They are sometimes recorded as numbers, but the numbers represent categories rather than actual amounts of things. There are three types of categorical variables: binary, nominal, and ordinal variables. Binary vs nominal vs ordinal variables. Type of variable. A variable is defined as anything that has a quantity or quality that varies.

The dependent variable is the variable a researcher is interested in. An independent variable is a variable believed to affect the dependent variable. Confounding variables are defined as interference caused by another variable. In our medical example, age is an example of a quantitative variable because it can take on multiple numerical values.

It also makes sense to think about it in numerical form; that is, a person can be 18 years old or 80 years old. Weight and height are also examples of quantitative variables. Such variables in statistics are broadly divided into four categories such as independent variables, dependent variables, categorical and continuous variables. Apart from these, quantitative and qualitative variables hold data as nominal, ordinal, interval and ratio.

A nominal variable is a type of variable that is used to name, label or categorize particular attributes that are being measured. It takes qualitative values representing different categories, and there is no intrinsic ordering of these categories.

Some examples of nominal variables include gender, Name, phone, etc. A nominal variable has no intrinsic ordering to its categories. For example, gender is a categorical variable having two categories male and female with no intrinsic ordering to the categories. An ordinal variable has a clear ordering. Dependent and independent variables are important because they drive the research process.

Concepts like justice, ideology, southernness , ethnic antipathy, and political interest are relatively more abstract. You can imagine seeing examples of behavior that may capture something about these concepts like a person speaking with a southern accent, or someone asking a friend about a political issue , but you know that the concept includes far more than just that little bit of behavior.

In some concepts, like ideology, there may be no behavior at all to observe. It may just be values that exist in people's minds electro-chemical arrangements? The point is that concepts range from abstact to concrete. The more abstract, the less it is directly observable and the more it needs careful definition so that we know and others to whom we are talking know what is included in the mental box.

The more concrete it is, the easier it is to communicate what it means and what is included and what should be observed in doing research by simply saying the concept. Defining Concepts in Research. This is important in research--and in life for that matter. In using words, we cannot communicate unless we are using the same words for the same things. Certainly researchers cannot increase the body of knowledge about any topic if they attach different meanings to concepts that they are studying.

All they are doing is increasing confusion. One may find that alienation is decreased by additional years of formal education and another may find that alienation is increased by additional years of formal education. They could both be right if they are defining the concepts of alienation differently. Some types of alienation may be increased by education disdain for popular culture while other forms of alienation a sense of separation from government might be decreased by more years of formal education.

Generally speaking, you should think about four things when dealing with definitions and concepts. Keep the definitions clear. This is not easy because we use words to define other words.

Usually definitions try to use more simple words to define more complex words. This is a useful way to approach definitions of abstract concepts. There are a variety of political dictionaries that can be useful e. Jack Plano et al. Make the definition appropriate. By appropriate, I mean use a definition that is consistent with the way the concept is used in the literature.

What too many researchers do is stipulate a new definition for a term that others have used. So we end up with 84 studies on alienation, which use 27 different definitions for the concept. It is not surprising that so many of the results do not seem consistent when they are really studying different phenomenon.

What you should do is find out how others use the term that is part of your literature review and be consistent with what they do. If they are not consistent, then you should note their inconsistency in your literature review. I did a lot of this in my master's thesis, which was on political alienation. If you feel you must use a new definition, you probably should use a new term, or at least a modified term so as not to add to the confusion. So you may have to write about "popular cultural alienation" rather than just alienation.

Or you may have to create a totally new word, like " popculination. The danger here is that you add to the jargon and those outside the discipline do not know what we are talking about. But maybe that is good--it allows us to be more mysterious and look more scientific and command more money to explain what we are talking about this is said with tongue in cheek.

A relatively new term that is really in vogue is the concept of "social capital," developed by Robert Putnam. It refers to the social interconnectedness that citizens have in the web of groups and organizations to which they belong that's the definition. Avoid defining concepts with related concepts. You should create definitions with simpler words, as I mentioned above. So if you are talking about ideology, you should say something like attitudes about the degree to which government should be involved in regulating economic matters and regulating personal behavior.

You should NOT say that ideology is partisan identification. Partisan identification is an abstract term and it is a separate, although related, concept. Democrats do tend to be more liberal and Republicans more conservative, but it is far from a one-to-one relationship. Group membership and interests, family and regional loyalties, and heroes are also involved in partisanship.

Avoid circular definitions. Political alienation may be the degree to which one is alienated to politics, but that is not a good definition. In choosing naming conventions, take into account the similarity or dissimilarity of existing standards and usage. Use titles from existing standards only for what is defined in the standards. Use standard definitions to make it possible to compare data collected from different sources and to integrate data across sources Statistics Canada, In addition to Statistics Canada's standard classifications, there are international standard classifications produced by the United Nations Statistical Office, the International Labour Office, Eurostat, and other international and regional agencies.

The Standards Division has produced official concordances to a number of international standard classifications. When there is a requirement to provide data to international agencies, use official concordances when they are available. Use standard units of observation to facilitate the comparison of data. Classifications are usually designed with particular units of observation in mind.

Be aware of derived statistical activities or statistical frameworks e. Sometimes, there is more than one way to measure a concept. The variables and classifications chosen to measure a concept will also need to take into account factors such as the ease of obtaining the information required, the respondent burden imposed, the collection method, the context in which the question s must be asked, the processing of the data especially editing, imputation and weighting techniques , whether the information can be obtained from administrative records, and the costs associated with collection and processing.

Thus, the measurement approach adopted may be more or less successful in providing the desired interpretation of the concept. A variable chosen at one point in time may become obsolete later if new factors come into play and may therefore need to be modified or changed.

Therefore, it is important to ensure that the latest approved version of the variable is used. Updated standards are made available on the Statistics Canada website. In the absence of an official standard, examine the concepts, variables and classifications being used by related statistical programs and consult with the Standards Division when necessary.

Describe key statistical concepts, including the statistical measure, the population, variables, units, domains and time reference. In a factorial design, multiple independent variables are tested. If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable. There are 4 main types of extraneous variables :. Controlled experiments require:. Depending on your study topic, there are various other methods of controlling variables.

The difference between explanatory and response variables is simple:. On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis. Random and systematic error are two types of measurement error. Random error is a chance difference between the observed and true values of something e.

Systematic error is a consistent or proportional difference between the observed and true values of something e. Systematic error is generally a bigger problem in research. With random error, multiple measurements will tend to cluster around the true value.

Systematic errors are much more problematic because they can skew your data away from the true value. Random error is almost always present in scientific studies, even in highly controlled settings. You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking blinding where possible.

A correlational research design investigates relationships between two variables or more without the researcher controlling or manipulating any of them. A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables. Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions. A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

Controlled experiments establish causality, whereas correlational studies only show associations between variables. In general, correlational research is high in external validity while experimental research is high in internal validity. Correlation describes an association between variables: when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables. Causation means that changes in one variable brings about changes in the other; there is a cause-and-effect relationship between variables.

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not. A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires. Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from.

These questions are easier to answer quickly. Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered. You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents.

A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects. Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording. Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions. A research design is a strategy for answering your research question.

It defines your overall approach and determines how you will collect and analyze data. The priorities of a research design can vary depending on the field, but you usually have to specify:.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources.

This allows you to draw valid , trustworthy conclusions. Quantitative research designs can be divided into two main categories:. Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs. Correlation coefficients always range between -1 and 1. The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage. This method is often used to collect data from a large, geographically spread group of people in national surveys, for example.

You take advantage of hierarchical groupings e. Triangulation means using multiple methods to collect and analyze data on the same subject. By combining different types or sources of data, you can strengthen the validity of your findings.

These are four of the most common mixed methods designs :. Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples.

In multistage sampling , you can use probability or non-probability sampling methods. For a probability sample, you have to probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication. Scientists and researchers must always adhere to a certain code of conduct when collecting data from others. These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe. Both are important ethical considerations. You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals. Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure. Want to contact us directly?

No problem. We are always here for you. Scribbr specializes in editing study-related documents. We proofread:. You can find all the citation styles and locales used in the Scribbr Citation Generator in our publicly accessible repository on Github.

Frequently asked questions See all. What is sampling? Reliability and validity are both about how well a method measures something: Reliability refers to the consistency of a measure whether the results can be reproduced under the same conditions. Validity refers to the accuracy of a measure whether the results really do represent what they are supposed to measure.

What is the difference between internal and external validity? What is experimental design? To design a controlled experiment, you need: A testable hypothesis At least one independent variable that can be precisely manipulated At least one dependent variable that can be precisely measured When designing the experiment, you decide: How you will manipulate the variable s How you will control for any potential confounding variables How many subjects or samples will be included in the study How subjects will be assigned to treatment levels Experimental design is essential to the internal and external validity of your experiment.

What are independent and dependent variables? For example, in an experiment about the effect of nutrients on crop growth: The independent variable is the amount of nutrients added to the crop field.

The dependent variable is the biomass of the crops at harvest time. What is the difference between quantitative and categorical variables? What is the difference between discrete and continuous variables? Discrete and continuous variables are two types of quantitative variables : Discrete variables represent counts e. Continuous variables represent measurable amounts e. What is a confounding variable? How do I decide which research methods to use?

If you want to measure something or test a hypothesis , use quantitative methods. If you want to explore ideas, thoughts and meanings, use qualitative methods. If you want to analyze a large amount of readily-available data, use secondary data.

If you want data specific to your purposes with control over how it is generated, collect primary data. If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods. What is mixed methods research?