-depth understanding of a topic, and to find out the thoughts and feelings of those being studied.
Mixed methods research is useful for an in-depth understanding of a topic, and to find out the thoughts and feelings of those being studied.
It has the benefit of incorporating both objective facts as well as subjective opinions, enabling deeper insight into topics like addiction and depression.
However, it takes more time and effort because there are two sets of data gathering that need to happen concurrently.
For example, an interviewer needs to conduct interviews but should also take notes about observable behavior and physical cues.
Data collection methods in research
Collecting data for research purposes is a key step in the research process.
All of the different ways to collect data depending on the type of research, and what exactly is being researched.
Qualitative and quantitative data collection methods are the two main types of data acquisition methods.
Depending on the research, one or the other method is used.
For example, if the researcher is studying statistics, the research is quantitative and so a survey might be used. If the researcher is studying art history, the research is qualitative and so a questionnaire might be used.
There are three basic categories of data: primary, secondary, and tertiary
This is information obtained through original research such as surveys, laboratory testing, interviewing subjects, and participant observation.
Primary data is obtained through original research such as surveys, laboratory testing, interviewing subjects, and participant observation.
This is acquired from someone else’s research, such as published articles and books.
Secondary data is useful when conducting reviews of existing literature. It can also help researchers decide whether or not they want to do more research on a certain topic, based on the quality of prior work.
This is data that is found in public databases and archives, often created for other purposes.
As these sources have already been organized and documented, they can be useful sources of information even though they were not obtained through original research. They also contain large amounts of data and cost less to obtain.
Examples include the Bureau of Labor Statistics, The World Bank, and the US Census Bureau.
Data collection methods for quantitative research
Quantitative research data collection methods include surveys, questionnaires, interviews, online communities, experiments, and observations.
These methods gather numerical data which is then analyzed statistically.
Surveys are most commonly used to gather quantitative data because they require a minimal investment of resources while still providing detailed information.
Online communities can provide valuable feedback on a variety of topics which helps businesses and students understand their target audience better.
Surveys can be done quickly and easily with relatively little input from the respondent.
Respondents answer questions verbally or with clicks on a website interface; this gives researchers easy access to data.
Participants may voluntarily fill out surveys themselves, making them easy to distribute.
On the downside, respondents may not answer truthfully due to social desirability bias–the tendency for people to respond in ways that make them look good rather than revealing honest answers about themselves–or simply due to a lack of understanding about what constitutes truthful responses.
Interviews are also a popular choice for quantitative research as they allow for more in-depth responses and give the opportunity to ask follow-up questions.
Unlike surveys, interviews can only be conducted face-to-face, and require more effort on the part of the researcher.
Data collection methods for qualitative research
The primary data collection methods for qualitative research include surveys, questionnaires, interviews, online communities, and participant observation.
These methods all involve gathering subjective data which must be interpreted by the researcher instead of being statistically analyzed.
Qualitative research is ideal for investigating how and why things happen.
Experiments, where the researcher manipulates an independent variable, are less common in qualitative research because it’s difficult to isolate and manipulate variables without altering other aspects of the system.
However, some researchers will conduct an experiment in order to compare attitudes and behaviors between groups.
Qualitative data collection methods, like surveys and interviews, are less likely to lead to false data or wasted time.
That is because they don’t rely on memory and human honesty as much as quantitative methods. Furthermore, surveys and interviews are very quick to set up and distribute.
However, they may take longer to complete depending on the subject of the study. This can be a problem for studies with tight deadlines.
Data analysis methods in research
Data Analysis is typically broken down into three types: Descriptive, Inferential, and Predictive.
Basically, descriptive data analysis looks at the data and counts the frequencies of data in a group, displays data as a bar graph, or provides a pictograph to illustrate qualitative data.
Descriptive Data Analysis can include:
- Frequency distribution which analyzes how data is grouped
- Bar graphs that display data as bar charts
- Pictographs which provide a visual representation of qualitative data
- Line graphs to visualize trends in quantitative variables over time
- Tables to summarize data.
The inferential type of analysis is more complicated and looks at the relationships between different groups of data.
This could be accomplished with a line graph that would plot the relationship over time for quantitative variables.
Other examples of inferential data analysis include:
- Linear regression
- Multiple regression
- Chi-square tests.
Linear regression is a statistical technique for determining the relationship between two variables. This method can be applied to qualitative or quantitative data.
Multiple regression is also a statistical technique that uses linear regression but has more than one dependent variable.
Chi-square tests are the most commonly used statistic in the social sciences. This test is based on the chi-square distribution and can be used to examine differences in proportions, variances, and means.
Finally, predictive data analysis can be thought of as forecasting.
The idea behind inferential data analysis is that you can infer something from examining what it does.
For example, if you want to know if your business idea is working, you might use a survey asking people about their satisfaction levels with your product/service.
Examples of predictive data analysis include:
- Marketing planning
- Demographic analysis
- Market basket analysis.
Marketing Planning helps marketers decide what products to carry and when to carry them.
A Forecast predicts the future needs of consumers while a Demographic Analysis shows market segments such as age ranges or income brackets.
Market Basket Analysis examines what items are purchased together at various locations throughout the year.