Welcome to the PICOT Research Question Generator - Full Version! You will be prompted below to choose one of four potential objectives for your PICOT research question. Then, you will enter terms related to your PICOT research question into text boxes that describe each of the five PICOT components. You will then be able to generate your PICOT research question by clicking on a button.
Based on the choice of objective for your PICOT research question and the terms you entered for the five PICOT components, the rest of the web form will be able to generate your 1) null and alternate hypotheses, 2) your primary independent and dependent variables, 3) a description of your statistical power, and 4) the correct statistical test to answer your PICOT research question. You will also have the option to enter information and make choices regarding your population inclusion and exclusion criteria, sampling method, research design, demographic/clinical/confounding/control variables, and database structure. Finally, you will be able to receive a free download of all the information created by working through the form. It should take you between 15-20 minutes to complete the web form.
Thank you for using the PICOT Research Question Generator!
What is the statistical objective of your PICOT research question? Choose an option below and then select the answers that best fit your statistical objective. *required
The research question is the foundation of everything in applied research and statistics. The research question directly influences the following:
Below, you will work through the five parts of the PICOT framework. The text information you enter into the text boxes related to the five PICOT components will be used for the rest of the web form.
The first component of the PICOT framework is the Population. The population associated with a research question must be described in order to make proper inferences and generalizations based on research findings. When reading the empirical literature, the population often has its own section in manuscripts and the characteristics of the sample taken from the population are presented in Table 1.
In the text box below, please enter text describing the population of interest for your study in general terms and click on the Submit button.
Next, you need to define the population of interest in regards to inclusion and exclusion criteria. Inclusion criteria are characteristics that potential participants must possess to be included in the study. When defining inclusion criteria, they should be defined across four (4) general areas:
Define your Population in regards to inclusion criteria in the text box below and click on the Submit button.
Next, you need to define the exclusion criteria associated with the population of interest. Exclusion criteria are characteristics that participants should not possess. Exclusion criteria for a population should include:
Define your Population in regards to exclusion criteria in the text box below and click on the Submit button.
Hypothesis-driven studies focus on the effects of an intervention or independent variable on an outcome. The Intervention (I) in the PICOT framework is what researchers are manipulating in order to test the association or effect with the outcome. It could be an active intervention being administered to a population of interest, it could be a given characteristic possessed by a person, or it could be a group of participants that share a characteristic or experience. Essentially, the Intervention (I) is what the researchers are interested in studying because they believe it will cause some sort of effect on the Outcome (O).
In the text box below, please define the Intervention that is being administered and click on the Submit button.
In hypothesis-driven studies, there is a fundamental and requisite need for some sort of comparison/control/Comparator (C) in order to establish the effects of the Intervention (I). The Comparator (C) in the PICOT framework is compared to the Intervention (I) to see if the intervention has an effect on the outcome above and beyond the comparator.
In the text box below, please define the Comparator that is being administered and click on the Submit button.
The Outcome in the PICOT research question is what researchers measure for or observe in a population. It is also known as the dependent variable. Researchers measure for an outcome because they have hypothesized a relationship between the independent variable and the dependent/outcome variable. If at all possible, researchers should seek to measure for their outcomes at the current "gold standard" level because doing so increases the internal and external validity of the research study. The measurement of outcomes has a pervasive influence on the following aspects of any research study:
In the text box below, please define the primary Outcome that will be measured for in your study and click on the Submit button.
In the text box below, please define the Time frame for when you will observe the outcomes in your study and click on the Submit button.
After defining and operationalizing the important components of the research question using PICOT, it is useful to see how the PICOT components are stated in the research question itself. Here are some examples:
In "Population (P)," what is the difference between "Intervention (I)" and "Comparator (C)" on "Outcome (O)" across "Time (T)?"
In "Population (P)," what is the change in "Outcome (O)" across "Time (T)" from baseline or "Comparator (C)" associated with "Intervention (I)?"
In "Population (P)," what is the correlation between "Intervention (I)" and "Comparator (C)" during "Time (T)?"
In "Population (P)," does "Intervention (I)" versus "Comparator (C)" predict for "Outcome (O)" during "Time (T),"" when controlling for other variables?"
*required
Hypothesis testing is a central concept when asking research questions in applied research. Remember that researchers choose an independent variable because they hypothesize some sort of association with the dependent/outcome variable. Once a researcher has worked through the PICOT framework to generate a research question, it is useful to see how the PICOT components are stated in the null and alternative hypotheses. Here are some examples:
Null hypothesis: There is no difference between "Intervention (I)" and "Comparator (C)" on "Outcome (O)."
Alternative hypothesis: There is a difference between "Intervention (I)" and "Comparator (C)" on "Outcome (O)."
Null hypothesis: There is no change in "Outcome (O)" across "Time (T)" associated with "Intervention (I)."
Alternative hypothesis: There is a change in "Outcome (O)" across "Time (T)" associated with "Intervention (I)."
Null hypothesis: There is no correlation between "Intervention (I)" and "Comparator (C)."
Alternative hypothesis: There is a correlation between "Intervention (I)" and "Comparator (C)."
Null hypothesis: "Intervention (I)" versus "Comparator (C)" does not predict for "Outcome (O)."
Alternative hypothesis: "Intervention (I)" versus "Comparator (C)" does predict for "Outcome (O)."
The choice of sampling methodology made by researchers depends upon many factors when asking a PICOT research question. If researchers are mining existing data for purposes of a retrospective research design, then a non-probability sampling methodology is being used, often known as convenience sampling. If researchers are planning a prospective research design where outcomes have not occurred AND participants are being randomly selected from the population and being randomly assigned to treatment groups, then probability sampling is being used. Randomization is a central component of probability sampling and allows for researchers to make causal inferences.
Which sampling methodology will you use to sample from the population? *required
Researchers must choose if they are going to perform a retrospective research design or a prospective research design to answer the PICOT research question. The primary difference between retrospective and prospective designs relates to when the Outcome (O) of the PICOT framework is observed. If the outcome has already occurred in the past, then a retrospective research design is being used. If the outcome occurs in the future, then a prospective research design is being used.
The PICOT framework can be directly mapped onto any of the following research designs and can assist in understanding how a research question can be answered by a research design.
Which of the following research designs will you use to answer your PICOT research question? *required
In case-control studies, a group of study participants are chosen for participation based on having an outcome of interest. This study group is known as the case group. Next, a group of study participants are sampled that do not have the outcome of interest and are known as the control group. The case and control groups are then compared on demographic and clinical variables that may be potential risk factors for having the outcome of interest. Case-control designs are useful for studying rare disease outcomes and generating hypotheses. There are numerous selection and observation biases associated with a case-control design. See the visual below to understand how PICOT can be mapped onto case-control study.
Click on the button below if a case-control design will answer your PICOT research question.
In cross-sectional studies, participant data is collected at one point in time and analyzed to establish the prevalence of demographic and clinical parameters in a given population. Prevalence is defined as the proportion of a clinical parameter or disease state in a population at any given time. All survey studies are considered cross-sectional designs. Inferential associations (provide a p-value) can be tested, when hypothesized in an a priori fashion, but selection (lack of randomization) and observation (effect-cause) biases should be taken into consideration. See the visual below that depicts the PICOT framework mapped onto a cross-sectional study.
Click on the button below if a cross-sectional design will answer your PICOT research question.
In retrospective cohort studies, an existing cohort of participants from a population of interest and relevant predictor, demographic, and clinical variables are mined to test for significant differences or associations with outcomes of interest. Often times, there is an exposure group and a non-exposure group in the cohort that can be compared on outcome variables across time. Measures of odds and risk with 95% confidence intervals can be calculated. Selection biases (lack of randomization) and observation biases (loss to follow-up and missing or unreliable data) exist. See below for a depiction of how the PICOT framework can be mapped onto a retrospective cohort.
Click on the button below if a retrospective cohort design will answer your PICOT research question.
In prospective cohort studies, a cohort of participants are sampled from the population. Baseline data on demographic, clinical, and prognostic characteristics are collected. Participants are allocated to intervention groups to test for treatment effects and followed longitudinally to the development of outcomes. Measures of incidence, or the number of new and emergent cases in a population moving forward in time, and risk can be calculated. The time period for follow-up of outcomes should be defined and allow for enough time for outcomes to occur. There are fewer observation and selection biases, but causal inferences cannot be determined. See how PICOT can be mapped onto a prospective cohort design below.
Click on the button below if a prospective cohort design will answer your PICOT research question.
In randomized controlled trials, a population is described in regards to inclusion and exclusion criteria, the population is sampled from using random selection, and then the selected participants are randomly assigned to either a treatment group or a control group. Once assigned, participants either receive the treatment or the control intervention and are then followed forward in time to observe the outcome of interest. Randomization removes confounding effects, reduces selection and observation biases, and allows for causal inferences to be made when testing for the effects of treatments on outcomes. Blinding is also utilized to reduces biases. Randomized controlled trials are considered the "gold standard" research method, but can be costly and take years to complete. The PICOT framework is mapped onto a visual depiction of a randomized controlled trial below.
Click on the button below if a randomized controlled trial will answer your PICOT research question.
What is the primary independent variable in your PICOT research question?
What is the primary dependent variable in your PICOT research question?
What demographic variables will you collect to describe the sample that is taken from the population of interest? Enter one demographic variable per row and then click on the Submit button.
What clinical, confounding, or control variables will you collect for your study? Enter one variable per row and then click on the submit button.
When it comes to planning for the statistical power associated with your PICOT research question, there are five primary components to be taken into consideration:
Click on the button below to get an explanation of the statistical power for your PICOT research question.
Inferential statistics are used to answer PICOT research questions. When it comes to choosing the correct statistical test based on your PICOT research question, there are three primary aspects of your study that you have already defined that assist in making that decision:
Click on the button below to get the statistical test will answer your PICOT research question.
The PICOT research question and its components can assist with the creation and maintenance of research databases. Choose which type of database you will use to enter, store, and maintain your research data. *required
Receive your PICOT research question, research hypothesis, sampling methodology, research design, variables, statistical power, database, and statistical analysis based on your choices above.
References
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ Lawrence Erlbaum Associates, Publishers.