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: Second, decide how you will analyze the data. Data is the information that you collect for the purposes of answering your research question. The type of data you need depends on the aims of your research. 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.
You can also take a mixed methods approach, where you use both qualitative and quantitative research methods. Primary vs. secondary dataPrimary 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.
Descriptive vs. experimental dataIn 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.
Examples of data collection methodsResearch methods for collecting data
Methods for analyzing dataYour 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. Qualitative analysis methodsQualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:
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. Quantitative analysis methodsQuantitative 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:
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. Examples of data analysis methodsResearch methods for analyzing data
Frequently asked questions about research methodsWhat is sampling? A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students. In statistics, sampling allows you to test a hypothesis about the characteristics of a population. Is this article helpful?You have already voted. Thanks :-) Your vote is saved :-) Processing your vote... Which research strategy is not concerned with examining relationships between variables?The descriptive research strategy is the only strategy that is not concerned with relationships between variables. may be specific to the experimenter who has the expectations. Experiments allow researchers to Question options: answer cause-and-effect questions about the relationship between two variables.
Which research strategy is concerned with examining relationships between variables?Correlational research involves measuring two variables and assessing the relationship between them, with no manipulation of an independent variable.
Which type of research examines the relationship between two variables?A correlational study is a type of research design that looks at the relationships between two or more variables. Correlational studies are non-experimental, which means that the experimenter does not manipulate or control any of the variables. A correlation refers to a relationship between two variables.
Which research method is most likely to identify a causal relationship between variables?The use of a controlled study is the most effective way of establishing causality between variables. In a controlled study, the sample or population is split in two, with both groups being comparable in almost every way. The two groups then receive different treatments, and the outcomes of each group are assessed.
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