What is the difference between comparative and controlled experiments

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. Home Frequently asked questions What is the difference between a control group and an experimental group? What is the difference between a control group and an experimental group? 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? What is internal validity? What are threats to internal validity? What is the difference between a longitudinal study and a cross-sectional study? What are the pros and cons of a longitudinal study? What is an example of a longitudinal study? How long is a longitudinal study? Why do a cross-sectional study? What are the disadvantages of a cross-sectional study?

What is external validity? What are the two types of external validity? What are threats to external validity?

Why are samples used in research? When are populations used in research? What is sampling error? What is sampling bias? Why is sampling bias important? What are some types of sampling bias? How do you avoid sampling bias? What is probability sampling? What is non-probability sampling? Why are independent and dependent variables important? What is an example of an independent and a dependent variable?

The type of soda — diet or regular — is the independent variable. The level of blood sugar that you measure is the dependent variable — it changes depending on the type of soda.

Can a variable be both independent and dependent? Can I include more than one independent or dependent variable in a study? Why do confounding variables matter for my research? What is the difference between confounding variables, independent variables and dependent variables? How do I prevent confounding variables from interfering with my research? What is data collection? What are the benefits of collecting data? When conducting research, collecting original data has significant advantages: You can tailor data collection to your specific research aims e.

What is operationalization? What is hypothesis testing? What are the main qualitative research approaches? There are five common approaches to qualitative research : Grounded theory involves collecting data in order to develop new theories.

Ethnography involves immersing yourself in a group or organization to understand its culture. This is when a hypothesis is scientifically tested. In a controlled experiment, an independent variable the cause is systematically manipulated and the dependent variable the effect is measured; any extraneous variables are controlled.

The researcher can operationalize i. The quantitative data can be analysed to see if there is a difference between the experimental group and control group. In experiments scientists compare a control group and an experimental group that are identical in all respects, except for one difference - experimental manipulation.

Unlike the experimental group, the control group is not exposed to the independent variable under investigation and so provides a base line against which any changes in the experimental group can be compared.

Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance. Randomly allocating participants to independent variable groups means that all participants should have an equal chance of taking part in each condition. The principle of random allocation is to avoid bias in the way the experiment is carried out and to limit the effects of participant variables.

The researcher wants to make sure that it is the manipulation of the independent variable that has changed the changes in the dependent variable. Hence, all the other variables that could affect the dependent variable to change must be controlled. These other variables are called extraneous or confounding variables. Extraneous variables should be controlled were possible, as they might be important enough to provide alternative explanations for the effects.

For example, a scientist may wonder whether or not a species of bacteria needs oxygen in order to live. To test this, cultures of bacteria may be left in the air, while other cultures are placed in a sealed container of nitrogen the most common component of air or deoxygenated air which likely contained extra carbon dioxide. Which container is the control? Which is the experimental group? The most common type of control group is one held at ordinary conditions so it doesn't experience a changing variable.

For example, If you want to explore the effect of salt on plant growth, the control group would be a set of plants not exposed to salt, while the experimental group would receive the salt treatment. If you want to test whether the duration of light exposure affects fish reproduction, the control group would be exposed to a "normal" number of hours of light, while the duration would change for the experimental group. Experiments involving human subjects can be much more complex.

If you're testing whether a drug is effective or not, for example, members of a control group may expect they will not be unaffected. To prevent skewing the results, a placebo may be used. A placebo is a substance that doesn't contain an active therapeutic agent. If a control group takes a placebo, participants don't know whether they are being treated or not, so they have the same expectations as members of the experimental group. However, there is also the placebo effect to consider.

Here, the recipient of the placebo experiences an effect or improvement because she believes there should be an effect. Another concern with a placebo is that it's not always easy to formulate one that truly free of active ingredients. For example, if a sugar pill is given as a placebo, there's a chance the sugar will affect the outcome of the experiment.

Positive and negative controls are two other types of control groups:. Actively scan device characteristics for identification. Arbitrarily small effects can be detected with large enough sample size, but this makes for a very expensive experiment. We will need to balance our decision based on what we consider to be a biologically meaningful response and the resources at our disposal.

Whenever we perform more than one test we should adjust the P values 2. Although commonly used, the format used in Figure 1b is inappropriate for reporting our results: sample means, their uncertainty and P values alone do not present the full picture. A more complete presentation of the results Fig. The effect size, d , defined as the difference in means in units of pooled standard deviation, expresses this combination of measurement and precision in a single value.

Thus, neither significance itself nor differences in significance status should ever be used to conclude anything about the magnitude of the underlying differences, which may be very small and not biologically relevant. CIs explicitly show how close we are to making a positive inference and help assess the benefit of collecting more data. More information about our ability to detect an effect can be obtained from a post hoc power analysis, which assumes that the observed effect is the same as the real effect normally unknown , and uses the observed difference in means and pooled variance.

Other than increasing sample size, how could we improve our chances of detecting the effect of A? Our ability to detect the effect of A is limited by variability in the difference between A and C, which has two random components.

If we measure the same aliquot twice, we expect variability owing to technical variation inherent in our laboratory equipment and variability of the sample over time Fig. This variability is assumed to be the same in the untreated and treated condition, with effect d on aliquot x and y. A comparison of the mean weight after a month is confounded by the initial weights of the subjects in each group. If instead we focus on the change in weight, we remove much of the subject variability owing to the initial weight.

Now, the difference measurement derived from the same aliquot removes all the noise; in fact, a single pair of aliquots suffices for an exact inference. We can see the improved sensitivity of the paired design Fig. When reporting paired-test results, sample means Fig. As before, P values should be adjusted with multiple-test correction. The paired design is a more efficient experiment.