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. Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions. Action research links theory and practice in several cycles to drive innovative changes.
How do you analyze qualitative data? There are various approaches to qualitative data analysis , but they all share five steps in common: Prepare and organize your data. Review and explore your data. Develop a data coding system. Assign codes to the data. Identify recurring themes. What is a Likert scale? Are Likert scales ordinal or interval scales? What is the difference between a control group and an experimental group? What is blinding? What is the difference between single-blind, double-blind and triple-blind studies?
In a single-blind study , only the participants are blinded. In a double-blind study , both participants and experimenters are blinded. In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.
Why is blinding important? What is a quasi-experiment? When should I use a quasi-experimental design? What is simple random sampling? What is an example of simple random sampling? When should I use simple random sampling?
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. What is cluster sampling? The clusters should ideally each be mini-representations of the population as a whole.
What are the types of cluster sampling? In single-stage sampling , you collect data from every unit within the selected clusters. In double-stage sampling , you select a random sample of units from within the clusters. In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample. What are some advantages and disadvantages of cluster sampling? What is stratified sampling? When should I use stratified sampling?
Can I stratify by multiple characteristics at once? What is systematic sampling? How do I perform systematic sampling? There are three key steps in systematic sampling : Define and list your population , ensuring that it is not ordered in a cyclical or periodic order. Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
Choose every k th member of the population as your sample. How can you tell if something is a mediator? Why should you include mediators and moderators in a study? What is a control variable? Why are control variables important? What is random assignment? How do you randomly assign participants to groups? When do you use random assignment?
Can you use a between- and within-subjects design in the same study? What are the pros and cons of a between-subjects design?
Advantages: Prevents carryover effects of learning and fatigue. Shorter study duration. Disadvantages: Needs larger samples for high power.
Uses more resources to recruit participants, administer sessions, cover costs, etc. Individual differences may be an alternative explanation for results. What are the pros and cons of a within-subjects design?
Advantages: Only requires small samples, Statistically powerful, Removes the effects of individual differences on the outcomes. Disadvantages: Internal validity threats reduce the likelihood of establishing a direct relationship between variables, Time-related effects, such as growth, can influence the outcomes, Carryover effects mean that the specific order of different treatments affect the outcomes. What is a factorial design?
What are the types of extraneous variables? Experimenter effects : unintentional actions by researchers that influence study outcomes. What are the requirements for a controlled experiment? Controlled experiments require: A control group that receives a standard treatment, a fake treatment, or no treatment. Random assignment of participants to ensure the groups are equivalent. What are explanatory and response variables?
The difference between explanatory and response variables is simple: An explanatory variable is the expected cause, and it explains the results. A response variable is the expected effect, and it responds to other variables. How do explanatory variables differ from independent variables? How do you plot explanatory and response variables on a graph? If you have quantitative variables , use a scatterplot or a line graph.
If your response variable is categorical, use a scatterplot or a line graph. If your explanatory variable is categorical, use a bar graph. Is random error or systematic error worse? How do you avoid measurement errors? What is a correlation? A positive correlation means that both variables change in the same direction. A negative correlation means that the variables change in opposite directions. What is correlational research? What is a correlation coefficient?
How many variables are in a correlation? In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. In a correlational design , you measure variables without manipulating any of them. How do you order a questionnaire? If testing the effect of sunlight on the growth of a flower, the control group of flowers might be grown inside and away from the sun.
Here are the steps to take when performing an experiment with a control group:. Your experiment should begin with a question that needs an answer. Perhaps you've noticed an effect and are curious about its cause. This is your hypothesis, the integral starting point for figuring out what your control is going to be. Related: Hypothesis: Definition and Examples. Once you've settled on the question you hope to answer, begin making observations on the topic you hope to study.
If you're a medical professional trying to determine what effects a particular exercise regimen has on arthritic patients, note any patients doing similar exercises. Record any observations you make about their type of arthritis, what their regimen is and what effects it seems to have. This helps you decide which independent variables you wish to test and which groups are most likely to display the effects these variables may have. With a question that needs answering and some observation-based data, choose a more specific hypothesis.
Doing so will help you figure out the exact independent variable to use during your study. For example, if a psychologist observed that their patients benefit from spending time outside their house, the specific hypothesis becomes that periodically enjoying time away from the home has a positive effect on their health and recovery.
For example, there may be several exercise regimens that aid arthritis patients' mobility. However, since the scientific method only works by testing one variable at a time, you must only select one. This way, you can trace all data gathered back to one specific cause. Consider picking one exercise for all patients. Make sure they perform the same actions in the same way for the same amount of time. This eliminates the possibility of other variables affecting the outcome of your data.
Assign this variable to an experimental group of patients. Choose patients with the same condition as your experimental group but who either receive no treatment or the usual treatment for their condition. This is your baseline and is one of the most important aspects of your experiment.
Record the effects your control group exhibits and compare it to your experimental group. Since they have not experienced the variable you are testing, any effect observed in both groups cannot be attributed to your independent variable.
For example, if both groups have improved mobility, it is not due to the tested exercise regime. When selecting the control group, make sure they are as similar as possible to your experimental group.
Whether they are patients, plants or any other subject you wish to study, selecting those similar to your test group ensures that other variables have no or little effect on your experiment. After selecting your experimental and control groups, you can begin testing your experiment. 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.
In effect, each individual is their own control, with a before and after measurement. The experimenter is looking at the change in response of the individual rather than the average effect of the group.
It is a much more sensitive way to structure and analyze experiments like this. Agreed, these videos only skim the surface his book goes into much greater detail about a much wider range of controls. By controlling for a potentially large source of variability—the individual participant—statistical tests become much more sensitive to changes than averaging all of that variability by group in a simple post-test design.
Second, it is a check to see whether the randomization of participants into groups was successful. In many RTCs in the clinical sciences, there is recruitment bias, allowing for the sicker patients to be placed in the treatment group, for example.
No mention of Institutional Review Board?! The IRB will raise Dr. My own blood pressure readings change markedly in the course of a visit to the doctor. Late to the debate, but I think those are wonderful. Maybe next Control Kitty will ask just how he assembled all those volunteers for his test to be representative and blinding to minimize bias.
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