This is usually only feasible when the population is small and easily accessible. A statistic refers to measures about the sample , while a parameter refers to measures about the population. A sampling error is the difference between a population parameter and a sample statistic. Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.
Sampling bias is a threat to external validity — it limits the generalizability of your findings to a broader group of people. Some common types of sampling bias include self-selection, non-response, undercoverage, survivorship, pre-screening or advertising, and healthy user bias. Using careful research design and sampling procedures can help you avoid sampling bias.
Oversampling can be used to correct undercoverage bias. Probability sampling means that every member of the target population has a known chance of being included in the sample.
Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling. In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.
Common non-probability sampling methods include convenience sampling, voluntary response sampling, purposive sampling, snowball sampling, and quota sampling. Determining cause and effect is one of the most important parts of scientific research. You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time.
It must be either the cause or the effect, not both! Yes, but including more than one of either type requires multiple research questions. For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more.
Each of these is its own dependent variable with its own research question. You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable. To ensure the internal validity of an experiment , you should only change one independent variable at a time.
To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists. A confounding variable is closely related to both the independent and dependent variables in a study.
An independent variable represents the supposed cause , while the dependent variable is the supposed effect. A confounding variable is a third variable that influences both the independent and dependent variables. Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.
There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization. In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables. In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group.
The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable. In statistical control , you include potential confounders as variables in your regression.
In randomization , you randomly assign the treatment or independent variable in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.
Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations. However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. Operationalization means turning abstract conceptual ideas into measurable observations.
Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.
There are five common approaches to qualitative research :. There are various approaches to qualitative data analysis , but they all share five steps in common:.
The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis. In scientific research, concepts are the abstract ideas or phenomena that are being studied e. Variables are properties or characteristics of the concept e. The process of turning abstract concepts into measurable variables and indicators is called operationalization.
A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined. To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement. Overall Likert scale scores are sometimes treated as interval data.
These scores are considered to have directionality and even spacing between them. The type of data determines what statistical tests you should use to analyze your data. An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways. Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment.
If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results. A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship.
The main difference with a true experiment is that the groups are not randomly assigned. Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment. Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity as they can use real-world interventions instead of artificial laboratory settings. Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population.
Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset. The American Community Survey is an example of simple random sampling. In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.
If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity. However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,. If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.
Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.
There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample. Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area. However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.
In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share e. Once divided, each subgroup is randomly sampled using another probability sampling method. Using stratified sampling will allow you to obtain more precise with lower variance statistical estimates of whatever you are trying to measure. For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race.
Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions. Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup.
In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups. Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval — for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling. There are three key steps in systematic sampling :.
A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship. A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related. Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world.
They are important to consider when studying complex correlational or causal relationships. Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.
Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity. Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs. In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization.
With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group. In contrast, random assignment is a way of sorting the sample into control and experimental groups. Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.
Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups. Random assignment is used in experiments with a between-groups or independent measures design.
Random assignment helps ensure that the groups are comparable. In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic. In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.
In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables a factorial design. In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.
While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design. Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful. 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.
A typical use of a control group is in an experiment in which the effect of a treatment is unknown and comparisons between the control group and the experimental group are used to measure the effect of the treatment. Why is it important that an experiment include a control group? Without a control group, there is no basis for knowing if a particular result is due to the variable being tested or to some other factor. If it is unknown which group subjects are in, it is less likely that results can be tampered with.
In the Proteus Syndrome experiment what was the control group? Which of the following best describes a control group in an experiment? In a controlled experiment, what is manipulated? Controlled variables are quantities that a scientist wants to remain constant.
Most experiments have more than one controlled variable. For example, if you are testing a new cold medicine, the controlled variable might be that the patient has a cold and a fever. A simple example of a control group can be seen in an experiment in which the researcher tests whether or not a new fertilizer has an effect on plant growth.
The negative control group would be the set of plants grown without the fertilizer, but under the exact same conditions as the experimental group. Is a control group necessary?
Although a two-group design is the simplest and probably most common experimental format, your research questions might suggest a different approach. Such an approach, which uses two experimental groups and no control group is indeed possible, and sometimes appropriate. Control Groups and Experimental Groups Without an experimental control group, it is difficult to determine the effects of the independent variable on the dependent variable in an experiment.
This is because there can always be outside factors that are influencing the behavior of the experimental group. In scientific experiments, a scientific control is one in which the subject or a group would not be tested for the dependent variable s. A study with control s is designed to ensure that the effects are due to the independent variables in the experiment. An experiment without the proper controls is meaningless.
Controls allow the experimenter to minimize the effects of factors other than the one being tested. This goes beyond science — controls are necessary for any sort of experimental testing, no matter the subject area. This is often why so many bibliometric studies of the research literature are so problematic. Inadequate controls are often performed which fail to eliminate the effects of confounding factors, leaving the causality of any effect seen to be undetermined.
These short videos offer quick lessons in positive and negative controls, as well as how to validate your experimental system. David acquired and managed a suite of research society-owned journals with OUP, and before that was the Executive Editor for Cold Spring Harbor Laboratory Press, where he created and edited new science books and journals, along with serving as a journal Editor-in-Chief.
David received his PhD in Genetics from Columbia University and did developmental neuroscience research at Caltech before moving from the bench to publishing. We could add one more necessary control in this experiment—controlling for variability in individual response. In the three videos, the experimenter may only detect differences between groups or average differences. He is unable to detect changes in individuals. Some participants may be more sensitive to caffeine than others, some may show negative changes, and some may show no changes at all.
If we take the blood pressure of participants before they drink coffee, we have a baseline measurement for all individuals. We also have a check on whether the experimenter was able to randomly assign participants to each treatment group.
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