- 1.An introduction to research methods
- 2.Methods for collecting data
- 3.Examples of data collection methods
- 4.Methods for analyzing data
- 5.Examples of data analysis methods
1.An introduction to research methods
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make.
First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:
- - Qualitative vs. quantitative: Will your data take the form of words or numbers?
- - Primary vs. secondary: Will you collect original data yourself, or will you use data that has already been collected by someone else?
- - Descriptive vs. experimental: Will you take measurements of something as it is, or will you perform an experiment?
Second, decide how you will analyze the data.
- - For quantitative data, you can use statistical analysis methods to test relationships between variables.
- - For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.
2.Methods for collecting data
Data is the information that you collect for the purposes of answering your research question. The data collection methods you use depend on the type of data you need.
2.1.Qualitative vs. quantitative data
Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.
For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data.
If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing, collect quantitative data.
- Flexible – you can often adjust your methods as you go to develop new knowledge.
- Can be conducted with small samples.
- Can’t be analyzed statistically or generalized to broader populations.
- Difficult to standardize research.
- Can be used to systematically describe large collections of things.
- Generates reproducible knowledge.
- Requires statistical training to analyze data.
- Requires larger samples.
You can also take a mixed methods approach, where you use both qualitative and quantitative research methods.
2.2.Primary vs. secondary data
Primary data is any original information that you collect for the purposes of answering your research question (e.g. through surveys, observations and experiments). Secondary data is information that has already been collected by other researchers (e.g. in a government census or previous scientific studies).
If you are exploring a novel research question, you’ll probably need to collect primary data. But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.
-Can be collected to answer your specific research question.
-You have control over the sampling and measurement methods.
-More expensive and time-consuming to collect.
-Requires training in data collection methods.
-Easier and faster to access.
-You can collect data that spans longer timescales and broader geographical locations.
-No control over how data was generated.
-Requires extra processing to make sure it works for your analysis.
2.3.Descriptive vs. experimental data
In descriptive research, you collect data about your study subject without intervening. The validity of your research will depend on your sampling method.
In experimental research, you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design.
To conduct an experiment, you need to be able to vary your independent variable, precisely measure your dependent variable, and control for confounding variables. If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.
- Allows you to describe your research subject without influencing it.
- Accessible – you can gather more data on a larger scale.
- No control over confounding variables.
- Can’t establish cause and effect relationships.
- More control over confounding variables.
- Can establish cause and effect relationships.
- You might influence your research subject in unexpected ways.
- Usually requires more expertise and resources to collect data.
3.Examples of data collection methodsResearch methods for collecting data
|Research method||Primary or secondary?||Qualitative or quantitative?||When to use|
|Experiment||Primary||Quantitative||To test cause-and-effect relationships.|
|Survey||Primary||Quantitative||To understand general characteristics of a population.|
|Interview/focus group||Primary||Qualitative||To gain more in-depth understanding of a topic.|
|Observation||Primary||Either||To understand how something occurs in its natural setting.|
|Literature review||Secondary||Either||To situate your research in an existing body of work, or to evaluate trends within a research topic.|
|Case study||Either||Either||To gain an in-depth understanding of a specific group or context, or when you don’t have the resources for a large study.|
4.Methods for analyzing data
Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.
Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.
4.1.Qualitative analysis methods
Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:
- - From open-ended survey and interview questions, literature reviews, case studies, and other sources that use text rather than numbers.
- - Using non-probability sampling methods.
Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions.
4.2.Quantitative analysis methods
Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).
You can use quantitative analysis to interpret data that was collected either:
- - During an experiment.
- - Using probability sampling methods.
Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.
5.Examples of data analysis methods
|Research method||Qualitative or quantitative?||When to use|
|Statistical analysis||Quantitative||To analyze data collected in a statistically valid manner (e.g. from experiments, surveys, and observations).|
|Meta-analysis||Quantitative||To statistically analyze the results of a large collection of studies.
Can only be applied to studies that collected data in a statistically valid manner.
|Thematic analysis||Qualitative||To analyze data collected from interviews, focus groups or textual sources.
To understand general themes in the data and how they are communicated.
|Content analysis||Either||To analyze large volumes of textual or visual data collected from surveys, literature reviews, or other sources.
Can be quantitative (i.e. frequencies of words) or qualitative (i.e. meanings of words).