Research methods are procedures for gathering and analyzing information. The development of your research methods is an important aspect of your research plan. There are two major decisions to be made when planning your methods.

Decide how you’ll collect data first. The type of data you need to answer your research question will determine your methods:

• Quantitative vs. qualitative data: Will your data be expressed in words or numbers?

• Primary vs. secondary data: Will you collect your own data or will you rely on data that has already been collected?

• Descriptive vs. experimental: Will you measure something exactly as it is or will you conduct an experiment?

Decide how you’ll analyze the data next.

• You can use statistical analysis methods to test relationships between variables with quantitative data.

• To interpret trends in qualitative data, you can utilize approaches such as thematic analysis and data collection methods.

The information you collect to answer your research topic is referred to as data. The sort of data you require is determined by the objectives of your study.

Quantitative vs. qualitative information

The type of information you wish to develop will determine whether you collect qualitative or quantitative data.

Collect qualitative data to answer questions about ideas, experiences, and meanings, or to examine anything that can’t be stated statistically.

Collect quantitative data if you wish to build a better mechanistic grasp of a topic or if your research involves hypothesis testing.

• Pros • Cons

Qualitative • Adaptable – you can change your methods as you gain new insights.

• Small sample sizes are possible.

• Cannot be statistically examined or applied to larger populations.

• Research is difficult to standardize.

Quantitative • Can be used to describe big groups of objects in a methodical way.

• Produces repeatable information.

• Data analysis necessitates statistical training.

• Larger samples are required.

You can also use both qualitative and quantitative research methods in a mixed methods approach.

Secondary vs. primary data

Any original information that you collect for the purpose of answering your research question is referred to as primary data (e.g. through surveys, observations and experiments). Secondary data is information gathered previously by other researchers (e.g. in a government census or previous scientific studies).

You’ll almost certainly need to acquire primary data if you’re investigating a fresh research subject. Secondary data, on the other hand, may be a better alternative if you want to combine existing knowledge, evaluate historical trends, or uncover patterns on a wide scale. • Pros • Cons

• Primary data can be gathered to answer a specific study topic.

• The sampling and measuring techniques are under your control.

• Collecting is more expensive and time-consuming.

• Data collection procedures must be learned.

Secondary • Access is easier and faster.

• You can collect data over longer durations and from a wider geographical area; however, you have no control over how the data was generated.

• Requires additional processing to ensure that it is suitable for your analysis.

Experimental vs. descriptive data

You acquire data about your study subject without intervening in descriptive research. Your sampling method will determine the validity of your study.

You methodically intervene in a process and measure the outcome in experimental research. Your experimental design will determine the validity of your findings.

You must be able to change your independent variable, properly measure your dependent variable, and adjust for confounding variables in order to conduct an experiment. This method is the greatest choice for answering issues regarding causation and effect if it is both realistic and ethically possible. • Pros • Cons

• You can use descriptive language to describe your study topic without influencing it.

• You can collect more data on a greater scale; • You have no control over confounding variables.

• Inability to determine cause and effect linkages.

• More control over confounding variables in experiments.

• You might have an unanticipated impact on your research subject if you can identify cause and effect linkages.

• Data collection usually necessitates additional knowledge and resources.

Methods of data acquisition examples

Data collection strategies for research

mode of investigation

Primary or secondary education?

Is it better to use qualitative or quantitative data?

When should you use

Experiment

Primary Quantitative

To see if there are any cause-and-effect correlations.

Survey

Primary Quantitative

To comprehend a population’s general characteristics.

Focus group/interview

Primary Qualitative

To develop a deeper understanding of a subject.

Observation Primary Either

To comprehend how something happens in its natural environment.

Examining the literature

Secondary

Either to place your findings in the context of previous work or to assess trends within a research area.

An example

Either to obtain a thorough insight of a specific population or situation, or when you lack the means to do a broad study.

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Data analysis techniques

The sort of data you collect and how you prepare it for analysis will determine your data analysis strategies.

Data can frequently be examined numerically as well as qualitatively. Survey responses, for example, could be evaluated qualitatively by looking at the meanings of responses or statistically by looking at response rates.

Methods of qualitative research

To comprehend words, thoughts, and experiences, qualitative analysis is applied. It can be used to interpret data acquired in the following ways:

• From open-ended survey and interview questions, literature reviews, case studies, and other non-numerical sources.

• Non-probability sampling techniques

Because qualitative analysis is flexible and relies on the researcher’s judgment, you must carefully consider your choices and assumptions.

Methods of quantitative analysis

Quantitative analysis employs numbers and statistics to comprehend frequency, averages, and correlations (in descriptive research), as well as cause-and-effect relationships (in experiments).

You can use quantitative analysis to analyze data acquired in one of two ways:

• During a research project.

• Probability sampling techniques

The results of quantitative analysis may be easily standardized and disseminated across academics because the data is collected and evaluated in a statistically accurate manner.

Methods of data analysis examples

Data analysis research methodologies

mode of investigation

Is it better to use qualitative or quantitative data?

When should you use

Analytical statistics

Quantitative

To conduct a statistically valid analysis of data collected (e.g. from experiments, surveys, and observations).

Meta-analysis

Quantitative

Analyze the outcomes of a large number of studies statistically.

Can only be used in research when the data was acquired in a statistically sound manner.

Thematic investigation

Qualitative

Data from interviews, focus groups, or textual sources will be analyzed.

To comprehend the data’s overall themes and how they are communicated.

Analyze the content

To examine enormous amounts of textual or visual data gathered from surveys, literature reviews, and other sources.

Can be quantitative (i.e. word frequencies) or qualitative (i.e. (i.e. meanings of words).