diff --git a/DESCRIPTION b/DESCRIPTION
index de17d61..59bb5d6 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -6,7 +6,8 @@ Authors@R: c(
     person("Frederik", "Aust", email = "frederik.aust@uni-koeln.de", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-4900-788X")),
     person("Lisa", "Spitzer", email = "lisa.spitzer95@yahoo.de", role = "aut"),
     person("Jeffrey R.", "Stevens", email = "jeffrey.r.stevens@gmail.com", role = "ctb", comment = c(ORCID = "0000-0003-2375-1360")),
-    person("Masataka", "Ogawa", email = "ogawa@phiz.c.u-tokyo.ac.jp", role = "ctb")
+    person("Masataka", "Ogawa", email = "ogawa@phiz.c.u-tokyo.ac.jp", role = "ctb"),
+    person("Henry", "Kouker", email = "henry.samuel.kouker@gmail.com", role = "aut")
   )
 Description: Provides a collection of templates to author preregistration documents for scientific studies in PDF format.
 URL: https://github.com/crsh/prereg
diff --git a/inst/rmarkdown/templates/aspredicted_prereg/skeleton/skeleton.Rmd b/inst/rmarkdown/templates/aspredicted_prereg/skeleton/skeleton.Rmd
index 8cd58bc..c8166e4 100644
--- a/inst/rmarkdown/templates/aspredicted_prereg/skeleton/skeleton.Rmd
+++ b/inst/rmarkdown/templates/aspredicted_prereg/skeleton/skeleton.Rmd
@@ -31,55 +31,65 @@ output: prereg::prereg_pdf
 **It's complicated.** We have already collected some data but explain in Question 8 why readers may consider this a valid **pre**-registration nevertheless.	
 
 
+## Data collection
+<!-- Have any data been collected for this study already? (Note: "Yes" is not an accepted answer.) -->
 
-## Hypothesis
-<!-- What's the main question being asked or hypothesis being tested in this study? -->
+**Yes**, we already collected the data.
 
-> Example: A month-long academic summer program for disadvantaged kids will reduce the drop in academic performance that occurs during the summer.
+**No**, no data have been collected for this study yet.
 
+**It's complicated.** We have already collected some data but explain in Question 8 why readers may consider this a valid **pre**-registration nevertheless.	
+
+
+
+## Hypothesis
+<!-- What's the main question being asked or hypothesis being tested in this study? Example: A month-long academic summer program for disadvantaged kids will reduce the drop in academic performance that occurs during the summer.-->
 
-## Dependent variable
-<!-- Describe the key dependent variable(s) specifying how they will be measured. -->
 
-> Example: Simple average GPA across all courses during the first semester after the intervention.
+## Dependent variable
+<!-- Describe the key dependent variable(s) specifying how they will be measured. Example: Simple average GPA across all courses during the first semester after the intervention.-->
 
 
 ## Conditions
-<!-- How many and which conditions will participants be assigned to? -->
+<!-- How many and which conditions will participants be assigned to?
 
-> Example 1: Two conditions: Offering summer program: yes vs no.
+ Example 1: Two conditions: Offering summer program: yes vs no.
 
-> Example 2: 12 conditions in a mixed design lab study. Participants will be assigned to one of four conditions: math training, verbal training, memory task, or control (4 between-subject conditions). Each participant will complete a math test, a verbal test, and a memory test (3 within-subject conditions).
+ Example 2: 12 conditions in a mixed design lab study. Participants will be assigned to one of four conditions: math training, verbal training, memory task, or control (4 between-subject conditions). Each participant will complete a math test, a verbal test, and a memory test (3 within-subject conditions).-->
 
 
 ## Analyses
-<!-- Specify exactly which analyses you will conduct to examine the main question/hypothesis. -->
-
-> Example: Linear regression predicting the simple average GPA in the semester after the intervention with a dummy variable indicating whether the participant was offered the summer program or not (intention-to-treat analysis). We will also conduct the same regression controlling for simple average GPA during the semester before the intervention, gender, & household income (an 8-point scale ranging from 1 = below $20,000 and 8 = above $150,000).
+<!-- Specify exactly which analyses you will conduct to examine the main question/hypothesis.
+Example: Linear regression predicting the simple average GPA in the semester after the intervention with a dummy variable indicating whether the participant was offered the summer program or not (intention-to-treat analysis). We will also conduct the same regression controlling for simple average GPA during the semester before the intervention, gender, & household income (an 8-point scale ranging from 1 = below $20,000 and 8 = above $150,000).-->
 
 
 ## Outliers and exclusions
-<!-- Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations. -->
+<!-- Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations.
 
-> Example 1: We will compute the overall mean and standard deviation across all conditions, and winsorize at 2.5 SD above/below the mean.
-> Example 2: We will exclude participants who incorrectly answer at least 2 of our 3 attention check questions.
-> Example 3: We will exclude any participants who complete the survey in less than 30 seconds.
+ Example 1: We will compute the overall mean and standard deviation across all conditions, and winsorize at 2.5 SD above/below the mean.
+ Example 2: We will exclude participants who incorrectly answer at least 2 of our 3 attention check questions.
+ Example 3: We will exclude any participants who complete the survey in less than 30 seconds.-->
 
 
 ## Sample size
-<!-- How many observations will be collected or what will determine sample size? No need to justify decision, but be precise about _exactly_ how the number will be determined. -->
+<!-- How many observations will be collected or what will determine sample size? No need to justify decision, but be precise about _exactly_ how the number will be determined. 
 
-> Example: We will offer the program until 500 people have agreed to participate in it or until June 30, 2016 (whichever comes first).
+Example: We will offer the program until 500 people have agreed to participate in it or until June 30, 2016 (whichever comes first).-->
 
 
 ## Other
-<!-- Anything else you would like to pre-register? (e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?) -->
+<!-- Anything else you would like to pre-register? (e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?) 
+
+ Example: We will include a battery of questions for exploratory purposes, including life satisfaction, amount of videogame playing, and family activity. We will also provide an additional survey with 24 questions assessing achievement orientation. We will not report the results of those analyses for the project being pre-registered.
+ NOTE: If you leave this blank it will read 'Nothing else to pre-register.'-->
 
-> Example: We will include a battery of questions for exploratory purposes, including life satisfaction, amount of videogame playing, and family activity. We will also provide an additional survey with 24 questions assessing achievement orientation. We will not report the results of those analyses for the project being pre-registered.
-> NOTE: If you leave this blank it will read 'Nothing else to pre-register.'
+## Name
+<!-- Give a title for this Aspredicted pre-registration
+Suggestion: use the name of the project, followed by study description.
 
-## Study type
-Finally. For record keeping purposes, please tell us the type of study you are pre-registering.
+Example: SUMMER PROGRAMS - GPA performance, Chicago, July 2018-->
+
+## Type of study
 
 * Class project or assignment
 * Experiment
@@ -87,6 +97,15 @@ Finally. For record keeping purposes, please tell us the type of study you are p
 * Observational/archival study
 * Other:
 
+## Data source
+
+* Prolific
+* MTurk
+* Cloud Research
+* University lab
+* Field experiment / RCT
+* Other:
+
 
 # References
 ## 
diff --git a/inst/rmarkdown/templates/cruewell_prereg/skeleton/skeleton.Rmd b/inst/rmarkdown/templates/cruewell_prereg/skeleton/skeleton.Rmd
index b65b66b..ab62f8d 100644
--- a/inst/rmarkdown/templates/cruewell_prereg/skeleton/skeleton.Rmd
+++ b/inst/rmarkdown/templates/cruewell_prereg/skeleton/skeleton.Rmd
@@ -20,17 +20,35 @@ output: prereg::prereg_pdf
 
 # Study Information
 
+
 ## Description 
 <!-- Briefly describe your study, giving some information on its background and purpose.
-
-Keep this description short---while it is helpul to give some context for your
-project, you do not have to include a whole introductory section for the purposes of this preregistration. -->
+Example: While animals have been found to be able to reach optimality in the
+speed-accuracy tradeoff, the evidence for humans is mixed. It is unclear whether
+humans only do not act optimally by default, or whether they are unable to do so. Here,
+we want to investigate this issue by giving participants time to practice, and varying the
+amount of feedback they receive, to see if these affect whether they reach optimality in
+the speed-accuracy tradeoff.
+More info: Keep this description short – while it is helpful to give some context for your
+project, you do not have to include a whole introductory section for the purposes of this
+preregistration -->
 
 Enter your response here.
 
 
-## Hypotheses
-<!-- List the hypotheses to be tested. Ensure specificity, precisenesss, and exhaustiveness by stating all your hypotheses/predictions as specifically and unambigXuously as possible, ideally also emphasising that you will only be testing these hypotheses in your confirmatory analyses. Do specify if the hypotheses are directional or non-directional, and state the direction if appropriate. -->
+## Hypotheses*
+<!-- List the hypotheses to be tested. Ensure specificity, precisenesss, and exhaustiveness
+by stating all your hypotheses/predictions as specifically and unambiguously as possible,
+ideally also emphasising that you will only be testing these hypotheses in your
+confirmatory analyses. Do specify if the hypotheses are directional or non-directional,
+and state the direction if appropriate.
+Example: We will test the following hypotheses only :
+H1 With suitable practice and medium feedback, participants get closer to optimality with
+each block of trials. (directional)
+H2 After suitable practice and medium feedback, participants will have an approximately
+optimal speed-accuracy trade-off. (directional)
+H3 Participants who complete a fixed number of trials are closer to optimality than
+participants who complete trials in a fixed amount of time. (directional) -->
 
 Enter your response here.
 
@@ -39,68 +57,88 @@ Enter your response here.
 <!-- If you are using pre-existing data, please fill in this section and ignore the sections Sampling Plan, Design Plan, and Variables. If you are collecting the data yourself, please ignore this section and fill in the sections Sampling Plan, Design Plan, and Variables. -->
 
 
-## Dataset(s)
-<!-- Name or brief description of dataset(s) -->
+## Dataset(s)*
+<!-- Name or brief description of dataset(s) Example: Motion discrimination task with a random dot kinematogram in 70 participants.
+The data used for this study is only a relevant subset of the full data set, which includes
+more groups and 133 participants in total -->
 
 Enter your response here.
 
 
-## Publically available?
+## Publically available?*
 
 **Yes**
 
 **No**
 
 
-## Access
+## Access*
 <!-- How can the data be accessed? Provide link if available online. -->
 
 Enter your response here.
 
 
-## Date of access 
-
-Enter your response here.
-
-
-## Data Source
-<!-- Please select and describe what entity originally collected this data. -->
+## Date of access/download*
 
 Enter your response here.
 
 
-## Codebook
-<!-- What kind of sample did the original study use? (e.g., student, Mturk, representative) -->
+## Data Source*
+<!-- Please select and describe what entity originally collected this data. (required)
+Examples/Options:
+National Data Set – a nationally representative sample collected by another team of
+researchers.
+Private Organizational Data – Internally collected data by an organization made available
+for academic purposes.
+Own Lab Collection-Data were connected by one of the analysts’ lab.
+Other Lab Collection – Data were collected by another researcher’s lab (analysts were
+not involved in data collection).
+Meta-Analysis – A systematic review of published studies.
+Multi-lab collaboration – Data were collected at several sites using the same procedure.
+Other – please explain -->
 
 Enter your response here.
 
 
-## Survey format 
-<!-- Some studies (usually publically aYailable) offer codebooks to describe their data. If such a codebook is available, please link to it here or upload the document. -->
+## Codebook*
+<!--Some studies (usually publically available) offer codebooks to describe their data. If such
+a codebook is available, please link to it here or upload the document. -->
 
 Enter your response here.
 
 
-## Sampling and data collection
-<!-- If the data collection procedure is already well documented, please provide a link to the information. If the data collection procedure is not yet well documented, please describe, to the best of your ability, how data were collected. What populations were sampled from, what were the recruitment efforts, what was the procedure for running participants through the study, were researchers blind to the research question, hypotheses or conditions, was randomization of any kind used, etc.? -->
+## Sampling and data collection procedures*
+<!-- If the data collection procedure is already well documented, please provide a link to the
+information. If the data collection procedure is not yet well documented, please describe,
+to the best of your ability, how data were collected. What populations were sampled
+from, what were the recruitment efforts, what was the procedure for running participants
+through the study, were researchers blind to the research question, hypotheses or
+conditions, was randomization of any kind used, etc?
+Example: 70 participants were recruited at the University of Newcastle and completed
+the experiment online, for which they received course credit. The participants were
+randomly (and equally) divided into the two groups of fixed trials and fixed time. The
+task used the random dot kinetogram, following Evans & Brown (2017) and Evans et al.
+(2018) -->
 
 Enter your response here.
 
 
-## Prior work
-<!-- Have you published/presented any previous work based on this dataset? Include any publications, conference presentations (papers, posters), or working papers (in-prep, unpublished, preprints) based on this data set that you have worked on. -->
+## Prior work based on the dataset*
+<!--Have you published/presented any previous work based on this dataset? Include
+any publications, conference presentations (papers, posters), or working papers
+(in-prep, unpublished, preprints) based on this data set that you have worked on.
+Example: There does not exist prior work based on this data set. -->
 
 Enter your response here.
 
 
-## Prior research
-<!-- Have you worked with these data before? Describe any prior research activity using these data in a specific and transparent way. -->
-
-Enter your response here.
-
-
-## Prior knowledge
-<!-- Describe any prior knowledge of the dataset. Be specific and transparent. -->
+## Prior Research Activity*
+<!-- Describe any prior knowledge of the dataset. Be specific and transparent.
+Example: We have already fit the model from Figure 1 [not included here] to the
+data and checked the results from one of the groups that are not relevant for the
+current study, and has run the model-based analyses using the model in Figure 2
+[not included in example] and here also checked the consistency of the Bayes
+factor approximation for one of the irrelevant groups. -->
 
 Enter your response here.
 
@@ -109,77 +147,118 @@ Enter your response here.
 
 <!-- If you are using pre-existing data, you may delete this section. -->
 
-## Data collection
-
-<!-- Please describe your data collection process. If you are using human subjects, this should include the population from which you obtain subjects, recruitment efforts, payment for participation, how subjects will be selected for eligibility from the initial pool (e.g. inclusion and exclusion rules), and your study timeline. For studies that do not include human subjects, include information about how and for how long samples will be collected, the source or location of samples, and/or the batch numbers you will use.
-
-The answer to this question should be specific enough that another person could repeat the data collection procedures and recreate the study population. Alternatively, if the study population would be unable to be reproduced because it relies on a specific set of circumstances unlikely to be recreated (e.g., a community of people from a specific time and location), the criteria and methods for creating the group and the rationale for this unique set of subjects should be clear. -->
+## Data collection procedures*
+
+<!-- Please describe your data collection process. If you are using human subjects, this
+should include the population from which you obtain subjects, recruitment efforts,
+payment for participation, how subjects will be selected for eligibility from the initial pool
+(e.g. inclusion and exclusion rules), and your study timeline. For studies that do not
+include human subjects, include information about how and for how long samples will be
+collected, the source or location of samples, and/or the batch numbers you will use.
+Example: Participants will be students recruited through advertisements at a university,
+where they will receive course credit for their participation.
+More information: The answer to this question should be specific enough that another
+person could repeat the data collection procedures and recreate the study population.
+Alternatively, if the study population would be unable to be reproduced because it relies
+on a specific set of circumstances unlikely to be recreated (e.g., a community of people
+from a specific time and location), the criteria and methods for creating the group and the
+rationale for this unique set of subjects should be clear -->
 
 Enter your response here.
 
 
-## Sample size
-<!-- Describe the sample size of your study. How many units will be analyzed in the study? If the units are not individuals, then describe the size requirements for each unit. Eiter state exact numbers or an expected range. -->
+## Sample size*
+<!--Describe the sample size of your study. How many units will be analyzed in the study? If
+the units are not individuals, then describe the size requirements for each unit. Eiter state
+exact numbers or an expected range.
+Example: Our target sample size is 70 participants. We will attempt to recruit up to 90, to
+allow for exclusions. -->
 
 Enter your response here.
 
 
 ## Sample size rationale
-<!-- This could include an arbitrary constraint such as time, money, or personnel, power analysis (if applicable), or an analysis based on a parameter recovery study.
-
-This gives you an opportunity to specifically state how the sample size will be determined. A wide range of possible answers is acceptable; remember that transparency is more important than principled justifications. Any pre-specified reasoning behind the sample size is preferable to ambiguity and potential confusion for the reader. -->
+<!-- This could include an arbitrary constraint such as time, money, or personnel, power
+analysis (if applicable), or an analysis based on a parameter recovery study.
+Example: The number of subjects is based on a parameter recovery study.
+More information: This gives you an opportunity to specifically state how the sample
+size will be determined. A wide range of possible answers is acceptable; remember that
+transparency is more important than principled justifications. Any pre-specified reasoning
+behind the sample size is preferable to ambiguity and potential confusion for the reader -->
 
 Enter your response here.
 
 
 ## Stopping rule
-<!-- If you cannot pre-specify your sample size, specify a stopping rule, i.e., how you will decide when to terminate your data collection.
-
-Unacceptable rationales include stopping based on e.g. p-values if checkpoints and stopping rules are not specified. If you have control over your sample size, then including a stopping rule is not necessary, though it must be clear in this question or a previous question how an exact sample size is attained. -->
+<!-- If you cannot pre-specify your sample size, specify a stopping rule, i.e., how you will
+decide when to terminate your data collection.
+Example: We will post participant sign-up slots by week on the preceding Friday night,
+with 20 spots posted per week. We will post 20 new slots each week if, on that Friday
+night, we are below 320 participants.
+More information: Unacceptable rationales include stopping based on e.g. p-values if
+checkpoints and stopping rules are not specified. If you have control over your sample
+size, then including a stopping rule is not necessary, though it must be clear in this
+question or a previous question how an exact sample size is attained. -->
 
 Enter your response here.
 
 
 
 # Design Plan
-<!-- If you are using pre-existing data, you may delete this section. In this section, you will be asked to describe the experimental design of your study. Remember that this research plan is designed to register a single study, so if you have multiple experimental designs, please complete a separate preregistration. Note that this is about experimental design; your modelling design choices will be registered in a later section. -->
-
-## Study type
-
-**A. Experiment --- A researcher randomly assigns treatments to study subjects, this includes field or lab experiments. This is also known as an intervention experiment and includes randomized controlled trials.**
-
-**B. Observational Study - Data is collected from study subjects that are not randomly assigned to a treatment. This includes surveys, natural experiments, and regression discontinuity designs.**
-
-**C. Other**
-
-## Blinding
-<!-- Blinding describes who is aware of the experimental manipulations within a study. Mark all that apply. -->
-
-**A. No blinding is involved in this study.**
-
-**B. For studies that involve human subjects, they will not know the treatment group to which they have been assigned.**
-
-**C. Personnel who interact directly with the study subjects (either human or non-human subjects) will not be aware of the assigned treatments. (Commonly known as "double blind").**
-
-**D. Personnel who analyze the data collected from the study are not aware of the treatment applied to any given group.**
+<!-- If you are using pre-existing data, you may delete this section. In this section, you will be asked
+to describe the experimental design of your study. Remember that this research plan is
+designed to register a single study, so if you have multiple experimental designs, please
+complete a separate preregistration. Note that this is about experimental design; your modelling
+design choices will be registered in a later section. -->
+
+## Study type*
+<!-- Please select one of the following options by deleting the others -->
+A. Experiment - A researcher randomly assigns treatments to study subjects, this
+includes field or lab experiments. This is also known as an intervention experiment
+and includes randomized controlled trials.
+
+B. Observational Study - Data is collected from study subjects that are not randomly
+assigned to a treatment. This includes surveys, natural experiments, and regression
+discontinuity designs. 
+
+C. Other
+
+
+## Blinding*
+<!-- Blinding describes who is aware of the experimental manipulations within a study. Mark
+all that apply.-->
+A. No blinding is involved in this study.
+B. For studies that involve human subjects, they will not know the treatment group to
+which they have been assigned.
+C. Personnel who interact directly with the study subjects (either human or non-human
+subjects) will not be aware of the assigned treatments. (Commonly known as
+“double blind”)
+D. Personnel who analyze the data collected from the study are not aware of the
+treatment applied to any given group. -->
 
 ## Additional blinding
-
+<!--Is there any additional blinding in this study?-->
 Enter your response here.
 
 
-## Experimental design
+## Experimental design*
 <!-- Describe your experimental design.
-
-This question has a variety of possible answers. The key is for a researcher to be as detailed as is necessary given the specifics of their design. -->
+Example: We have a 3 (feedback: low, medium, high) by 2 (fixed time, fixed trial) by 23
+(practice block) design.
+More info: This question has a variety of possible answers. The key is for a researcher
+to be as detailed as is necessary given the specifics of their design -->
 
 Enter your response here.
 
 
 ## Randomization
 <!-- If you are doing a randomized study, how will you randomize, and at what level?
-
-If randomization is required for the study, the method should be specified here, not simply the source of random numbers. --> 
+Example: We will use block randomization, where each participant will be randomly
+assigned to one of the four equally sized, predetermined blocks. The random number list
+used to create these four blocks will be created using the web applications available at
+http://random.org.
+More info: If randomization is required for the study, the method should be specified
+here, not simply the source of random numbers. --> 
 
 Enter your response here.
 
@@ -189,61 +268,116 @@ Enter your response here.
 <!-- In this section you can describe all variables that are manipulated and measured in your experiment. If you are using pre-existing data, you may delete this section. -->
 
 ## Manipulated variables
-<!-- Describe all variables you plan to manipulate and the levels or treatment arms of each variable. This is not applicable to any observational study.
-
-For any experimental manipulation, you should give a precise definition of each manipulated variable. -->
+<!-- Describe all variables you plan to manipulate and the levels or treatment arms of each
+variable. This is not applicable to any observational study.
+Example: We manipulated the level of feedback given to participants, and whether they
+completed trials in a fixed amount of time, or a fixed number of trials.
+More information: For any experimental manipulation, you should give a precise
+definition of each manipulated variable. -->
 
 Enter your response here.
 
 
-## Measured variables
+## Measured variables*
 <!-- Describe each variable that you will measure.
-
-Observational studies will include only measured variables. As with the previous questions, the answers here must be precise. -->
+Example: The outcome variables measured are reaction time and response accuracy.
+More information: Observational studies will include only measured variables. As with
+the previous questions, the answers here must be precise. -->
 
 Enter your response here.
 
 
 ## Indices
-<!-- If any measurements are going to be combined into an index (or even a mean), what measures will you use and how will they be combined? Include either a formula or a precise description of your method.
-
-If you are using multiple pieces of data to construct a single variable, how will this occur? Both the data that are included and the formula or weights for each measure must be specified. Standard summary statistics, such as means do not require a formula, though more complicated indices require either the exact formula or, if it is an established index in the field, the index must be unambiguously defined. For example, "biodiversity index" is too broad, whereas "Shannon's biodiversity index" is appropriate. -->
+<!-- If any measurements are going to be combined into an index (or even a mean), what
+measures will you use and how will they be combined? Include either a formula or a
+precise description of your method.
+Example: For each cell and each individual, we will calculate mean response time and
+accuracy.
+More information: If you are using multiple pieces of data to construct a single variable,
+how will this occur? Both the data that are included and the formula or weights for each
+measure must be specified. Standard summary statistics, such as “means” do not
+require a formula, though more complicated indices require either the exact formula or, if
+it is an established index in the field, the index must be unambiguously defined. For
+example, “biodiversity index” is too broad, whereas “Shannon’s biodiversity index” is
+appropriate. -->
 
 Enter your response here.
 
 # Data Cleaning
 
 
-## Data exclusion
-<!-- How will you determine what data (e.g., participants or trials), if any, will be excluded from your analyses? How will outliers be handled? Will you use any awareness checks?
-
-Any rule for excluding a particular set of data is acceptable. You may describe rules for excluding a participant and/or for identifying outlier data. -->
+## Data exclusion*
+<!-- How will you determine what data (e.g., participants or trials), if any, will be excluded
+from your analyses? How will outliers be handled? Will you use any awareness checks?
+Example: For all participants, the first block of trials will be excluded to allow for
+participants to become adequately practiced at the task. Trials with response times
+below 150ms or above 10000ms will be excluded as anticipatory responses and trials
+where participants lost attention, respectively. Participants with task accuracy below 60%
+or less than 200 eligible trials (based on the number of trials required for accurate
+parameter estimation) will be excluded.
+More information: Any rule for excluding a particular set of data is acceptable. You may
+describe rules for excluding a participant and/or for identifying outlier data. -->
 
 Enter your response here.
 
 
 ## Missing data
-<!-- How will you deal with incomplete or missing data?
-
-Any relevant explanation is acceptable. As a final reminder, remember that the final analysis must follow the specified plan, and deviations must be either strongly justified or included as a separate, exploratory analysis. -->
+<!--How will you deal with incomplete or missing data?
+Example: If a subject does not complete the entire duration of the experiment, that
+subject will not be included in the analysis.
+More information: Any relevant explanation is acceptable. As a final reminder,
+remember that the final analysis must follow the specified plan, and deviations must be
+either strongly justified or included as a separate, exploratory analysis. -->
 
 Enter your response here.
 
 
 # Cognitive modelling
 
-## Cognitive model
-<!-- Please include the type of model used (e.g. diffusion model, Linear ballistic accumulator model), and a specific parameterisation/parameterisations.
-
-The architecture of the model should be pre-specified in a way that is specific, precise, and exhaustive. In the given example, we emphasized that only the stated parameters will be estimated. To this end, you should also ideally include a plate diagram and specify the relevant equations. Motivate your choices. Note: If you are using e.g. Bayesian hierarchical modelling for parameter estimation, the structure of the hierarchical model and the prior distribution over the parameters belong into this parameterisation as well. -->
+## Cognitive model*
+<!-- Please include the type of model used (e.g. diffusion model, Linear ballistic accumulator
+model), and a specific parameterisation/parameterisations.
+Example: The parameters of a simple diffusion model will be estimated, namely only:
+drift rate (v), starting point (z), threshold (a), non-decision time (ter). This differs from
+[previous paper], where the full diffusion model was estimated, i.e., including
+between-trial variability parameters for drift rate, starting point, and non-decision time.
+These between-trial variability parameters are not relevant here, and without them, the
+simple diffusion model has better parameter recovery results (Lerche & Voss, 2016).
+Figure 1 shows a plate diagram of the hierarchical structure used for the qualitative
+model-based analysis assessing 1) whether groups appear to get closer to optimality
+over time, 2) whether each group differs from optimality, and 3) whether there appears to
+be a difference between the groups (see Analysis Plan for more information); i indexes
+participants, and j indexes blocks. Only the threshold parameter varies between blocks,
+to estimate changes in the speed accuracy trade-off. More information: The architecture of the model should be pre-specified in a way that is
+specific, precise, and exhaustive (as can be seen in the example above). In the given
+example, we emphasise that ”only” the stated parameters will be estimated. To this end,
+you should also ideally include a plate diagram as mentioned in the example (see e.g.,
+Figure 1) and specify the relevant equations. Motivate your choices. Note: If you are
+using e.g. Bayesian hierarchical modelling for parameter estimation, the structure of the
+hierarchical model and the prior distribution over the parameters belong into this
+parameterisation as well. -->
 
 Enter your response here.
 
 
-## Parameter estimation
+## Parameter estimation*
 <!-- Please specify and motivate your method of parameter estimation.
-
-If you are not interested in the parameters and are going straight to statistical inference without estimating the parameters, please state this clearly and motivate this choice. If you are using Bayesian methods, specify and motivate priors. In general, specify as much as possible, including e.g. the starting point (distribution) for estimation. If the data are going to be summarised into descriptive statistics, state which descriptive statistics will be used, and how. -->
+Example: Only Bayesian hierarchical modeling will be used to estimate the parameters
+of the diffusion model, constraining individual-level parameters to follow group-level
+truncated normal distributions. For the estimation model (see Figure 1, the two groups
+(fixed-trial and fixed-time) are given a separate hierarchical structure, and the
+group-level parameters are not constrained between groups. We will use likelihood
+functions taken from the ``fast-dm'' toolbox (Voss & Voss, 2007) for the calculation of the
+density function of the simple diffusion model. For the first model, for sampling from the
+posterior distributions over parameters, we will use Markov-chain Monte Carlo with
+differential evolution proposals (Turner et al., 2013), using 66 chains, drawing 3,000
+samples from each, and discarding the first 1,500 samples.
+More information: If you are not interested in the parameters and are going straight to
+statistical inference without estimating the parameters, please state this clearly and
+motivate this choice. If you are using Bayesian methods, specify and motivate priors. In
+general, specify as much as possible, including e.g. the starting point (distribution) for
+estimation. If the data are going to be summarised into descriptive statistics, state which
+descriptive statistics will be used, and how. -->
 
 Enter your response here.
 
@@ -253,43 +387,126 @@ Enter your response here.
 <!-- Please specify at least one confirmatory analysis. The confirmatory analyses described here have to be included in the final article, with a clear distinction from any additional exploratory analyses. This preregistration has to state up front which parameters are assessed, for example to vary across conditions. Only then is it a confirmatory analysis, otherwise it is exploratory. You may describe exploratory plans here, but a clear confirmatory analysis is needed. -->
 
 ## Statistical analyses
-<!-- Specify the methods that will be used to test each hypothesis as precisely as possible. In particular, specify the method(s) and process(es) of (statistical) inference and on which parameters of interest you will be applying them. In the case of model-based analyses, make sure to also include the relevant models in the Cognitive Modelling section above. Keep in mind that any analyses not mentioned and specified in these confirmatory sections have to be clearly labeled as exploratory in the final output.
-
-As with all of the other questions, the key is to provide a specific recipe for analyzing the collected data. Ask yourself: is enough detail provided to run the same analysis again with the information provided by the user? -->
+<!-- Specify the methods that will be used to test each hypothesis as precisely as possible. In
+particular, specify the method(s) and process(es) of (statistical) inference and on which
+parameters of interest you will be applying them. In the case of model-based analyses,
+make sure to also include the relevant models in the Cognitive Modelling section above.
+Keep in mind that any analyses not mentioned and specified in these confirmatory
+sections have to be clearly labeled as exploratory in the final output.
+Example: Our hypotheses will be quantitatively tested by means of only the following
+tests:
+Testing H2: Using only the second half of all 20 blocks (11-20, so as to account for
+participants adjusting to the task), we will test whether each group, separately, differs
+from optimality using Bayes factors, approximated with the Savage-Dickey Ratio on
+𝜇c.
+Testing H3: Again using only blocks 11-20, we will use the Savage-Dickey method on
+𝛥c (𝛥c = 𝜇c1 - 𝜇c2) to test whether the groups differ in their distance from optimality.
+More information: As with all of the other questions, the key is to provide a specific
+recipe for analyzing the collected data. Ask yourself: is enough detail provided to run the
+same analysis again with the information provided by the user? -->
 
 Enter your response here.
 
 
 ## Other analyses
-<!-- Specify any other confirmatory analyses you intend to perform. -->
+<!-- Specify any other confirmatory analyses you intend to perform.
+Example: Testing H1/2/3: In addition to the statistical analyses listed above, we will
+qualitatively compare the posterior distributions of the decision threshold parameters
+(actual thresholds for each block as estimated using the model in Figure 1) against the
+posterior predictive distributions for the optimal threshold calculated as above -->
 
 Enter your response here.
 
 
 ## Inference criteria
-<!-- What criteria will you use to make inferences? Please describe the information you will use (e.g. p-values, Bayes Factors, etc.), as well as a cut-off criterion, where appropriate. Will you be using one or two tailed tests for each of your analyses? If you are comparing multiple conditions or testing multiple hypotheses, will you account for this? -->
+<!--What criteria will you use to make inferences? Please describe the information you will
+use (e.g. p-values, Bayes Factors, etc.), as well as a cut-off criterion, where appropriate.
+Will you be using one or two tailed tests for each of your analyses? If you are comparing
+multiple conditions or testing multiple hypotheses, will you account for this?
+Example: These are the inference criteria for our analyses listed above:
+1. Criterion for BF testing H2: Following Jeffreys (1961) for the interpretation of strength
+of evidence given a Bayes Factor. If c = 0, the behaviour of the group is optimal, if c < 0
+it is urgent, and if c > 0 it is cautious.
+2. Criterion for BF testing H3: Following Jeffreys (1961) for the interpretation of strength
+of evidence given a Bayes Factor. If
+𝝙c = 0, the groups are the same regarding their
+difference from optimality, and if
+𝝙c < 0 or 𝝙c > 0, the groups differ in their distance from
+optimality.
+3. Criterion to test H1, qualitatively evaluating the plots comparing posterior to posterior
+predictive distribution: If the actual thresholds of both (or one) group(s) clearly show a
+trend towards optimality, we will conclude that participants move towards optimising the
+speed-accuracy trade-off. Otherwise, our conclusion will be suitably less strong and
+discuss any lack of clarity. -->
 
 Enter your response here.
 
 
 ## Exploratory analysis
 <!-- Describe any exploratory analyses you expect to do.
-
-An exploratory test is any test where a prediction is not made up front, or there are multiple possible tests that you are going to use. A statistically significant finding in an exploratory test is a great way to form a new confirmatory hypothesis, which could be registered at a later time. It is crucial to clearly distinguish confirmatory from exploratory results in your final article. -->
+Example: We are currently not planning any exploratory analyses.
+More information: An exploratory test is any test where a prediction is not made up
+front, or there are multiple possible tests that you are going to use. A statistically
+significant finding in an exploratory test is a great way to form a new confirmatory
+hypothesis, which could be registered at a later time. It is crucial to clearly distinguish
+confirmatory from exploratory results in your final article. -->
 
 Enter your response here.
 
 
-## Robustness checks/ sensitivity analyses
-<!-- Please specify any planned robustness checks and/or sensitivity analyses, if any. If a parameter recovery study was performed, please report its results and your conclusions here.
+# Robustness checks
+## Robustness Checks and Sensitivity Analyses*
+<!--Please specify any planned robustness checks and/or sensitivity analyses, if any. If a
+parameter recovery study was performed, please report its results and your
+conclusions here.
+Example: The key analysis will be replicated a) including participants/trials that were
+initially excluded in line with our exclusion criteria, and b) using a model in which the
+threshold parameter and the drift rate parameter vary across blocks. Their results will be
+mentioned alongside the key results, and interpreted accordingly. If these results show a
+lack of robustness, this will be an interesting outcome worthy of further investigation.
+More information: This section ensures that robustness checks and parameter
+recovery simulations are not performed and/or reported selectively. It is important to note
+that, given the preregistration of modelling and analyses, it should be clear that any lack
+of robustness is at least not due to post-hoc, data-driven choices. -->
+
+Enter your response here.
+
 
-This section ensures that robustness checks and parameter recovery simulations are not performed and/or reported selectively. It is important to note that, given the preregistration of modelling and analyses, it should be clear that any lack of robustness is at least not due to post-hoc, data-driven choices. -->
+# Contingency plans
+<!-- Please specify any and all contingency plans to ensure that your preregistration plan is
+robust to common issues. There is no need to cover every eventuality, but if possible, try
+and cover some of these common issues suggested here, and/or issues that you
+commonly encounter. Contingency plans may be easiest to specify precisely when
+phrased as the broader issue (e.g., whether or not the data are captured well), the more
+specific conditions that will be assessed to determine whether the contingency plan is
+activated (e.g., the specific trends in the data that will be assessed for sufficient fit), and
+the contingency plan -- or plans -- that will be used.
+Example: A) Issue – The model fits poorly to the data; Condition – The model is unable
+to capture some specific trend(s) in the data that are deemed important a-priori, such as
+specific response time quantiles; Contingency Plan – A different, preregistered
+parameterisation of the same model, or a different model altogether, which you believe
+will be able to account for these trends.
+B) Issue – The sampling algorithm does not appear to be effectively searching through
+the parameter space; Condition – The search algorithm appears to be getting stuck in
+certain areas of the parameter space and gives different answers for different runs;
+Contingency Plan – A different preregistered sampling algorithm, or perhaps a different
+preregistered parameterisation of the model that may have a parameter space that is
+easier to search. C) Issue: The proposed methods end up taking more time than
+planned; Condition – Executing the originally preregistered plans would take a month of
+computing time; Contingency Plan – A preregistered version that is simpler, such as a
+reduced version of the model, or a simpler method of performing inference. -->
 
 Enter your response here.
 
 
-## Contingency plans
-<!-- Please specify any and all contingency plans to ensure that your preregistration plan is robust to common issues. There is no need to cover every eventuality, but if possible, try and cover some of these common issues suggested here, and/or issues that you commonly encounter. Contingency plans may be easiest to specify precisely when phrased as the broader issue (e.g., whether or not the data are captured well), the more specific conditions that will be assessed to determine whether the contingency plan is activated (e.g., the specific trends in the data that will be assessed for sufficient fit), and the contingency plan---or plans---that will be used. -->
+# Other
+
+## Other (optional)
+<!--Any other information not included in the above. You may include e.g., literature cited,
+disclosures of any related work such as replications or work that uses the same data, or
+other context that will be helpful for future readers and/or yourself.-->
+
+Enter your response here.
 
 
 # References
diff --git a/inst/rmarkdown/templates/eeg_erg_prereg/skeleton/skeleton.Rmd b/inst/rmarkdown/templates/eeg_erg_prereg/skeleton/skeleton.Rmd
new file mode 100644
index 0000000..19dc0eb
--- /dev/null
+++ b/inst/rmarkdown/templates/eeg_erg_prereg/skeleton/skeleton.Rmd
@@ -0,0 +1,433 @@
+---
+title           : "EEG ERP Preregistration Templatee"
+shorttitle      : "My preregistration"
+date            : "`r Sys.setlocale('LC_TIME', 'C'); format(Sys.time(), '%d\\\\. %B %Y')`"
+author: 
+  - name        : First Author
+    affiliation : 1
+  - name        : Ernst-August Doelle
+    affiliation : "1,2"
+affiliation:
+  - id          : 1
+    institution : Wilhelm-Wundt-University
+  - id          : 2
+    institution : Konstanz Business School
+
+output: prereg::prereg_pdf
+---
+
+<!--Introduction
+This preregistration template guides researchers who wish to preregister their EEG projects, more specifically studies investigating event-related potentials (ERPs) in the sensor space. We invite researchers to use and/or adapt the template to other forms of EEG analysis (e.g., source space, time-frequency, steady-state).
+We emphasize that all examples in this template are non-exhaustive and are aimed to provide guidance on how to transparently preregister a study’s analysis plan. Besides descriptions for individual items, we provide examples for better illustration only. With these examples we do not intend to provide researchers with guidance about choosing specific preprocessing and analysis steps.
+This template accompanies the paper “Making ERP research more transparent: Guidelines for preregistration” (Paul et al., 2021). The paper describes common ways to preprocess and analyze ERP data, discusses the (often arbitrary) choices that lead to a “garden of forking paths”, and highlights preregistration as a viable solution. It also offers more information and additional examples.-->
+
+
+# Study Information
+
+
+
+## Description
+
+<!-- Please give a brief description of your study, including some background, the
+purpose of the study (e.g., whether it is a replication), or broad research questions.
+ Example: We will test whether the mismatch negativity (MMN) is modulated by pitch differences differently in people with and without musical training. We will present a group of musicians and a group of non-musicians with tones in an oddball paradigm. The difference between the standard and the deviant tone will be either small (4 Hz) or large (8 Hz). We collect EEG data as well as behavioral accuracy in detecting the oddball as measured by a button-press.
+ More info: The description should be no longer than the length of an abstract. It can give some context for the proposed study, but great detail is not needed here for your preregistration.-->
+
+Enter your response here
+
+## Hypotheses*
+
+<!-- List specific, concise, and testable hypotheses. Please state if the hypotheses
+are directional or non-directional. If directional, state the direction. A predicted effect size is also appropriate here. If a specific interaction or moderation is important to your research, you can list that as a separate hypothesis. State the specific ERP component for which an experimental manipulation is (or is not) expected.
+ Example:
+H1 (directional). We predict that, behaviourally, people with musical training would be more accurate at discriminating small pitch differences compared to those without musical training, as evidenced by statistically more accurate performance in the behavioral discrimination task for the small pitch difference among musically trained individuals.
+H2 (directional). We predict that, at the electrophysiological level, people with musical training would show a stronger discrimination response to both small and large pitch differences compared to people without musical training. This would be reflected in a larger mean amplitude of the MMN to both the smaller and the larger pitch difference for the musically trained individuals.-->
+
+Enter your response here
+
+# Design Plan
+<!--In this section, you will be asked to describe the overall design of your study. Remember that this research plan is designed to register a single study, so if you have multiple experimental designs, please complete a separate preregistration.-->
+
+## Stimuli*
+
+<!-- Describe the stimuli used in the experiment.
+ Example: The pitch difference for the small difference is 4 Hz (standard 400
+Hz, deviant 404 Hz), and for the large difference is 8 Hz (standard 400 Hz, deviant 408 Hz). There will be 300 trials per block, 80% of which are standards (N = 240; 400 Hz), and 20% are deviants (N = 60, either 404 Hz or 408 Hz). The stimuli will be created as pure sine tones (mono, sampling frequency of 44100 Hz, amplitude of 0.2 Pa and Fade-in and fade-out durations of 0.01 sec).-->
+
+Enter your response here
+
+## Study design*
+
+<!-- Describe your study design. Is it a between-subject, within-subject, or mixed design?
+ Example: We will employ a mixed 2x2 design, with one between-subject factor musical training (two levels: musicians - non-musicians) and one within-subject factor pitch difference (two levels: small difference, large difference).
+Describe the experimental design. Consider adding the following information:
+* Stimulus presentation duration
+* Inter-stimulus interval
+* Trial duration
+* Inter-trial interval
+* Number of trials
+* Blocked or randomized trial presentation
+* Participant’s task
+* Duration of the study
+* Response window
+ Example. Stimuli will be presented for 400 ms, with an ISI of 500 ms. Participants will be presented with 4 blocks (two with small pitch difference, two with large pitch difference) of 300 trials each, separated by short self-paced pauses. The total duration of the experiment will be about 20 minutes. Participants will be instructed to press the spacebar when they detect the deviant (i.e., stimulus that has a higher pitch).
+More info: This question has a variety of possible answers. The key is for a researcher to be as detailed as is necessary given the specifics of their design. Be careful to determine if every parameter has been specified in the description of the study design. There may be some overlap between this question and the following questions. That is OK, as long as sufficient detail is given in one of the areas to provide all of the requested information. For example, if the study design describes a complete factorial, 2x2 design and the treatments and levels are specified previously, you do not have to repeat that information.-->
+
+Enter your response here
+
+## Randomization*
+
+<!-- If you are doing a randomized study, how will you randomize, and at what
+level? What kind of counterbalancing will you use?
+Example: We will pseudo-randomize the stimuli such that there will be at
+least 3 consecutive standards between each deviant. Participants’ group assignment is based on pre-set musicality criteria and therefore cannot be randomized. Non-musicians will be age-matched to musicians. Block order will be counterbalanced: within each group, participants assigned with an odd number will be presented with the small difference block first, whereas participants assigned with an even number will be presented with the large difference block first.-->
+
+Enter your response here
+
+## Blinding
+
+<!-- Blinding describes who is aware of the experimental manipulations within a
+study. You can specify whether the study was blind for subjects, experimenters or double blind. You can also specify whether the analysis was performed in a blind way.
+Example: Group assignment is based on pre-set musicality criteria, and will not be blinded for the experimenter. The experimenter will not be blinded to block order either, since this is a perceivable pitch difference (at least for a trained listener). Preprocessing will be blinded, that is, the researcher performing the preprocessing will not be aware of the participant’s group, or of the pitch difference of the block.-->
+
+Enter your response here
+
+## Manipulation check
+
+<!--Describe whether you are going to do any manipulation checks
+Example: We will check whether the participants are, independent of our
+manipulation, able to hear the tones. Therefore, we will assess whether all individual participants show a P100 averaged across conditions.
+Sampling Plan
+In this section, please describe how you plan to collect samples, as well as the number of samples you plan to collect and your rationale for this decision. Please keep in mind that the data described in this section should be the actual data used for analysis, so if you are using a subset of a larger dataset, please describe the subset that will actually be used in your study.-->
+
+Enter your response here
+
+## Existing data*
+
+<!-- Please select the description that best describes your situation. Do not hesitate to contact the Center for Open Science (prereg@cos.io) if you have questions about how to answer this question.-->
+
+Registration prior to creation of data: As of the date of submission of this research plan for preregistration, the data have not yet been collected, created, or realized.
+
+Registration prior to any human observation of the data: As of the date of submission, the data exist but have not yet been quantified, constructed, observed, or reported by anyone - including individuals that are not associated with the proposed study. An example could be data collected for a student project that has not been accessed yet.
+
+Registration prior to accessing the data: As of the date of submission, the data exist, but have not been accessed by you or your collaborators. Commonly, this includes data that has been collected by another researcher or institution.
+
+Registration prior to analysis of the data: As of the date of submission, the data exist and you have accessed it, though no analysis has been conducted related to the research plan (including calculation of summary statistics). A common situation is a large dataset that is used for many different studies over time, or when a data set is randomly split into a sample for exploratory analyses and the other section of data is reserved for later confirmatory data analysis. Another scenario for ERP research is that the data have been preprocessed but no ERP averages have been calculated yet.
+
+Registration following analysis of the data: As of the date of submission, you have accessed and analyzed some of the data relevant to the research plan. This includes preliminary analysis of variables, calculation of descriptive statistics, and observation of data distributions.
+If this is the case, please indicate in your final paper that this is a preregistration after (part of the) data has been analyzed.
+
+
+
+## Explanation of existing data
+
+<!--If you indicate that you will be using some data that already exists, please
+describe the steps you have taken to assure that you are unaware of any patterns or summary statistics in the data. This may include an explanation of how access to the data has been limited, who has observed the data, or how you have avoided observing any analysis of the existing data you will use in your study.-->
+
+Enter your response here
+
+## Participant recruitment procedure 
+
+<!-- Please describe the process by which you will collect (or have collected, see
+section 10) your data. If you are using human participants, this should include the sampling population, recruitment efforts, payment/reimbursement for participation, inclusion and exclusion criteria, and study timeline.
+Example: Participants will be recruited through advertisements at the local university and the conservatory. Participants will be paid €10 or receive course credit for their participation. Participants must be at least 18 years old, have normal hearing, with no history of neurological diseases. We will exclude participants that speak a tone language as L1/L2 (acquired before the age of 12). Participants will be considered musicians if they have received at least 12 years of formal music training. Participants will be considered non-musicians if they have never had any formal music training, except for group lessons in primary school.
+More information: The answer to this question requires a specific set of instructions so that another person could replicate the data collection procedures and recreate the study population as closely as possible. If the study population cannot be reproduced (e.g., patients with rare diseases, elite athletes), please clarify this here.-->
+
+Enter your response here
+
+## Sample size*
+
+<!-- Describe the sample size of your study. How many participants will be
+recruited?
+Example: Within the data collection phase of the study (three months), we
+aim to collect clean datasets from 80 participants (40 per group: with vs.
+without musical training).
+More information: For some studies, this will simply be the number of
+participants and/or the number of clusters. For others, this could be an expected range, minimum, or maximum number.-->
+
+Enter your response here
+
+## Sample size rationale*
+
+<!-- This gives you an opportunity to specifically state how the sample size will be
+determined. This could include a power analysis or an arbitrary constraint such as time, money, or personnel. If there is more than one hypothesis,
+choose the largest estimated sample size that is required from the power
+analyses corresponding to your hypotheses.
+ Example: We used MOREpower software (Campbell & Thompson, 2012) to
+calculate the necessary sample size to detect an effect size of 𝞰2 = 0.15 with alpha level set at .05 for the interaction term (H1) in the 2 x 2 mixed ANOVA design to obtain .90 power. The target effect size is taken from a recent study by X et al. (2016) and accounted for a potential effect size inflation by taking 75% of the original effect size.
+ More information: A wide range of possible answers is acceptable; remember that transparency is more important than principled justifications. If you state any reason for a sample size upfront, it is better than stating no reason and leaving the reader to “fill in the blanks.” Acceptable rationales include: a power analysis, an arbitrary number of subjects, or a number based on time or monetary constraints. For details, see Lakens (2022).-->
+
+Enter your response here
+
+## Rationale for number of trials
+
+<!-- Provide a justification for how the number of trials was determined. This could
+be a power analysis, time and resource constraints or based on previous
+experiments in the literature.
+ Example: Previous studies in the literature (e.g., X et al, 2016; X et al., 2012)
+have collected 600 trials per condition, and we have kept that consistent here.
+ More information: A wide range of answers is acceptable, including a
+justification based on a power analysis, time constraints, or pilot studies.-->
+
+Enter your response here
+
+## Stopping rule
+
+<!-- If your data collection procedures do not give you full control over your exact
+sample size, specify how you will decide when to terminate your data
+collection.
+ Example: If we have to exclude participants due to data quality issues
+(specify the criteria in the section “Data exclusion”), we will recruit additional participants to replace the excluded datasets. The minimum acceptable sample size within the scheduled data collection phase is 60 (30 per group). Due to budgetary constraints, we will terminate data collection after reaching a total of 100 participants.
+ More information: You may specify a stopping rule based on p-values only in the specific case of sequential analyses with pre-specified checkpoints, alpha levels, and stopping rules. Unacceptable rationales include stopping based on p-values if checkpoints and stopping rules are not specified.-->
+
+Enter your response here
+
+# Variables
+
+<!-- In this section you should describe all variables that will be measured during the experiment and will later be used in your analysis plan. In your analysis plan, you will have the opportunity to describe how each variable will be used. If you have variables which you are measuring for exploratory analyses, you are not required to list them, though you are encouraged to do so in the exploratory analysis section.-->
+
+## Manipulated variables*
+
+<!-- Describe all variables you plan to manipulate in your experimental paradigm.
+ Example:
+Between-subject variable:
+Variable musical training, with two levels: musicians and non-musicians. Within-subject variable:
+Variable pitch difference, with two levels: 4 Hz (small difference) and 8 Hz (large difference).
+ More information: For any experimental manipulation, you should give a precise definition of each manipulated variable. This must include a precise description of the levels at which each variable will be set, or a specific definition for each categorical treatment. For example, “loud” or “quiet,” should instead give either a precise decibel level or a means of recreating each level. 'Presence/absence' or 'positive/negative' is an acceptable description if the variable is precisely described.-->
+
+Enter your response here
+
+## Measured variables*
+
+<!-- Describe each variable that you will measure. This will include behavioral and EEG outcome measures, as well as any predictors or covariates that you will collect. In case of the EEG variables, please specify how you subselect your data in time and sensor space.
+ Example:
+EEG outcome measure:
+The EEG outcome variable will be the difference wave, i.e., the mean amplitude in response to the deviant minus the mean amplitude in response to the previous standard, in a time-window of 50-250 ms at electrodes Fz, FCz, and Cz. Time window and electrodes were chosen based on recommendations by X et al. (2014).
+Behavioral outcome measure:
+Behavioral outcome will be measured by calculating the hit rate. An accurate response is defined as a button-press during the response window: between 100 ms after the start of the oddball until the start of the following standard.
+ More information: As with the previous questions, the answers here must be precise. For example, 'MMN’ or ‘accuracy’ is too vague.
+ More information on channel selection: Non-exhaustive list of examples:
+* Exact a-priori ROI: "We will select electrodes FCz and Cz based on previous research (insert references to studies which use this combination of electrodes that were used to make the decision)"
+* Functional localizer: "In order to localize whether and where the MMN is reliably elicited, we will add a separate localizer task (discriminate between tones with a difference of 20 Hz), and select those electrodes that show a robust modulation to this difference."
+* We will consider a large candidate ROI over left motor cortex, including Cz, C1, C3, CPz, CP1, CP3, Pz, P1, P3, and select those electrodes that showed a robust modulation by left vs. right hand responses at p < .05, paired t-test"
+* Collapsed localizer: “We will select the 4 electrodes with the largest MMN between 50 and 250 ms on the grand average data”
+* No selection of channels necessary: e.g., mass-univariate, ICA/PCA, source-space, cluster-based permutation tests
+ More information on time windows: Non-exhaustive list of examples:
+* Specified a-priori: “We will select a time-window 100-200 ms following stimulus onset”
+* Adaptive mean approach: “We will locate the peak amplitude within 200 ms from stimulus onset and average amplitude values for the 100 ms surrounding that peak for each participant individually”-->
+
+Enter your response here
+
+## Indices
+
+<!--  If any measurements are going to be combined into an index (or even a mean), what measures will you use and how will they be combined? Include either a formula or a precise description of your method. If you are using a more complicated statistical method to combine measures (e.g., factor analysis, z-standardization within conditions/participants), you can note that here but describe the exact method in the analysis plan section. Also include the time window and region of interest over which the index will be computed.
+ Example: We will compute the mean amplitude based on the above specified time window (50-250 ms) and electrodes (Fz, FCz, Cz).-->
+
+Enter your response here
+
+# Acquisition
+<!--In this section you should describe the hardware you plan to use to collect data (e.g., computer screen, response box, EEG hardware).-->
+
+## Computer screen
+<!-- Type (e.g., LCD, CRT), resolution, refresh rate. Example: LED screen, 1920 x 1080 resolution, 59 Hz refresh rate.
+Distance between computer screen and participant, use of a chinrest or other constraints. Example: 1 m distance between screen and participant, no
+chinrest or constraints.-->
+
+Enter your response here
+
+## EEG hardware and acquisition settings*
+
+<!-- Amplifier: manufacturer, model. Example: EEG activity will be recorded using a BioSemi Active-Two system (BioSemi, Inc., Netherlands).
+ Electrode cap: manufacturer, model: Example: Brain Products (BrainVision) BrainCap.
+ Electrodes:
+ Do scalp electrodes have pre-amplifiers: Active (yes)/passive (no)
+ Number and location of electrodes, including reference and ground,
+but excluding EOG and other non-EEG electrodes. Example: There will be 35 electrodes in total including two mastoid electrodes and a ground electrode at AFz. The remaining 32 electrodes are: F7, F8, FC5, FC6, T7, T8, CP5, CP6, P7, P8, FP1, FP2, AF3, AF4, FC1, FC2, F3, F4, C3, C4, CP1, CP2, P3, P4, PO3, PO4, Oz, Fz, Cz, Pz).
+ Electrode material. Example: Ag/AgCl.
+ Conductive medium. Example: Gel/dry/saline/adhesive paste
+ Montage (e.g., standard 10‐5 system, custom). Example: we will use the standard 10-5 international electrode montage (Oostenveld & Praamstra, 2001).
+ Online reference and ground electrode placement. Example: The BioSemi ActiveTwo system has two electrodes, the common mode sense (CMS) active electrode and the driven right leg (DRL) passive electrode, which will be used as reference and ground electrodes, respectively.
+ Impedance level deemed acceptable for data collection or alternative data quality indicator for high input impedance amplifiers. Example: Electrode impedances on all recording sites will be kept below 5 kΩ at the beginning of data collection for all participants.
+ Online sampling rate and filter settings (if applicable). Example: EEG activity will be recorded at a 1024 Hz sampling rate with a 100 Hz low-pass filter and a 0.16 Hz high-pass filter.
+ Local power line frequency (usually 60 Hz in America and parts of Asia or 50 Hz in other parts of the world). Example: 50 Hz
+ Describe other equipment in addition to EEG in as much detail as appropriate (e.g., ECG/EMG/EOG/eye tracking). (optional)-->
+
+Enter your response here
+
+# Pre-processing
+
+<!--Please reorder these preprocessing steps to reflect the order you will apply them to your data. Please first read all questions in sections 22-30, which list a wide (but non-exhaustive) array of possible steps. Based on those suggestions, write an individualized pipeline about which steps you will take in which order. If you have already preprocessed the data before preregistering, you can still specify the steps you took, but please indicate that these steps have already been performed. For recommendations on the order of pre-processing steps, see Luck (2014).
+Please also clarify whether (some) preprocessing steps are common to all hypotheses or specific to only a sub-type of them. Furthermore, please specify whether different software is used at different stages of the preprocessing pipeline.-->
+
+## General Setup*
+
+<!-- List the order in which you will apply the preprocessing steps. Example:
+Resampling, re-referencing, offline-filtering, artifact correction/rejection,
+epoching, averaging, baseline correction
+ Which program/toolbox/package are you going to use? For example,
+FieldTrip, EEGLAB, Brainstorm, or ERPLAB in MATLAB, or MNE in Python. Which version number? Example: All EEG data preprocessing steps will be scripted and run in MATLAB (v. 2021b) and the FieldTrip toolbox using the version that will be the latest at the time pre-processing begins.
+Will you use a pre-existing/standardized preprocessing pipeline (e.g., PREP, BEAPP, HAPPE; remember to also report the version number) or develop your own? Example: We will not use any pre-existing preprocessing pipelines. The planned preprocessing steps are described below.-->
+
+Enter your response here
+
+## Data Import*
+
+<!-- What software will be used to record the EEG data? Example: EEG data will
+be recorded using BrainVision Recorder software (the latest available version
+at the start of data collection).
+ What file format will you use at recording? Are you planning to export the files
+to a different format for analyses (e.g., .edf, .cnt, .bnf)? Example: The data
+will be recorded in .eeg format and exported to .edf for analyses.
+ Will you import all recorded channels or a subset? Example: All recorded
+channels will be imported.-->
+
+Enter your response here
+
+## Resampling*
+
+<!-- Are you going to resample the continuous EEG signal? If so, to what
+sampling rate? Example: The EEG data will be downsampled to 200 Hz to decrease file size and computational time.-->
+
+Enter your response here
+
+## Re-Referencing*
+
+<!-- Are you going to re-reference your data?
+To what reference? Example: average reference, mean mastoids, Laplacian.
+ Which channels are you going to re-reference? Example: all EEG channels will be re-referenced.-->
+
+Enter your response here
+
+## Offline Filtering*
+
+<!-- Will filters be applied to continuous or epoched data?
+ How will data be filtered (e.g., high-pass, low-pass, band-pass)? Please
+report at least the following properties:
+filter type, e.g., FIR, IIR, Butterworth
+filter order, e.g., 5th order
+filter cut-off type and frequency, e.g., 40 Hz ( −3 dB half-amplitude)
+filter roll-off, e.g., 12 dB/oct
+Example: “[A] 5th order infinite impulse response (IIR) Butterworth filter [will be] used for low-pass filtering on the continuous (nonsegmented data), with a cut-off frequency (3 dB point) of 40 Hz and 12 dB/octave roll-off.” (Keil et al., 2014, p. 6).-->
+
+Enter your response here
+
+## Artifact rejection/correction*
+
+<!-- How are you going to identify noisy channels for subsequent interpolation
+(e.g., visually or using automated algorithms)? Example: EEG data will be inspected visually for flat channels which will be selected for interpolation. We will also use the FieldTrip semi-automatic ft_rejectvisual tool to identify noisy channels for interpolation.
+ Will you use an interpolation algorithm for those bad channels? If so, what kind of algorithm will you use (e.g., spherical spline)? Example: Selected channels will be interpolated from 4 neighboring electrodes using the spherical spline method based on 3D sensor locations.
+ Are you going to perform artifact rejection on continuous or epoched data? Example: Artifact rejection will be performed on continuous data.
+ Are you going to do artifact rejection automatically, semi-automatically, manually, or not at all? Example: Artifact rejection will be performed automatically.
+ If artifacts are rejected automatically or semi-automatically, what kind of artifact rejection algorithm will you use (e.g., z-value approach implemented in FieldTrip, detect abrupt spikes or flat activity based on kurtosis, reject epochs using spectral estimates)? What parameter values will you use for your algorithm? If artifacts are rejected manually, which criteria will you apply (e.g., only blinks and horizontal eye movements)?) Example: We will use the automatic artifact rejection algorithm implemented in Fieldtrip: we will reject from the analysis trials with a z-value > 3.
+ What kind of artifact correction will you perform? For example, independent component analysis like FASTica as implemented in EEGLAB or Artifact Subspace Reconstruction (Mullen et al., 2013)? What parameter values will you use for your algorithm? Will you correct all artifacts (blinks, saccades, alpha, muscle, etc) or only a subset? For example, in infant research you might want to reject muscle artifacts from the neck but correct eye movement artifacts. Example: We will use AMICA algorithm (Palmer et al., 2012) (calculated data rank with 'pcakeep' option) for independent component analysis (ICA). The components corresponding to eye movements, blinks, heart activity will be identified and rejected semi-automatically with ICLabel (Pion-Tonachini et al., 2019): if a component is classified as eye movements or heart activity with > 60% and < 30% brain activity (and confirmed visually), then it will be rejected.
+ More information: https://eeglab.org/tutorials/06_RejectArtifacts-->
+
+Enter your response here
+
+## Epoching and averaging*
+
+<!-- Are you going to epoch data? If so, what will the epoch length be? What part
+of the stimulus/response will epochs be time-locked to?
+ Will you include a time window before the event of interest as a baseline?
+How long will it be and how did you come to this decision?
+ How will these epochs be averaged to create ERPs? Include information
+about which stimuli and/or responses will be included in each average.
+ Example: Epochs will be created starting 400 ms before the onset of the standard and deviant tones, and will last for 2400 ms. Standard and the two kinds of deviant tones (small or large difference) will be averaged separately
+per participant.-->
+
+Enter your response here
+
+## Baseline correction*
+
+<!-- Will you apply baseline correction?
+ Over what time window? Example: 400 ms before the stimulus onset
+will be used for baseline correction.
+Will you calculate a separate baseline per trial, condition, block, participant? Example: the baseline will be extracted preceding stimulus onset for every trial
+What procedure will you use? Example: subtraction, division, covariate in statistical model
+ More information: If you are recording other types of data or analyses in addition to ERPs (e.g., eye-tracking data), describe relevant software and pre-processing in as much detail as appropriate.-->
+
+Enter your response here
+
+# Analysis Plan
+
+<!-- You may describe one or more planned analyses in this preregistration. An analysis plan must state up front which variables are predictors (independent) and which are the outcomes (dependent). Please remember that all analyses specified below must be reported in the final article, and any additional analyses must be reported in a separate section as data-driven analyses.-->
+
+## Statistical models*
+
+<!-- What statistical model will you use to test your hypotheses? Please include
+the type of model (e.g., ANOVA, cluster-based permutation test, linear mixed model, etc.) and the model specification (this includes each variable that will be included as predictors, outcomes, or covariates). Please specify any interactions, subgroup analyses, pairwise or complex contrasts, or follow-up tests from omnibus tests. Will you be using one- or two-tailed tests? If you are comparing multiple conditions or testing multiple hypotheses, how will you correct for multiple comparisons? Please also indicate which statistical model tests which hypothesis.
+ Example: To analyze the EEG data, we will use a 2 x 2 mixed ANOVA on MMN mean amplitude values. Based on our main hypothesis, we will only consider whether the pitch difference x musical training interaction is statistically significant. We will not consider the main effects and therefore will not correct for multiple testing for several comparisons within the ANOVA. If the interaction (in either or both ANOVAs) is statistically significant, we will run post-hoc t-tests for the given ANOVA.
+ More information: Please provide a specific recipe for analyzing the collected data. Ask yourself: did you provide enough detail so that someone else could run the same analysis again?-->
+
+Enter your response here
+
+## Transformations
+
+<!-- If you plan on transforming, centering, or recoding the data, please describe
+that process here. Transformations often are not necessary for ERP data, but
+other factors in your model may need recoding (see example below).
+ Example: We will compute the difference wave by subtracting the standard
+from the deviant waveform.
+ More information: If any categorical predictors are included in a regression,
+indicate how those variables will be coded (e.g., dummy coding, summation coding, etc.) and what the reference category will be.-->
+
+Enter your response here
+
+## Inference criteria*
+
+<!-- What criteria will you use to make inferences (e.g., p-values, Bayes factors,
+specific model fit indices)? Where appropriate, please also report the cut-off
+criterion (e.g., p < .05, BF10 > 10).
+ Example: We will use p <. 05 to determine statistical significance.-->
+
+Enter your response here
+
+## Data exclusion*
+
+<!-- How will you determine what data or samples, if any, to exclude from your
+analyses? How will outliers be handled? Will there be awareness checks? Is there a pre-specified minimum number of trials for participants to be retained in the final analysis? If so, how did you come to this decision (e.g., prior work indicates that 30 trials are required to elicit a reliable MMN)?
+ Example: Participants will be excluded if there are less than 40 deviant trials per condition left after artifact rejection or due to technical failure; this cutoff is based on prior work by X et al. (2014).-->
+
+Enter your response here
+
+## Missing data*
+
+<!-- How will you deal with incomplete or missing data?
+ Example: Since we exclude all the data of participants that have less than
+our prespecified number of trials in any condition, we will have an average MMN value for each participant for each condition. Missing deviants due to noise will not be imputed.
+ Example: In case of missing values in one or more conditions for a participant, we will employ Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm (Buuren & Groothuis-Oudshoorn, 2011), as implemented in the R package mice (Buuren & Groothuis-Oudshoorn, 2011).-->
+
+Enter your response here
+
+
+## Exploring your data
+
+<!-- You are obviously free to explore your data set to look for unexpected
+relationships. You may describe this procedure here. Describing this procedure here can serve as a reminder to meticulously log your analysis steps and decisions while performing your data exploration.
+ Example: We will explore relationships between age and handedness and MMN amplitude.-->
+
+Enter your response here
+
+# Other
+
+<!--If there is any additional information that you would like to include in your preregistration, please enter it here.-->
+
+Enter your response here
+
+
+## Supplementary materials (optional)
+<!-- Example: figures, templates/checklists such as eCOBIDAS (Pernet et al.,
+2020) or ARTEM-IS (Styles et al., 2021), preprocessing and analysis scripts
+(tested on pilot data)
+ Data Management Plan: a document that specifies how data will be
+organized, tracked, stored, and eventually shared following the FAIR guiding principles of Findability, Accessibility, Interoperability, and Reusability, to facilitate result reproducibility. Your institution and/or funding body may already require it and you can consider adding a copy here as well.-->
+
+Enter your response here
+
+# References
+## 
+\vspace{-2pc}
+\setlength{\parindent}{-0.5in}
+\setlength{\leftskip}{-1in}
+\setlength{\parskip}{8pt}
+\noindent
diff --git a/inst/rmarkdown/templates/exp_sampling_prereg/skeleton/skeleton.Rmd b/inst/rmarkdown/templates/exp_sampling_prereg/skeleton/skeleton.Rmd
new file mode 100644
index 0000000..d9f2229
--- /dev/null
+++ b/inst/rmarkdown/templates/exp_sampling_prereg/skeleton/skeleton.Rmd
@@ -0,0 +1,326 @@
+---
+title           : "Preregistration of Experience-Sampling Studies"
+shorttitle      : "My preregistration"
+date            : "`r Sys.setlocale('LC_TIME', 'C'); format(Sys.time(), '%d\\\\. %B %Y')`"
+
+author: 
+  - name        : First Author
+    affiliation : 1
+  - name        : Second Author
+    affiliation : "1,2"
+
+affiliation:
+  - id          : 1
+    institution : First institution
+  - id          : 2
+    institution : Second institution
+
+output: prereg::prereg_pdf
+---
+
+# Study Information
+
+
+## Description
+<!-- Please give a brief description of your study, including some background, the purpose of the study, or broad research questions. Example: Although previous self-report surveys have found an association between baking and positive mood, no studies have yet investigated whether or not baking may correlate with momentary changes in positive affect, measured indaily life.  
+ More information: The description should be no longer than the length of an abstract. It can give some context for the proposed study, but great detail is not needed here for your pre-registration.-->
+
+Enter your response here
+
+## Hypotheses*
+<!-- List specific, concise, and testable hypotheses. Please state if the hypotheses are directional or non-directional. If directional, state the direction. A predicted effect is also appropriate here. If a specific interaction or moderation is important to your research, you can list that as a separate hypothesis. Example: Participants who report engaging in baking at one measurement occasion will be more likely to report higher positive affect at the following measurement occasion.-->
+
+Enter your response here
+
+
+# Design Plan
+<!--In this section, you will be asked to describe the overall design of your study. Remember that this research plan is designed to register a single study, so if you have multiple experimental designs, please complete a separate preregistration. For large ESM datasets that will be used for multiple analyses by different people (and possibly different labs), for clarity, we recommend one registration per study/paper.-->
+
+
+## Study type*
+<!-- Observational study - The majority of ESM studies will be observational in nature,and will not involve participants being exposed to different experimental conditions. Participants may be recruited to target or control groups based on presence or absence of particular characteristics, e.g. mental health status.
+Other -->
+
+Enter your response here
+
+## Blinding*
+<!--Blinding describes who is aware of the intervention and control group allocations within a study. Mark all that apply.-->
+*No blinding is involved in this study.
+*Participants will not know the intervention group to which they have been assigned.
+*Personnel who interact directly with the participants will not be aware of the assigned intervention group. (Commonly known as “double blind”)
+*Personnel who analyze the data collected from the study are not aware ofthe intervention status of any given group.
+
+## additional blinding
+
+Enter your response here
+
+## Study design*
+<!-- Describe your study design. Examples include between- or within-participants designs, N=1 single-case or multi-observer designs. Typical study designs for observation studies include cohort, cross sectional, and case-control studies.Example: We have a cohort design.-->
+
+Enter your response here
+
+
+# Sampling Plan
+<!--In this section we’ll ask you to describe how you plan to collect samples, as well as the number of samples you plan to collect and your rationale for this decision. Please keep in mind that the data described in this section should be the actual data used for analysis, so if you are using a subset of a larger dataset, please describe the subset that will actually be used in your study-->
+
+## Existing data*
+<!-- Preregistration is designed to make clear the distinction between confirmatory tests, specified prior to seeing the data, and exploratory analyses conducted afterobserving the data. Therefore, creating a research plan in which existing data will be used presents unique challenges. Please select the description that best describes your situation. Please do not hesitate to contact us if you have questions about how to answer this question (prereg@cos.io)-->
+*Registration prior to creation of data: As of the date of submission of this research plan for pre-registration, the data have not yet been collected, created, or realized. 
+*Registration prior to any human observation of the data: As of the date of submission, the data exist but have not yet been quantified, constructed, observed, or reported by anyone - including individuals that are not associated with the proposed study. Examples include passively collecteddata from smartphones or wearables, e.g. location data, time spent using a particular app, and are inaccessible.
+*Registration prior to accessing the data: As of the date of submission, the data exist, but have not been accessed by you or your collaborators. Commonly, this includes data that has been collected by another researcher or institution.
+*Registration prior to analysis of the data: As of the date of submission, thedata exist and you have accessed it, though no analysis has been conducted related to the research plan (including calculation of summary statistics). A common situation for this scenario when a large dataset exists that is used for many different studies over time, or when a data setis randomly split into a sample for exploratory analyses, and the other section of data is reserved for later confirmatory data analysis.
+*Registration following analysis of the data: As of the date of submission, you have accessed and analyzed some of the data relevant to the research plan. This includes preliminary analysis of variables, calculation of descriptive statistics, and observation of data distributions. Please see cos.io/prereg for more information.
+
+
+
+## Explanation of existing data 
+<!-- Name and briefly describe the data set(s), and if applicable, the subset(s) of the data you plan to use.Example: The current study will use a subset of variables (see variables section for names) from Wave I of the RE-AL cohort study, using data from all participants within the dataset (N=220).-->
+
+### Availablity
+<!--Specify whether this data is open or publicly available.Example: Data are not publicly available.-->
+
+Enter your response here
+
+### Access of data
+<!--How can the data be accessed? Provide a persistent identifier or link if the data are available online, or give a description of how you obtained the dataset.Example: Data will be obtained by application to the lead researcher of the RE-AL study (Cook, E. M. M. M.; e.cook@uchoc.be). Our application has already been approved (10th January 2020).-->
+
+Enter your response here
+
+### date of download
+<!--Specify the date of download and/or access.Example: Data have not yet been accessed or downloaded. Once this post-registration has been made on the Open Science Framework, data will be released to YUM with a time and date-stamped receipt.-->
+
+Enter your response 
+
+### documentation of data collection
+<!--If the data collection procedure is well documented, provide a link to that information. If the data collection procedure is not well documented, describe, to the best of your ability, how data were collected.Example: The protocol of the RE-AL cohort study is published: Cook, E. M. M. M., Fondu. E., & Biccies, T. (2014). Protocol and sample for the RE-AL study: Anadolescent experience sampling cohort study to investigate nutrition and mental health. Nutrition Methods, 11(3), 157-163.-->
+
+Enter your response here
+
+### codebooks
+<!--Some studies offer codebooks to describe their data. If such a codebook is publicly available, link to it here or upload the document. If not, provide other available documentation. Also provide guidance on what parts of the codebook or other documentation are most relevant.Example: The codebook for Wave I of the RE-AL study is publicly available online at (URL for codebook).-->
+
+Enter your response here
+
+
+## ESM data collection procedure*
+<!--Please specify the ESM data collection procedure in as much detail as possible, indicating your decisions for the options below. Note that this pre-registration template is mainly intended for studies that involve multiple self-report measures, where participants are assessed at least once per day in the context of their daily life. As there is a large variation in the methodology of these studies, not every option below may be applicable and may therefore be indicated as such.-->
+
+### study duration 
+<!--Study duration (number of days)Example: The ESM period lasts for 6 days. If participants have filled out less than 30% of all beeps after this period, the ESM period is extended by 2 days-->
+
+Enter your response here
+
+### Type of sampling scheme
+
+Enter your response here
+
+### Details of the sampling scheme
+<!--Details of the sampling scheme may be included here, including the type of scheme, the number of beeps, sampling rate, timing of beeps, and potential minimum windows between consecutive beeps. -->
+
+Enter your response here
+
+### Type of sample scheme
+<!--The sample scheme can be one of the following: Interval-contingent, signal-contingent (with fixed or random intervals), event-contingent (prompted or participant-initiated), or mixed.Example: We use a mixed sampling scheme, with both semi-random andfixed elements. Participants receive 10 notifications per day, between 7.30 AM and 10.30 PM on weekdays, and between 9.00 AM and 0.00 PMon weekend days. Each notification is randomly scheduled within one of ten 90-minute blocks throughout the day (i.e., notification 1 between 7.30 AM and 9.00 AM; notification 2 between 9.01 AM and 10.30 AM, etc.). A window is scheduled between notifications, so that there are at least 15 minutes between consecutive notifications. In addition, a morning questionnaire is continuously available to participants between 7.00 AM and 10.00 AM each day to fill out a morning questionnaire.Example: We use an event-contingent sampling scheme. Participants areasked to fill out the questionnaire following each social interaction lasting ver 15 minutes. A maximum of 12 social interactions per day can be recorded. (Alternatively: Participants are prompted with the questionnaire on their mobile phone after it has made contact via Bluetooth with their dyad partner. Participants are not prompted if they have already filled out a questionnaire in the last two hours)-->
+
+Enter your response here
+
+### Items
+<!--Total number and type of items (open-ended or closed) and estimated questionnaire duration. Indicate the total number of items per assessment, also when items are not used for the analyses specified in this pre-registration. A full list of items should be provided in an appendix.Example: The ESM questionnaire consists of a minimum of 30 and a maximum of 45 multiple choice items, and two open-ended items. Questionnaire length varies as a function of certain item responses (e.g., some items are only presented after participants indicate that they are alone). Following a pilot, we estimate the completion time for each questionnaire to range from two to four minutes.-->
+
+Enter your response here
+
+### time-out specifications
+<!--Time-out specifications. Here you can indicate details about the amount of time that participants have to respond to a questionnaire (i.e., response time), and thepotential maximum amount of time that participants have to respond to one item, or complete the full questionnaire.Example: Following the initial notification, the ESM questionnaire is available to the participant for 15 minutes. Once the questionnaire has been opened, it will time out and become unavailable if participants have not completed any item within two minutes, or after they have been unable to complete the full questionnaire within 15 minutes.-->
+
+Enter your response here
+
+### additional passive monitoring
+<!-- Additional passive monitoring.Example: In addition to completing the ESM, participants’ heart rate is also measured throughout the ESM week, using the Fitbit Charge 2.-->
+
+Enter your response here
+
+### Incentivization
+<!--Incentivization and increasing participant engagement. Please provide a description of the incentive for participation, and of all efforts to increase participant engagement/compliance, such as participation reminders viatelephone or text, participation fees that are conditional on the compliance rate, or potential gamification of the ESM. Please also indicate whether participants have access to information about their compliance rate during the ESM period. Example: Participants are informed that they receive €10 if they have filled out atleast 20 beeps throughout the ESM week, with an additional €0.50 per filled out beep. Participants are also informed that they will receive no fee if they have filled out less than 20 beeps. Participants will not have access to their compliance information during the testing period and will not receive reminders tocomplete notifications. -->
+
+Enter your response here
+
+### additional details
+<!-- Please indicate any additional details that are relevant to the ESM data collectionprocedure-->
+
+Enter your response here
+
+## Sample size*
+<!-- Describe the sample size of your study. If individuals are clustered in higher-order units, e.g. schools or groups according to diagnosis, then describe the size required for each unit.Example: We will include a total of 2000 participants in our sample, 1000 12 year-olds, 500 14 year-olds, and 500 16 year-olds.-->
+
+Enter your response here
+
+## Rationale for sample size
+<!--Please provide a rationale justifying your decisions regarding the sample size. This includes the temporal design (number of days and number of measurement occasions per day) and the number of participants included within the study. For example, the temporal design can be selected taking into consideration protocolsbased on previous research that studied a similar target process. The rationale for selecting the number of participants can include a power analysis. Arbitrary constraints such as time, money and personnel should also be explicitly reported.When considering sample size, researchers should take into account the expected compliance rate, as this can significantly affect power and the quality ofthe data.  Example: We estimated power using Monte Carlo based simulation for a two-level model assuming an ESM protocol of 6 days with 10 measurement occasions per day. Our goal was to obtain .95 power to test a significant effect for the main hypothesis of interest (i.e., where the null hypothesis is that the maineffect of interest is zero, assuming a significance level of 0.05). -->
+
+Enter your response here
+
+### more information
+<!-- More information: This gives you an opportunity to state specifically how the sample size will be determined. A range of reasons can be stated; remember thattransparency is key here. If you state any reason for a sample size upfront, it is better than stating no reason and leaving the reader to “fill in the blanks.” An acceptable rationale would include a power analysis, an arbitrary number of subjects, or a number based on previous studies, time or monetary constraints-->
+
+Enter your response here
+
+## Stopping rule
+<!--If your data collection procedures do not give you full control over your exact sample size, specify how you will decide when to terminate your data collection. This may refer to the termination of additional participant inclusion as well as terminating data collection of ESM data for individual participants.
+Example: Every Friday evening, we will post participant sign-up slots for the following week. There will be 20 testing slots available per week. We will post 20 new slots each week if we are below 320 participants on the Friday preceding a new testing week.
+More information: You may specify a stopping rule based on p-values only in the specific case of sequential analyses with pre-specified checkpoints, alphas levels, and stopping rules. Unacceptable rationales include stopping based on p-values if checkpoints and stopping rules are not specified. If you have control over your sample size, then including a stopping rule is not necessary, though it must be clear in this question or a previous question how an exact sample size is attained.-->
+
+Enter your response here
+
+# Variables
+<!--In this section, you can describe all variables (both ESM and non-ESM variables and manipulated and measured variables) that will later be used in your confirmatory analysis plan. In your analysis plan, you will have the opportunity to describe how each variable will be used. Ifyou have variables that you are measuring for exploratory analyses, you are not required to list them, though you are permitted to do so.-->
+
+## Measured non-ESM/time invariantvariables 
+<!--Describe each variable that you will measure, which is not measured during the ESM period. This will include outcome measures, as well as any predictors or covariates that you will measure outside of the ESM period. You do not need to include any non-ESM variables that you plan on collecting if they are not going tobe included in the confirmatory analyses of this study.Example: The first baseline predictor variable will be age in years, reported by participants at baseline. The second baseline predictor variable will be educational level, reported by participants as years of formal education.
+More information: Observational studies will include only measured variables. As with the previous questions, the answers here must be precise. For example, 'intelligence,' 'extraversion,' and 'aggression' are too vague. Acceptable alternatives could be 'IQ as measured by Wechsler Adult Intelligence Scale' 'number of close friends,' and 'number of threat displays'-->
+
+Enter your response here
+
+## Measured ESM/time-variant variables*
+<!-- Describe each variable that is measured during the ESM period. This will include outcome measures, as well as any predictors or covariates that you will measure during ESM, or that were measured during the ESM period (in the case of pre-existing data). If the ESM questionnaire includes items other than those used in the current pre-registration, provide an appendix with the full ESM questionnaire. Specify the level at which each variable is measured and describe the response scale (Visual Analogue Scale, Likert scale, categorical, or other, in which case, specify). Also, indicate whether items were presented in a randomized or fixed order.  Examples: The ESM predictor variable will be stress. We will measure this at themomentary level throughout the day by asking participants to indicate their agreement with the statement ‘I feel stressed’ (on a scale of 1-7, 1 being ‘not at all’, 7 being ‘very much’). The first ESM outcome variable will be engagement. We will measure this at the momentary level by first asking participants ‘What areyou doing right now?’ (an open-ended question with a maximum of 150 characters’). We will then measure engagement at the momentary level by asking participants ‘How engaged are you in this activity?’ (on a scale of 1-7, 1 being ‘not at all’, 7 being ‘very much’).
+More information:Indicate the order and, if applicable, the branching in the ESM questionnaire. For example, ‘if yes to question 1. “Since the last notification, I have consumed alcohol”, question 2. will follow: “Who were you with at the time of alcohol consumption? Partner; Family; Friends; Colleagues; Acquaintances; Unknown people / Others; No one”. -->
+
+Enter your response here
+
+## Open-ended questions 
+<!--Describe any open-ended questions that will be coded and used in confirmatory analyses. How much text can be entered? How will coding be done? Specify the level of each open-ended question (e.g. each measurement occasion, once a day, or at the end of the ESM period). This is not applicable to questionnaires including only closed questions. Example: At each measurement occasion, participants will be asked an open-ended question “What were you doing at the moment before the notification arrived?” Participants can respond with a maximum of 150 characters. Answers will be coded into two categories: work and non-work. Answers will be coded as work if they include a minimum of one word from the “work” category in Fictional Language Processing Software.-->
+
+Enter your response here
+
+## Indices
+<!--If any measurements collected during the ESM period or outside of it are going tobe combined into an index (or even a mean), what measures will you use and how will they be combined? Include either a formula or a precise description of your method.  For any indices or summary statistics used in confirmatory analyses, specify the level at which the statistic is constructed (such as per person, day, or measurement occasion). This may apply to both questionnaire measurements and measurements collected via passive monitoring. If you are using a more complicated statistical method to combine measures (e.g. a factor analysis), you can note that here but describe the exact method in the analysis plan section.Example: We will use the momentary-level within-person beta coefficients of the association between stress (“How unpleasant was the most important event that happened to you since the last beep?”, 0 = not at all, 7 = very much) and negative affect (mean of items “I feel down”, “I feel anxious”, and “I feel insecure”,0 = not at all, 7 = very much) as an index of stress reactivity. 
+More information: If you are using multiple pieces of data to construct a single variable, how will this occur? Both the data that are included and the formula or weights for each measure must be specified. Standard summary statistics, such as “means” do not require a formula, though more complicated indices require either the exact formula or, if it is an established index in the field, the index mustbe unambiguously defined. For example, “positive affect” is too broad, whereas “daily mean of positive affect items: “I feel relaxed”, “I feel content”, and “I feel cheerful” each measured on a 1-7 scale (1 = not at all, 7 = very much)” is appropriate.-->
+
+Enter your resonse here
+
+## Manipulated variables
+<!-- Describe all ESM and non-ESM variables that you plan to manipulate and the conditions or treatment arms of each variable. Specify the level (/timing) of manipulation of each variable. This is not applicable to any observational study. Example: Participants will walk for 30 minutes once a day. We will assign participants to two daily walking categories for the duration of the ESM period. The categories of this variable are indoors and outdoors.Example: We will assign participants to two notification conditions for the duration of the ESM period. Participants will receive either 10 notifications per day or 6 notifications per day.
+More information: For any experimental manipulation or treatment, you should give a precise definition of each manipulated variable. This must include a precise description of the values at which each variable will be set, or a specific definition for each categorical treatment. For example, “loud or quiet,” should instead give either a precise decibel level or a means of recreating each level. 'Presence/absence' or 'positive/negative' is an acceptable description if the variable is precisely described.-->
+
+Enter your response here
+
+# Prior knowledge of data 
+<!-- List the publications, conference presentations (papers, posters), and working papers (inprep, unpublished, preprints) you have worked on that are based on the data set.Describe which variables you have previously analyzed and which information you used in these analyses. Limit yourself to variables that are relevant to the current study. If the dataset is longitudinal, include information about which wave(s) of data were previously analyzed. Also, include any knowledge regarding missingness within the dataset or compliance, including at what level e.g. overall compliance, compliance for different types of reports, the mean level of compliance, range of compliance across participants.Example: YUM has previously used the RE-AL Wave I dataset to investigate the relationship between positive affect (variables: mood_cheerful, mood_relaxed and mood_satisfied) and frequency of snacking (variable: eat_snack). Both of these variables will be used in the current study. YUM’s previous analyses using these variables were pre-registered (URL to pre-reg). They were published as: Munch, Y.U., Cook. E. &, Monster, M. M. M. (2016). Happy snacking: A real-time monitoring study of positive affect and snacking using the RE-AL cohort. Journal of Snack Studies, 21(3), 244-252, and were also presented by YUM as an oral presentation at the International Association of Nutrition conference, New York, 22-26th September, 2014 (link to abstract; link to slides). All of the authors of the current study are aware of these findings. 17 participants had more than 80% missing data due to a technical issue and these participants were excluded from Munch and Cook’s (2016) study because of this. Overall compliance was 83%.
+More information: It is important to specify all the different ways you have previously used the dataset because this will provide information about how you have been exposed to the data previously. If available, include references to any relevant publications or pre-prints. This information helps you to establish any previous knowledge of the data you may have, which is relevant for the next question.-->
+
+Enter your response here
+
+## prior knowledge
+<!--More information: Are you aware of summary statistics or the statistical distribution of variables, or do you know about correlations between variables? Your prior knowledge could stem from working with the data first-hand, from reading previously published research, or from codebooks. Also provide any relevant knowledge of different subsets of the data (e.g., a different wave than you will be using). Finally, provide information about your prior knowledge for every author separately.Example: Apart from YUM, none of the other co-authors has had prior access to any of the variables within the RE-AL dataset. All authors of the current study are aware of Munch, Cook & Monster’s (2016) study on positive affect and snacking, therefore all authors are aware that the ICC for this relationship (positive affect ~ snacking + age + gender + (SubjID| 1) is .36-->
+
+Enter your response here
+
+
+# Analysis Plan
+<!--You may describe one or more confirmatory analyses in this pre-registration. Please remember that all analyses specified below must be reported in the final article, and any additional analyses must be noted as exploratory or hypothesis-generating.A confirmatory analysis plan must state upfront which variables are predictors (independent) and which are the outcomes (dependent), otherwise, it is an exploratory analysis. You may describe any exploratory work here, but a clear confirmatory analysis is required.-->
+
+## Statistical models*
+
+### type of statistical model
+<!--What statistical model will you use to test each hypothesis? Please include the type of model (e.g. mixed effect models, repeated measurement ANOVA, SEM, etc.) and the specification of the model (this includes each variable that will be included as time-varying and time-invariant predictors and outcomes). 20.2.If a multilevel analysis is applied, a description of the following elements can be included:-->
+
+Enter your response here
+
+
+### Distribution of the outcome variable
+<!--Example: the outcome variable is a binary variable. Therefore, we will use a logistic mixed-model-->
+
+Enter your response here
+
+
+### Distribution of the within-subject errors
+<!--Example: the errors are assumedto be Gaussian distributed and serially correlated. The serial correlation will be modeled using an AR(1) process.-->
+
+Enter your response 
+
+
+### Distribution of the random effects
+<!--Example: the model will include a random intercept and a random slope, and will be assumed to be bivariate Gaussian distributed with an uncorrelated correlation structure.-->
+
+Enter your response here 
+
+
+### Fixed-effect predictors
+<!--please specify which predictors are considered fixed and which are considered random. Example: the model includes the variable momentary stress that is assumed to be fixed (does not include a random slope).-->
+
+Enter your response here 
+
+
+### Transformations
+<!--Transformations applied to time-varying explanatory variables and time-invariant explanatory variables. Example: the time-varying variable is centered using the persons' mean. The time-invariant variable is centeredusing the grand mean.-->
+
+Enter your response here
+
+
+### inclusion of lag-dependent variables
+<!--Inclusion of lag-dependent variables. Example: the model includes the lagged predictor. The variable has been lagged within days and within participants.-->
+
+Enter your response here 
+
+### more information
+more information: please specify any interactions, subgroup analyses, pairwise or complex contrasts, or follow-up tests from omnibus tests. Remember that any test not included here must be noted as an exploratory test in your final article. 
+more information: This is perhaps the most important and most complicated question within the pre-registration. As with all of the other questions, the key is to provide a specific recipe for analyzing the collected data. Ask yourself, is enough detail provided to run the same analysis again? Be aware of instances where the statistical models appear specific, but actually leave openings for analytic flexibility. See the following examples: Example: If someone specifies a multilevel model with three time-varying predictors (X1, X2 and X3) and we are interested in estimating the effect of predictor X1, there is still flexibility over the specification. For example, does the model include a random slope for each of the three predictors? Does the model only consider a random slope for predictor X1? The two models produce differentestimates of the main effect of predictor X1. Example: The data has a three-level structure: repeated measurements (level 1)nested within days (level 2) and nested within an individual (level 3). If someone specifies a multilevel model with a random slope, there is still flexibility over the specification of the random effects: for example, the model includes a random intercept and a random slope that are allowed to vary within days and within individuals. Or the model includes a random intercept that is allowed to vary within days and within individuals and a random slope that is allowed to vary within individuals). These specifications imply different statistical models.-->
+
+Enter your response here
+
+## Analytic problem
+<!--
+What will you do should your data violate assumptions, your model not converge or some other analytic problem arises?
+Example: We will specify a linear-mixed effect model to estimate if there are group differences in Depression between participants diagnosed with major depressive disorder and control subjects. The dependent variable is Depressed and the predictors are the following lagged variables: Depressed, Angry, Anxious, Happy and Relaxed. To select the structure of the random effect we willsplit the dataset into training and test data to conduct adequate post-selection inference. We will use a ratio of 40/60 (i.e. training/test set). Using the training set, we will estimate a linear mixed-effect model using maximum likelihood, and we will compare three random effect structures: (1) all predictors have a fixed slope; (2) all predictors have a random slope and correlation structure is unrestricted; and (3) all predictors have uncorrelated random slopes. The model will be selected by comparing the AIC. We will fit the model with the minimum AIC using the test dataset. 
+More information: It is, of course, impossible to predict every single way that things might go awry during the analysis. One of the variables may have a strange and unexpected distribution, one of the models may not converge because of a quirk of the correlational structure, and you may even encounter error messages that you have never seen before. However, as with the questionsabove, you can use your prior knowledge of the dataset to set up a contingency plan for transforming a variable if the data do not behave precisely as you expect. (e.g., “The dependent variable is the number of negative events during the ESM period. We assumed that the distribution is Poisson. However, if the variable exhibits a high number of zeros, we will fit a zero-inflated Poisson model””). Which transformations to perform depends on the context. In some cases, it may be reasonable to use the logarithmic transformation for the dependent variable, however, you should verify that residuals in the fitted model are independent and normally distributed. The most important thing is to be clear and transparent about this process in the final write-up so that you can reasonably assure evaluators that transformations were not applied inappropriately or arbitrarily.-->
+
+Enter your response here
+
+## Transformations 
+<!--If you plan on transforming, centering, recoding the data, or will require a coding scheme for categorical variables, please describe that process.Example: If you estimate a model that includes a lag-dependent variable as a predictor, then there should be information on how the overnight or missed beepsare going to be treated (e.g. for each day the first beep of the day will be set as a missing observation).Example: The time-varying predictor will be centered using the individual means and the time-invariant predictor will be centered using the grand mean.
+More information: If any categorical predictors are included in a regression, indicate how those variables will be coded (e.g. dummy coding, summation coding, etc.) and what the reference category will be.-->
+
+Enter your response here
+
+## Inference criteria 
+<!--What criteria will you use to make inferences? Please describe the information you will use (e.g. p-values, Bayes factors, specific model fit indices), as well as the cut-off criterion, where appropriate. Will you be using one or two-tailed tests for each of your analyses? If you are comparing multiple conditions or testing multiple hypotheses, will you account for this?Example: We will use a Likelihood ratio test to test the difference between two nested models using the Chi-squared distribution. If the p-value of the likelihood ratio test is inconclusive we will use parametric bootstrap. The parametric bootstrap is a more efficient test. 
+More information:P-values, confidence intervals, and effect sizes are standard means for making an inference, and any level is acceptable, though some criteriamust be specified in this or previous fields. Bayesian analyses should specify a Bayes factor or a credible interval. If you are selecting models, then how will you determine the relative quality of each? In regards to multiple comparisons, this is a question with few “wrong” answers. In other words, transparency is more important than any specific method of controlling the false discovery rate or false error rate. One may state an intention to report all tests conducted or one may conduct a specific correction procedure; either strategy is acceptable.-->
+
+Enter your response here
+
+## Missing data 
+<!--Expectations of missingness. This is a common feature of ESM studies, for example, a participant can drop out and all their data can be missing, participantscan miss entire notifications resulting in missing (notification-level) observations, or a participant can have partially completed questionnaires resulting in (item-level) missingness. Provide a justification for your expectations.Example: We expect fewer than 5 participants to drop out, approximately 20% ofnotifications to be missing within the non-dropouts, and approximately 90% of thenotifications to be completed within the non-dropouts. These expectations are based on an ESM study with a similar design and population (Voorbeeld et al., 2019).
+More information: You may base your expectations on e.g., pilot data or existing literature-->
+
+Enter your respone here
+
+## Data exclusion 
+<!--Irrespective of whether data are pre-existing, will participants or observations be excluded based upon missingness, due to either technical issues or compliance?If so, specify a decision rule for this including:
+How will you define a valid observation? Example: All observations completed within 1-10 minutes will be defined as valid.
+How will you define compliance? E.g., Will notifications missing due to technical problems be counted as non-compliance? Will you use information regarding valid responses to determine compliance? In case you set a minimum compliance rate or threshold for each participant, please specify the value or range that you will use in the analysis and provide a justification for this.Example: Compliance will be defined as the number of valid notifications where at least 20 questions were answered, divided by the total number of delivered notifications. If a participant experienced technical issues, compliance will be calculated based on full days when technical issues were not present.
+Which criteria will you use to define an outlier? How will outliers be handled? How are you going to deal with careless responding?Example: We define outliers as a score of ± 3 SDs from the mean on the baseline depression variable and exclude participants with these scores from the analyses. We define careless responding during the ESM period as completion of the full questionnaire in less than 1 minute. We will exclude all of the ESM data from participants who display careless responding 3 times in a row-->
+
+Enter your response here 
+
+## Exploratory analysis
+<!--If you plan to explore your data set to look for unexpected differences or relationships, you may describe those tests here. An exploratory test is any test where a prediction is not made upfront, or there are multiple possible tests that you are going to use. A statistically significant finding in an exploratory test is a great way to form a new confirmatory hypothesis, which could be registered at a later time. Example: We expect that symptoms measured at the baseline are highly correlated (depressed mood, loss of interest, weight problems, fatigue, psychomotor disturbance). We are going to conduct a factor analysis to study thevariability of the set of items.-->
+
+Enter your response here
+
+
+# Other
+
+## Other 
+<!--Which software is used to estimate the statistical model(s)? Specify whether the default options or specific options of functions are used.
+If there is any additional information that you feel needs to be included in your (pre-)registration, please enter it here, e.g. literature cited within your (pre-)registration.
+Include an analysis script if possible. This file can be attached to an “open ended pre-registration” on the OSF-->
+
+Enter your response here
+
+
+# References
+## 
+\vspace{-2pc}
+\setlength{\parindent}{-0.5in}
+\setlength{\leftskip}{-1in}
+\setlength{\parskip}{8pt}
+\noindent
diff --git a/inst/rmarkdown/templates/multi_analyses_prereg/skeleton/skeleton.Rmd b/inst/rmarkdown/templates/multi_analyses_prereg/skeleton/skeleton.Rmd
new file mode 100644
index 0000000..98cec97
--- /dev/null
+++ b/inst/rmarkdown/templates/multi_analyses_prereg/skeleton/skeleton.Rmd
@@ -0,0 +1,170 @@
+---
+title           : "Preregistration of Multiverse Analyses"
+shorttitle      : "My preregistration"
+date            : "`r Sys.setlocale('LC_TIME', 'C'); format(Sys.time(), '%d\\\\. %B %Y')`"
+
+author: 
+  - name        : First Author
+    affiliation : 1
+  - name        : Second Author
+    affiliation : "1,2"
+
+affiliation:
+  - id          : 1
+    institution : First institution
+  - id          : 2
+    institution : Second institution
+
+output: prereg::prereg_pdf
+---
+
+
+
+# Preregistration guide
+<!--Note that developing a specification curve analysis / multiverse analysis (SCA/MVA) will likely require an extensive review of existing literature to see how reasonable researchers may differently characterized the associations of interest. If you are developing this analysis after a study has been designed and data collected, it may be the case that your particular data set does not instantiate all of the possible reasonable specifications. To optimize transparency, it can be useful in the registration and in subsequent analyses and reports to be able to speak to the portions of the multiverse not covered.
+
+The steps below may not be fully linear! In other words, if you start at the top and work your way down, you may find yourself revisiting previous steps. This is okay and good.
+
+Also note that, for brevity, this guide does not outline the description of the sample, data collection, instruments, etc.-->
+
+## Research question (construct-level)
+<!--This is where you can specify your research question, e.g., “What is the association between screen-time and adolescent mental health?”
+
+What is the association or associations of primary interest?
+What construct is the independent variable, cause, or treatment?
+What construct is the dependent variable, effect, or outcome?
+This is a crucial step, as it will strongly determine the criterion type you choose to compare across specifications (see sec. 5). You may also need to clarify this after considering some certain decision points and discovering that they represent comparisons of interest rather than specifications against which we test robustness (see sec. 3.1).-->
+
+Enter your response here
+
+
+# Decisions
+
+## Covariates
+<!--
+1. What are other constructs that are involved in, or somehow affect, the associations of interest?
+* These could be 
+-mediators, 
+-moderators, 
+-confounds, or 
+-constructs 
+that impact the effects, even if they don’t impact the causes
+* These other constructs may be from competing causal models—we’ll tackle this below.
+2. List these…-->
+
+Enter your response here
+
+## Causal model(s)
+<!--The variables you’ve listed above may play different roles in different causal models.
+
+1. What is the causal flow between the causes and the effects? (Cinelli, Forney, and Pearl 2020)
+2. Are there mediators involved, i.e., does the cause impact the effect in part via its effect on an intermediate construct?
+3. Are there confounders involved? Is there a construct that causes both the cause and effect of interest?
+4. Are there moderators of the cause’s impact on the effect, i.e., are there constructs that change how much the cause impacts the effect?
+5. If you (and theory, and the literature) are uncertain about what role a construct plays, it is likely there are competing causal models.
+6. Write out all reasonable, plausible, causal models. It may be helpful to create a diagram in a graphical program (InkScape, LucidChart, PowerPoint, Adobe Illustrator) or using the ggdag package in R.-->
+
+Enter your response here
+
+## Measurements of constructs
+<!--
+1. Are the constructs identified above measured in your data set? If you’re designing a new study, how will these constructs be measured?
+2. Are there multiple measurement instruments a reasonable researcher could use? Have you already collected measurements of constructs using multiple different instruments?
+3. List these…-->
+
+Enter your response here
+
+## Processing of measurements
+<!--
+1. Is it possible to score your instruments in meaningfully different ways?
+2. Is it possible to derive different metrics of the same construct from your behavioral data?
+3. Are there different preprocessing steps that people could apply to behavioral data?
+4. List these…-->
+
+Enter your response here
+
+## Sample composition
+<!--Are there different ways to construct the sample?
+
+1. Inclusion and exclusion criteria?
+2. Data quality cutoffs?
+3. List these…-->
+
+Enter your response here
+
+## Other decisions
+<!--It’s possible the above set of headings is not exaustive. Include other decisions as well.-->
+
+Enter your response here
+
+
+# Decision types
+<!--Here, we will use the framework of Giudice and Gangestad (2021) to group these various decision points into three types. What we do next, depends on this categorization,
+The three types of analytic decisions are
+1. Equivalent (Type E): the decision options are expected to be (evidently and conceptually), arbitrary and effectively the same.
+    * Example: two equally valid measures of depression.
+    * Importantly, what constitutes a decision between equivalent options is often best derived from what is left over after considering the next two categories.
+2. Non-equivalent (Type N): the decision options are not equally good choices, or they differ in important conceptual respects.
+    * Measurement non-equivalence: primarily validity and reliability differences.
+    * Power/precision non-equivalence: differences that effect estimation precision, e.g., exclusion criteria, and inclusion of certain types of covariates.
+    * Effect non-equivalence: differences in the effect being measured, including differences in the implied causal model, and interaction effects.
+3. Uncertain-equivalence (Type U): the decision options are not clearly non-equivalent, but there is not enough evidential or conceptual clarity to say that they are or should be equivalent.-->
+
+
+## Statistical comparison of Type U decisions
+<!--Some decision points invite comparisons between competing options that may be evaluated using existing statistical tools. In our preregistration example, we were interested in how pubertal development relates to neural activation during self-referential processing in the “social brain”. We started out identifying choice of region of interest in the social brain as a potential Type U decision. Does it matter whether we use data from ventro-medial prefrontal cortext (vmPFC) or posterior cingulate cortex (PCC)? Instead of examining several different multiverses for each ROI to examine the robustness of the effect size or significance of our association of interest, we could test this question in a model-based way. This becomes an overarching research question upon which our primary question depends, and we can examine the robustness of the answer to this question across our multiverse of decisions.
+1. Is it possible to adjudicate between options in some Type U decisions using model comparison or other common statistical tools?
+2. List these Type U decisions.
+3. Consider amending sec.1 in light of this new information.-->
+
+Enter your response here
+
+## Organizing the multiverse
+<!--Using the above categories, Giudice and Gangestad (2021) suggest the following approach:
+1. Type N: only the best option from non-equivalent decisions should be kept, in which case these decisions will not expand the set of analyses in the multiverse. In cases where the best option is not known (e.g., the correct causal model), it might make sense to examine the criterion (see below) for each option across separate multiverse analyses.
+2. Type U: each option should be used to generate a separate multiverse.
+3. Type E: the different possible combinations of options for these decisions will create the set of analayses for each multiverse.
+Using the above organizational strategy, describe, your mutliverse. To get a sense of the size of the set of the analyses in the multiverse, multiply the number of options for each decision together. If there are two Type E decisions each with three options, and one Type U decision, with two options, we would have two separate multiverses, each with ___ × ___ = ___ observations of the criterion.
+It can be helpful to describe using language like
+We will define a number of multiverses using the combination of Type U Factor I (3 options) and Type N Factor J (5 options), for a total of 10. Within each multiverse, analytic specifications comprise combinations of Type E Factor K (3 options), Factor L (2 options), and Factor Q (6 options) for a total of 36.-->
+
+Enter your response here
+
+
+# Model definitions
+<!--If there is only a single multiverse, it is likely that you will have a single model to describe here. Some Type U and Type N decisions that would lead to examining several multiverses could require different models. Specify each model here. A table might be helpful.-->
+
+Enter your response here
+
+
+# Criterion of interest
+<!--Options include
+* Effect size and confidence interval (CI)
+* Statistical significance (p-value)
+* Model comparison
+* Or any other thing you can think of…
+If your research question has to do with the strength of association between two variables, or even simply if that association is positive or negative, the effect size with CI is a good choice. If you are interested in whether there is a detectable association at all, you could use p-values (though this is generally less useful than other options and is not recommended, as the same information plus more can be gotten from the effect size with CI). If you are wondering whether the effect size is different depending on the level of some other variable (moderation, or interaction; see sec. 3.1), model comparison statistics like AIC can be useful.-->
+
+Enter your response here
+
+
+# Interpretation and Decision process
+<!--how will you describe the results of your multiverse analysis and interpret the results? Is your criterion of interest consistent across all Type E decisions? Is there variation across Type N and Type U decisions? Some authors have proposed methods for statistical comparisons between multiverses (Simonsohn, Simmons, and Nelson 2020). Others caution against the probabilistic interpretation of multiverse analyses these methods require (Giudice and Gangestad 2021), opting instead to describe them as “possibilistic. (Hall et al. 2022)” Since we are uncertain about the single best analytic plan, it is possible that any single one of the results from the multiverse is the closest to the truth.
+Consider the following points when planning your interpretations:
+1. How will you define the “consistency” of your criterion?
+2. For each multiverse, is the criterion consistent across Type E decisions?
+    * If results are inconsistent, this may mean that some auxiliary assumptions in your research domain should be more deeply evaluated, and can be an important contribution.
+3. Across multiverses, is the criterion consistent across Type U decisions?
+    * This may be evidence that pushes Type U decisions more toward Type E or Type N decisions.
+4. How will the results of this multivese analysis inform future research on this domain?-->
+
+Enter your response here
+
+
+# References
+## 
+\vspace{-2pc}
+\setlength{\parindent}{-0.5in}
+\setlength{\leftskip}{-1in}
+\setlength{\parskip}{8pt}
+\noindent
diff --git a/inst/rmarkdown/templates/prp_quant_v3_prereg/skeleton/skeleton.Rmd b/inst/rmarkdown/templates/prp_quant_v3_prereg/skeleton/skeleton.Rmd
new file mode 100644
index 0000000..bc5e0a4
--- /dev/null
+++ b/inst/rmarkdown/templates/prp_quant_v3_prereg/skeleton/skeleton.Rmd
@@ -0,0 +1,329 @@
+---
+title           : "PRP-QUANT v3"
+shorttitle      : "My preregistration"
+date            : "`r Sys.setlocale('LC_TIME', 'C'); format(Sys.time(), '%d\\\\. %B %Y')`"
+
+author: 
+  - name        : First Author
+    affiliation : 1
+  - name        : Second Author
+    affiliation : "1,2"
+
+affiliation:
+  - id          : 1
+    institution : First institution
+  - id          : 2
+    institution : Second institution
+
+output: prereg::prereg_pdf
+---
+
+
+
+# Title
+
+## Contributors, Affiliations, and Persistent IDs
+<!--Provide in separate entries the full name of each contributor, each contributor's professional affiliation, and each contributor's persistent ID. See ORCID iD for an example of persistent ID (https://orcid.org/). Include the intended contribution of each person listed (e.g. statistical analysis, data collection). Optionally, you can use CRediT to describe these roles (https://credit.niso.org/). It is possible to change the contributions later in the process.-->
+
+Enter your response here
+
+
+## Time point of registration
+<!--Indicate at which point in the project the study is preregistered. Select one of the options and add an explanatory text if you select "Other".-->
+*Registration prior to creation/collection of data
+*Registration prior to any human observation of the data (i.e., prior to anyone looking into the data)
+*Registration prior to accessing the data (e.g., when conducting secondary data analysis)
+*Registration prior to analysis of the data
+*Other:
+- Please specify:
+
+
+## Versioning information
+<!--Indicate whether this is the first preregistration of your study or an updated version-->
+* This is the first preregistration of this study.
+* This is an updated version.
+- Insert the link to the previous version below; this should be a persistent identifier such as a DOI:
+
+
+
+# Project Documentation
+
+##  Estimated duration of project
+<!--Include the best estimate for how long the project will take from preregistration submission to project completion.-->
+
+Enter your response here
+
+
+## Ethical considerations: Has appropriate institutional approval been obtained?
+<!--Please select an option.-->
+*Yes
+- Please provide the ID that has been assigned to your ethics proposal: ..
+*Not yet but is planned
+*Study will be exempt from ethical board review
+- Please provide your reasoning:
+
+## Conflict of Interest Statement
+<!--Identify any real or perceived conflicts of interest with this study’s execution. For example, any interests or activities that might be seen as influencing the research (e.g., financial interests in a test or procedure, funding by pharmaceutical companies for research).-->
+*All authors declare that they have no conflicts of interest in the conduct of this study.
+*There is a potential conflict of interest.
+- Please elaborate:
+
+## Software
+<!--Indicate which software will be used for which step (e.g., OpenSesame will be used to implement the experiment, R will be used to analyze the data).-->
+
+Enter your response here
+
+## Will you use standard lab practices?
+<!--Standard lab practices refer to a (timestamped) document, software package, or similar, which specifies standard pipelines, analytical decisions, etc. which always apply to certain types of research in a lab-->
+*Yes
+- If you have published your standard lab practices in a repository, you have received a persistent identifier (e.g., a DOI) that we ask you to enter here. Alternatively, you can also try to copy and paste your standard lab practice.
+*No
+
+## Sharing level of your materials
+<!--Indicate the materials resulting from your study and, for each, whether/how you will make them available (but note that not all repositories offer all options). If your study does not include the materials listed, select "not applicable". The different sharing levels are defined as follows.
+-Public use file: Data access via download; usage of data for all purposes
+-Scientific use file: Data access via download; usage of data restricted to scientific purposes
+-Case basis: Data access via download; usage of data has to be agreed and defined on an individual case basis
+-Secure data center: No download; usage/analysis only in a secure data center
+-Upon request: Data available upon email request by member of scientific community
+-None: You do not plan to share this research product at all.-->
+
+Analysis code
+* as public use file
+* as scientific use file
+* on case basis
+* via secure data center
+* per email request
+* none
+* not applicable
+
+Raw data
+* as public use file
+* as scientific use file
+* on case basis
+* via secure data center
+* per email request
+* none
+* not applicable
+
+Experimental code
+* as public use file
+* as scientific use file
+* on case basis
+* via secure data center
+* per email request
+* none
+* not applicable
+
+Processed data
+* as public use file
+* as scientific use file
+* on case basis
+* via secure data center
+* per email request
+* none
+* not applicable
+
+## Planned repositories
+<!--Specify the planned repository(s) for archiving your materials or, if you have already published them, provide their persistent identifier(s).-->
+
+Enter your response here
+
+## Other information
+<!--If there is anything else you want to add regarding your project documentation, you can  do so here.-->
+
+Enter your response here
+
+
+# Abstract
+
+## Abstract 
+<!--Write a short summary (ca. 150 words) that includes information about the background of your study (i.e. how are your research hypotheses justified?), your objectives and research questions, your sample (e.g. size, recruitment, participant characteristics, or compensation), your methods (i.e. type of study, design) and your analyses (e.g. t tests comparing A vs. B). You will find more information about each point in the later sections of this template.-->
+
+Enter your response here 
+
+## Keywords 
+<!--Include terms specific to your topic, methodology, and population. Use natural language and avoid words used in the title or overly general terms. If you need help with keywords, try a keyword search using your proposed keywords in a search engine to check the results. We recommend using between 3 and 7 keywords (e.g., “evaluative learning, subliminal influence, implicit learning, replication”).-->
+
+Enter your response here 
+
+# Introduction
+
+## Theoretical background
+<!--Provide a concise overview that justifies the research questions/hypotheses (you can use bullet points). This should contain any relevant theoretical and/or practical/methodological justification. Please include references here and list them at the end of this document in the reference section-->
+
+Enter your response here
+
+## Objectives and Research question(s)
+<!--Outline the a) objectives and b) research questions that inform the methodology and analyses described below.-->
+
+Enter your response here 
+
+## Hypotheses (H1, H2, …)
+<!--If you test hypotheses in your study: Provide hypotheses for predicted results in terms of the specific constructs that will inform the independent and dependent variables (e.g., main effects, interactions), if available. If multiple hypotheses, uniquely number them (e.g., H1, H2a, H2b) and refer to them the same way at other points in this registration document and the manuscript.-->
+
+Enter your response here 
+
+## Exploratory research questions (if applicable; E1, E2, ....)
+<!--If you have exploratory research questions: Provide a rationale for your exploratory questions here. If multiple exploratory research questions, uniquely number them (E1, E2a, E2b, ...) and refer to them in the same way in the registration document and future publications.--> 
+
+Enter your response here
+
+
+# Method
+
+
+## Pre-existing data: Publication status, source, and knowledge
+<!--Will pre-existing data be used in the planned study? If yes, indicate whether the data were previously published and specify the source of the data (e.g., DOI or APA style reference of original publication). Specify your level of knowledge of the data in more detail below (e.g., descriptive statistics from previous publications), whether or not this is relevant for the hypotheses of the present study, and how it is assured that you are unaware of results or statistical patterns in the data of relevance to the present hypotheses.-->
+
+Enter your response here
+
+## Piloting
+<!--Describe any conducted or planned piloting of your material/method. If already conducted, describe your pilot study, and what you have learned from it. You can also add the results of the pilot as supplementary material.-->
+
+Enter your response here
+
+## Sample size justification
+<!--Report relevant planned sample sizes:
+For designs with fixed sample sizes, provide a rationale (e.g. power analysis/curves, practical reasons). Give references, if appropriate.
+For sequential designs, indicate your ‘stopping rule’ such as the points at which you intend to be viewing your data and in any way analyzing them.-->
+
+Enter your response here
+
+## Participant recruitment, selection, and compensation
+<!--Indicate:
+(a) methods of recruitment (e.g., subject pool advertisement, community events, crowdsourcing platforms, snowball sampling)
+(b) selection and inclusion/exclusion criteria for the data collection (e.g., age, visual acuity, language facility)
+(c) details of any quota sampling or stratification sampling (i.e., splitting a population into subpopulations and sampling each subpopulation independently) used
+(d) planned participant characteristics (e.g., gender, race/ethnicity, sexual orientation, and gender identity, SES, education level, age, disability or health status, geographic location); 
+(e) compensation amount and method (e.g., same payment to all, pay based on performance, lottery).-->
+
+Enter your response here
+
+## How will participant drop-out be handled?
+<!--Indicate any special treatment for participants who drop out (i.e., who are recruited but withdraw before the study concludes), e.g., whether there is a follow-up in a manner different from the main sample, the last value is carried forward, or participants are replaced. Note that procedures to handle missing data during data analyses are inquired in the next section.-->
+
+Enter your response here
+
+## Masking of participants and researchers
+<!--Indicate whether you will use any form of masking. If yes, describe the masking procedure (e.g., are administrators, data collectors, raters, or confederates unaware of the condition to which participants were assigned; are participants, experimenters, or analysts unaware of the hypotheses).-->
+
+Enter your response here
+
+## Other information
+<!--If there is anything else you want to add regarding your sampling procedure and data collection, describe it here.-->
+
+Enter your response here
+
+## Type of study and study design
+<!--Indicate the type of study (e.g., experimental, observational, cross-sectional vs. longitudinal, single case, clinical trial), effects of interest (e.g., between vs. within subjects), the number of factors and factor levels, etc..-->
+
+Enter your response here
+
+## Randomization of participants and/or experimental materials
+<!--If applicable, describe how 
+(a) participants are assigned to conditions or treatments, 
+(b) how stimuli are assigned to conditions, and 
+(c) how the presentation of tests, trials, etc. is randomized or counterbalanced. 
+Indicate the randomization technique and whether constraints were applied (pseudo-randomization). Indicate any type of balancing across participants (e.g., assignments of responses to hands, etc.).-->
+
+Enter your response here
+
+## Measured variables, manipulated variables, covariates
+<!--This section shall be used to unambiguously clarify which variables are used to operationalize the hypotheses and/or exploratory research questions specified in the introduction section. Please (a) list all measured and manipulated variables, and (b) explicitly state the functional role of each variable (i.e., independent variable, dependent variable, covariate, mediator, moderator, exploratory measure). It is important to specify for each hypothesis how it is operationalized, i.e., which variables will be used to test the respective hypothesis and how the hypothesis will be operationally defined in terms of these variables. The description here shall be consistent with the statistical analysis plans specified in the analyses section.-->
+
+Enter your response here
+
+## Exhaustiveness of variables
+<!--Confirm that the above information is complete before proceeding. Whichever option you choose, adding additional variables at a later date is a deviation from this preregistration.-->
+* All variables of the planned study are outlined here and their roles are clearly defined.
+* Our planned study includes additional variables that are not listed here.
+- Please explain why not all variables are listed above:
+
+## Study Materials
+<!--Please describe any relevant study materials (e.g., stimulus materials used for experiments, questionnaires used for rating studies, training protocols for intervention studies) or provide their persistent identifier. State any adaptations made to existing measures.-->
+
+Enter your response here 
+
+## Study Procedures
+<!--Please describe any relevant information about how the study will be conducted (e.g., the number and timing of measurement for longitudinal research, the number of blocks, the number of training sessions in interventional studies, questionnaire/study administration for online assessments/laboratory setting).-->
+
+Enter your response here 
+
+## Other information
+<!--If there is anything else you want to add regarding your conditions and design, describe it here.-->
+
+Enter your response here 
+
+
+# Analysis plan
+
+
+## Criteria for post-data collection exclusion of participants
+<!--Describe all criteria that will lead to the exclusion of a participant's data (e.g. performance criteria, non-responding in physiological measures, incomplete data). Be as specific as possible (e.g., detail cut-offs, use of list- or case-wise exclusion). Clarify whether a participant who is an outlier in one variable will only be excluded from the analyses that involve that specific variable, or if they will be excluded from all tests.-->
+
+Enter your response here 
+
+## Criteria for post-data collection exclusions on trial level
+<!--Describe all criteria that will lead to the exclusion of a trial or item (e.g. statistical outliers, response time, reliability scores, incorrect trials). Be as specific as possible.--> 
+
+Enter your response here 
+
+## Exhaustiveness of exclusion criteria
+<!--Confirm that the above information is complete before proceeding. Whichever option you choose, adding additional exclusion criteria at a later date is a deviation from this preregistration.-->
+* All exclusion criteria (on the participant and trial level) that are currently planned are listed above.
+* Not all exclusion criteria are listed above.
+- Please explain why some exclusion criteria are not described:
+
+## Data preprocessing
+<!--Describe all data manipulations that are performed in preparation of the main analyses (e.g., calculation of variables or scales, recoding, any data transformations, preprocessing steps for imaging or physiological data). If applicable, refer to your publicly accessible standard lab procedure.-->
+
+Enter your response here
+
+## Data cleaning and screening
+<!--Indicate all steps related to data quality control (e.g., testing assumptions, outlier treatment, interrater reliability training, checks for normality).-->
+
+Enter your response here 
+
+## How will missing data be handled?
+<!--Indicate any procedures that will be applied during the analysis to deal with missing data, such as (a) case deletions; (b) averaging across scale items (to handle missing items for some); (c) test of missingness (MAR, MCAR, MNAR assumptions); (d) imputation procedures; (e) intention to treat analysis and per protocol analysis (as appropriate).-->
+
+Enter your response here 
+
+## Reliability analysis (if applicable)
+<!--Specify the type of scale reliability that will be estimated, whether it is internal consistency (e.g. Cronbach's alpha, omega), test-retest reliability, or some other form (e.g., a confirmatory factor analysis incorporating multiple factors as sources of variance). In a study involving measure development, researchers should specify criteria for removing items from measures a priori (e.g., largest factor loading magnitude, smallest drop in alpha-if-item removed).-->
+
+Enter your response here
+
+## Descriptive statistics
+<!--Specify which descriptive statistics will be calculated for which variables (refer to the variables specified in the previous section). If descriptive statistics are linked to specific hypotheses in the introduction section, explicitly link the information given here to the respective hypothesis.-->
+
+Enter your response here
+
+## Statistical models
+<!--If you test hypotheses in your study: Specify the statistical model (e.g. t test, ANOVA, LMM) that will be used to test each of your hypotheses. Give all necessary information about model specification (e.g., variables, interactions, planned contrasts) and follow-up analyses. Include model selection criteria (e.g., fit indices), corrections for multiple testing, and tests for statistical violations, if applicable.-->
+
+Enter your response here
+
+## Inference criteria
+<!--If you test hypotheses in your study: Specify the criteria used for inferences (e.g., p values, Bayes factors, confidence intervals, effect size measures) and the thresholds for accepting or rejecting your hypotheses. If possible, define the smallest effect size of interest. If inference criteria differ between hypotheses, specify separately for each hypothesis and respective statistical model by explicitly referring to the numbers of the hypotheses. Describe which effect size measures will be reported and how they are calculated-->
+
+Enter your response here 
+
+## Exploratory analysis
+<!--Based on the exploratory research questions outlined in the introduction section, describe any exploratory analyses to be conducted with your data. Include any planned analyses that are not confirmatory in the sense of being a direct test of one of the specified hypotheses. Please note that other non-registered exploratory analyses can be added in the article.-->
+
+Enter your response here
+
+## Other information
+<!--If there is anything else you want to add regarding your analysis plan, describe it here-->
+
+Enter your response here 
+
+# References
+## 
+\vspace{-2pc}
+\setlength{\parindent}{-0.5in}
+\setlength{\leftskip}{-1in}
+\setlength{\parskip}{8pt}
+\noindent
diff --git a/inst/rmarkdown/templates/qualitative_study_prereg/skeleton/skeleton.Rmd b/inst/rmarkdown/templates/qualitative_study_prereg/skeleton/skeleton.Rmd
new file mode 100644
index 0000000..26997b2
--- /dev/null
+++ b/inst/rmarkdown/templates/qualitative_study_prereg/skeleton/skeleton.Rmd
@@ -0,0 +1,177 @@
+---
+title           : "My preregistration for a qualitative study based on Haven et al. (2020) template"
+shorttitle      : "My preregistration"
+date            : "`r Sys.setlocale('LC_TIME', 'C'); format(Sys.time(), '%d\\\\. %B %Y')`"
+
+author: 
+  - name        : First Author
+    affiliation : 1
+  - name        : Second Author
+    affiliation : "1,2"
+
+affiliation:
+  - id          : 1
+    institution : First institution
+  - id          : 2
+    institution : Second institution
+
+output: prereg::prereg_pdf
+---
+
+# Study Information
+
+## Researcher background
+<!-- Describe your characteristics, background or standpoints as a researcher. Here you can think about your identity, credentials, gender, experience, values, etc. -->
+
+
+Enter your response here.
+
+
+## Duration
+
+Project start date: 
+
+Estimated project end date: 
+
+
+
+
+## Research Aim
+<!-- Please specify the overall aim of the research. -->
+
+Enter your response here.
+
+
+## Research Question(s)
+<!-- Please specify your research question(s). -->
+
+Enter your response here.
+
+
+## Theoretical expectations
+<!-- Please specify (theoretical) expectations you have. -->
+
+Enter your response here.
+
+
+## Hypotheses
+<!-- Specify whether your study is primarily hypothesis-testing or hypothesis-generating. Hypothesis here refers to a specific assertion about the world that you want to evaluate using your research. If relevant, describe both your main as well as the alternative hypotheses in as much detail as possible. Try to be precise in the proposition you want to evaluate. -->
+
+Enter your response here.
+
+
+# Design Plan
+
+## Study Type
+<!-- Please indicate and expand on your study type(s). Typical study types are case studies, evaluation research, intervention research, participatory research. In addition, if you do participatory research, you may want to elaborate on whether the participation is overt or covert. -->
+
+Enter your response here.
+
+
+## Tradition
+<!-- Please describe the tradition that best describes your approach. This may help the reader in interpreting your results later. Typical traditions that have their own approach to qualitative inquiry are: Grounded theory, Phenomenology, Narrative approach, Ethnography, Case study or Text-based approach (discourse analysis, conversation analysis). -->
+
+Enter your response here.
+
+
+## Sort of sample
+<!-- Please describe the composition of your sample. Typical compositions are heterogeneous, homogenous, extreme or deviant cases or typical cases. -->
+
+Enter your response here.
+
+
+## Sampling strategy
+<!-- Please elaborate on your sampling strategy, i.e. whether you want to focus on a specific case or whether you will select individuals from a population of interest. -->
+
+Enter your response here.
+
+
+# Data Collection
+
+## Origin of data
+<!-- Please indicate the origin of your data and elaborate: 
+* pre-existing data (e.g. archival work or data collected by other researchers) 
+* Original data collection (i.e. the data will be collected by the current researcher(s)) -->
+
+Enter your response here.
+
+
+## Type of Data
+<!-- Please describe the type of data. Typical types would be text data (spoken/written) or visual data (photos/videos/other). -->
+
+Enter your response here.
+
+
+## Data Collection Procedures
+<!-- Please describe your data collection procedure(s). Typical sorts of collection procedures are: interviews, surveys, enabling techniques (e.g. creative methods, card sorting), self-reports (e.g. diaries), participant observation, focus groups, archival research or use of secondary sources (e.g. published books/articles). -->
+
+Enter your response here.
+
+
+## Data collection plan
+<!-- Please describe your data collection plan freely for each procedure. Be as explicit as possible. For example, if you listed ‘interviews’ in the data collection procedures before, elaborate on the type of interviews you plan on conducting. You may want to upload your topic guide, observation guide, questionnaire, etc. Additionally, if you plan to use elicitation techniques in your interviews, you may want to describe that here. -->
+
+Enter your response here.
+
+
+## Sampling Rationale
+<!-- Please describe your sampling rationale, typical strategies are: maximum variation, purposive, theoretical, convenience, snowball, random or mixed. -->
+
+Enter your response here.
+
+
+## Stopping Rule
+<!-- Please describe what will determine the termination of your data collection. Typical ‘rules’ could be data saturation, when sample inclusion criteria are satisfied or resources (e.g. time/money). -->
+
+Enter your response here.
+
+
+# Analysis Plan
+
+## Data Analysis
+<!-- Please describe your data analysis approach. Typical approaches could be: Narrative analysis, Interpretive phenomenological analysis, Grounded theory, Thematic analysis, Process tracing or Comparative analysis. -->
+
+Enter your response here.
+
+
+## Evidence criteria
+<!-- Please describe what kinds of evidence or patterns in the evidence would count for or against the hypotheses being tested. For example, when using thematic content analysis, you may want to describe the criteria that will be used to identify when a theme will be an emerging theme. -->
+
+Enter your response here.
+
+
+## Weight of evidence
+<!-- Please describe which pieces of, or patterns in, the evidence will weigh most heavily. For example, when using thematic content analysis, this may be a place to describe whether the criteria you use to identify a theme were identified in advance (and how this may count as strong evidence) or derived from the data. If using process tracing, examples of useful categories of ‘tests’ would be Van Evera’s (1997) four test types (e.g., hoop, smoking-gun, straw-in-the-wind, doubly decisive). -->
+
+Enter your response here.
+
+
+## Data analysis explained
+<!-- Please describe how you will go about analyzing your data. Try to share with the reader what the actual procedure will look like. Questions to keep in mind could be: 
+* What will your coding process look like? 
+* How will you use qualitative data analysis software? 
+* What sort of analytical tools (e.g. active citation, computer assisted qualitative data analysis) will you use? -->
+
+Enter your response here.
+
+
+## Data validation
+<!-- Please describe what data validation measures you will employ. Typical data validation measures could be: 
+* member-checking (sending participants a summary of your understandings) 
+* have multiple researchers analyse the data 
+* bring in an ‘auditor’ 
+* triangulation with other data sources 
+* negative case analysis 
+* peer debriefing 
+* cross-checks for rivalling explanations -->
+
+Enter your response here.
+
+
+# References
+## 
+\vspace{-2pc}
+\setlength{\parindent}{-0.5in}
+\setlength{\leftskip}{-1in}
+\setlength{\parskip}{8pt}
+\noindent
diff --git a/inst/rmarkdown/templates/scoping_reviews_prereg/skeleton/skeleton.Rmd b/inst/rmarkdown/templates/scoping_reviews_prereg/skeleton/skeleton.Rmd
new file mode 100644
index 0000000..1cac50d
--- /dev/null
+++ b/inst/rmarkdown/templates/scoping_reviews_prereg/skeleton/skeleton.Rmd
@@ -0,0 +1,194 @@
+---
+title           : "Preregistration Template for Scoping Reviews"
+shorttitle      : "My preregistration"
+date            : "`r Sys.setlocale('LC_TIME', 'C'); format(Sys.time(), '%d\\\\. %B %Y')`"
+
+author: 
+  - name        : First Author
+    affiliation : 1
+  - name        : Second Author
+    affiliation : "1,2"
+
+affiliation:
+  - id          : 1
+    institution : First institution
+  - id          : 2
+    institution : Second institution
+
+output: prereg::prereg_pdf
+---
+
+
+# Title
+
+
+## Contributors, Affiliations, and Persistent IDs (recommend ORCID iD)
+<!--Provide in separate entries the full name of each contributor, each contributor's professional affiliation, and each contributor's persistent ID. See ORCID iD for an example of persistent ID (https://orcid.org/). Optional: include the intended contribution of each person listed (e.g. conceptualization, data collection; see CRediT, https://casrai.org/credit/).-->
+
+Enter your response here
+
+## Date of Preregistration
+<!--This is assigned by the system upon preregistration submission.-->
+
+Enter your response here
+
+## Project stage at registration
+<!--Describe what steps of project planning have already been completed and what the current status of the project is at the time of preregistration.-->
+
+Enter your response here
+
+## Anticipated start date
+<!--Indicate the approximate time when data gathering for the scoping review is scheduled to begin.-->
+
+Enter your response here
+
+## Estimated duration of project
+<!--Include best estimate for how long the project will take from preregistration submission to project completion.-->
+
+Enter your response here
+
+## Optional: IRB Status
+<!--If appropriate institutional approval has been obtained for the study, provide the relevant identifier here. If the study will be exempt from ethical board review, provide reasoning here.-->
+
+Enter your response here
+
+## Conflict of Interest Statement
+<!--Identify any real or perceived conflicts of interest with this review. For example, any interests or activities that might be seen as influencing the research (e.g., financial interests in a test or procedure, funding by pharmaceutical companies for research).-->
+
+Enter your response here
+
+## Keywords
+<!--Include terms specific to your topic, methodology, and population. Use natural language and avoid words used in the title or overly general terms. If you need help with keywords, try a keyword search using your proposed keywords in a search engine to check results.-->
+
+Enter your response here
+
+## Data accessibility statement and planned repository
+
+* We plan to make the data available (yes / no)
+
+<!--If "yes", please specify the planned data availability level by selecting one of the options:-->
+
+* Data access via download; usage of data for all purposes (public use file)
+* Data access via download; usage of data restricted to scientific purposes (scientific use file)
+* Data access via download; usage of data has to be agreed and defined on an
+individual case basis
+* Data access via secure data center (no download, usage/analysis only in a secure
+data center)
+* Data available upon email request by member of scientific community
+* Other (please specify)
+
+
+## Optional: Code availability
+<!--We plan to make the code available (yes / no).
+If "yes", please specify the planned code availability level (use same descriptors of data in T10).-->
+
+Enter your response here
+
+## Optional: Standard lab practices
+<!--Standard lab practices refer to a (timestamped) document, software package, or similar, which specifies standard pipelines, analytical decisions, etc. which always apply to certain types of research in a lab. Specify here and refer to at the appropriate positions in the remainder of the template:
+We plan to make the standard lab practices available (yes / no).
+If "yes", please specify the planned standard lab practices availability level (use same descriptors of data in T10).-->
+
+Enter your response here
+
+## Funding
+<!--Describe sources of funding for the included sources of evidence, as well as sources of funding for the scoping review. Describe the role of the funders of the scoping review.-->
+
+Enter your response here
+
+
+# Abstract
+
+## Structured summary
+<!--Provide a structured summary that includes (as applicable): background, objectives, eligibility criteria, sources of evidence, and charting methods that relate to the review questions and objectives.-->
+
+Enter your respone here
+
+
+# Introduction 
+
+
+## Rationale 
+<!--Describe the rationale for the review in the context of what is already known. Explain why the review questions/objectives lend themselves to a scoping review approach.-->
+
+Enter your respone here
+
+## Objectives
+<!--Provide an explicit statement of the questions and objectives being addressed with reference to their key elements (e.g., population or participants, concepts, and context) or other relevant key elements used to conceptualize the review questions and/or objectives.-->
+
+Enter your respone here
+
+
+# Methods 
+
+## Eligibility criteria
+<!--Specify characteristics of the sources of evidence that will be used as eligibility criteria (e.g., years considered, language, and publication status), and provide a rationale.-->
+
+Enter your respone here
+
+## Information sources
+<!--Where sources of evidence are compiled from, such as bibliographic databases, social media platforms, and Web sites.
+Describe all information sources in the search (e.g., databases with dates of coverage and contact with authors to identify additional sources), as well as the date the most recent search was executed-->
+
+Enter your respone here
+
+## Search
+<!--Present the full electronic search strategy for at least 1 database, including any limits that will be used, such that it could be repeated.-->
+
+Enter your respone here
+
+## Selection of sources of evidence
+<!--A more inclusive/heterogeneous term used to account for the different types of evidence or data sources (e.g., quantitative and/or qualitative research, expert opinion, and policy documents) that may be eligible in a scoping review as opposed to only studies. This is not to be confused with information sources.
+State the process for selecting sources of evidence (i.e., screening and eligibility) included in the scoping review.-->
+
+Enter your respone here
+
+# Data charting process
+<!--The frameworks by Arksey and O’Malley (Scoping studies: towards a methodological framework. Int
+J Soc Res Methodol. 2005;8:19-32.) and Levac and colleagues (Scoping studies: advancing the
+methodology. Implement Sci. 2010;5:69. [PMID: 20854677] doi:10.1186/1748-5908-5-69)
+guidance (Guidance for conducting systematic scoping reviews. Int J Evid Based Healthc. 2015;13:141-6.
+[PMID: 26134548] doi:10.1097/XEB.0000000000000050
+;
+Scoping reviews. In: Aromataris E, Munn Z, eds.
+Joanna Briggs Institute Reviewer's Manual. Adelaide, Australia: Joanna Briggs Inst; 2017.)
+refer to the process of data extraction in a scoping review as data charting.
+
+Describe the methods of charting data from the included sources of evidence (e.g., calibrated forms or forms that have been tested by the team before their use, and whether data charting will be done independently or in duplicate) and any processes for obtaining and confirming data from investigators.-->
+
+Enter your respone here
+
+## Data items
+<!--List and define all variables for which data will be sought and any assumptions and simplifications that will be made.-->
+
+Enter your respone here
+
+## Critical appraisal of individual sources of evidence
+<!--The process of systematically examining research evidence to assess its validity, results, and relevance before using it to inform a decision. This term is used for this item instead of "risk of bias" (which is more applicable to systematic reviews of interventions) to include and acknowledge the various sources of evidence that may be used in a scoping review (e.g., quantitative and/or qualitative research, expert opinion, and policy document).
+If done, provide a rationale for conducting a critical appraisal of included sources of evidence; describe the methods that will be used and how this information will be used in any data synthesis (if appropriate).-->
+
+Enter your respone here
+
+## Synthesis of results
+<!--Describe the methods of handling and summarizing the data that will be charted.-->
+
+Enter your respone here
+
+
+# Other information optional
+<!--(NOTE: If needed, multiple lines with other information can be included)-->
+
+
+## Other information
+<!--If there is any additional information that you feel needs to be included in your preregistration, please enter it here. Literature cited, disclosures of any related work such as replications or work that uses the same data, or other context that will be helpful for future readers would be appropriate here.-->
+
+Enter your respone here
+
+
+# References
+## 
+\vspace{-2pc}
+\setlength{\parindent}{-0.5in}
+\setlength{\leftskip}{-1in}
+\setlength{\parskip}{8pt}
+\noindent
diff --git a/inst/rmarkdown/templates/secondary_data_analysis_prereg/skeleton/skeleton.Rmd b/inst/rmarkdown/templates/secondary_data_analysis_prereg/skeleton/skeleton.Rmd
new file mode 100644
index 0000000..a7c0059
--- /dev/null
+++ b/inst/rmarkdown/templates/secondary_data_analysis_prereg/skeleton/skeleton.Rmd
@@ -0,0 +1,220 @@
+---
+title           : "My preregistration of a secondary data analysis"
+shorttitle      : "My preregistration"
+date            : "`r Sys.setlocale('LC_TIME', 'C'); format(Sys.time(), '%d\\\\. %B %Y')`"
+
+author: 
+  - name        : First Author
+    affiliation : 1
+  - name        : Second Author
+    affiliation : "1,2"
+
+affiliation:
+  - id          : 1
+    institution : First institution
+  - id          : 2
+    institution : Second institution
+
+output: prereg::prereg_pdf
+---
+
+
+
+# Part 1: Study information
+
+## Study title
+<!-- Provide the working title of your study. Make clear it is a secondary data analysis (e.g., by naming the dataset). -->
+
+Enter your response here.
+
+
+## Authors
+<!-- List all authors of this preregistration. Add identifiers (e.g., ORCID or personal webpage) so readers can assess prior work and potential prior knowledge of the data. -->
+
+Enter your response here.
+
+
+## Research questions
+<!-- List each research question to be addressed in this study. You can phrase them conceptually; it is also fine to preregister only hypotheses and skip separate research questions. -->
+
+Enter your response here.
+
+
+## Hypotheses and rationale
+<!-- Provide all hypotheses for your secondary data analysis. Make them specific and testable. Indicate your statistical framework (e.g., NHST, Bayesian). If directional, state the direction. Briefly explain the theoretical and empirical rationale for each hypothesis. -->
+
+Enter your response here.
+
+
+# Part 2: Data description
+
+## Dataset description
+<!-- Name and describe the dataset(s) and, if applicable, subset(s) you will use. Include: - type of data (e.g., cross-sectional, longitudinal), - general content and domains covered, - key details about respondents (e.g., population, time frame), - if longitudinal: relevant waves/measurement occasions. -->
+
+Enter your response here.
+
+
+## Openness and availability of the data
+<!-- Describe to what extent the dataset is open or publicly available. Mention any access restrictions (e.g., registration, data use agreements, approval by data owners, non-public data). -->
+
+Enter your response here.
+
+
+
+
+## How to access the data
+<!-- Explain how the data can be accessed. If possible, provide a persistent identifier (e.g., DOI) or a direct link. If not publicly accessible, describe how you obtained the data. -->
+
+Enter your response here.
+
+
+
+
+## Date of download and access
+<!-- For each author, specify: - when the data (and/or metadata) were downloaded, - when they were first accessed for this project. Indicate which author’s download will be used for the actual analyses. Use an unambiguous date format (e.g., “12 February 2019”). -->
+
+Enter your response here.
+
+
+
+
+## Data collection procedure
+<!-- If the data collection procedure is documented, provide a link. Otherwise, describe to the best of your knowledge: - who collected the data (e.g., lab, survey organization, private company), - sampling frame and target population, - recruitment and inclusion/exclusion criteria, - whether randomization was used, - any compensation, - aspects that may affect representativeness or bias. -->
+
+Enter your response here.
+
+
+
+
+## Codebook and documentation
+<!-- If a codebook or other documentation exists, link or describe it here. Indicate which parts are most relevant for this preregistration. If no codebook is publicly available, explain what documentation you can share (e.g., your own data dictionary) and any limitations. -->
+
+Enter your response here.
+
+
+# Part 3: Variables
+
+## Manipulated variables
+<!-- Describe any manipulated variables you plan to use, if applicable. For each, specify: - levels or treatment arms, - how groups are defined or collapsed, - any planned manipulation checks and decision rules. If no manipulated variables will be used, state that explicitly. -->
+
+Enter your response here.
+
+
+## Measured variables (outcomes, predictors, covariates)
+<!-- List all measured variables that will be used in confirmatory analyses: - outcome/dependent variables, - predictors/independent variables, - covariates or control variables. For each variable or scale, specify: - construct it represents, - item names/codes and response options, - how items will be aggregated (sum, mean, factor scores, etc.), - if aggregation is based on codebook, prior validation, EFA, or CFA (and how those will be done), - coding scheme for categorical variables (e.g., dummy coding, reference group). -->
+
+Enter your response here.
+
+
+
+
+## Inclusion and exclusion criteria; initial sample size
+<!-- Describe which units (e.g., respondents, cases) will be included or excluded. State any inclusion or exclusion criteria based on study design, population, or pre-treatment characteristics. Given these criteria and what you know about the dataset, provide an expected sample size for the analysis before accounting for missing data or outliers. If exact size is unknown, give a conservative estimate. -->
+
+Enter your response here.
+
+
+
+
+## Missing data and updated sample size
+<!-- Describe what you know about missing data: - overall missingness, - variable-specific missingness, - any known patterns (e.g., attrition, differential dropout). Explain how you will handle missing data (e.g., listwise deletion, multiple imputation), including any diagnostics for MCAR/MAR/MNAR if relevant. Provide an updated expected sample size after applying your missing data strategy. If you cannot precisely quantify missingness, provide a conservative estimate or describe any blinding/masking procedure you use to remain “data-naïve”. -->
+
+Enter your response here.
+
+
+
+
+## Outliers and final sample size
+<!-- Describe how you will define and handle outliers (for each relevant variable if necessary). Examples include: - fixed cutoffs, - distance-based rules (e.g., ±1.5×IQR, ±3 SD), - model-based approaches. Estimate how many observations you expect to remove and give the resulting expected final sample size. This will be the sample size used for power analyses. -->
+
+Enter your response here.
+
+
+
+
+## Sampling weights
+<!-- Indicate whether sampling weights are available and, if so, whether you will use them. If you plan to use weights, explain: - why they are needed, - how they will be incorporated in the analyses (e.g., software or packages used). If you do not use weights, briefly justify why. -->
+
+Enter your response here.
+
+
+# Part 4: Knowledge of data
+
+## Previous work with this dataset
+<!-- For each author, list any prior work (published, in preparation, conference presentations) based on this dataset that is relevant for the current analysis. For each piece of work, specify: - citation or description, - which wave(s) (if longitudinal) and which variables were used. Also indicate relevant work with the dataset by close collaborators or your lab, as this may have influenced your knowledge of the data. -->
+
+Enter your response here.
+
+
+
+
+## Prior knowledge relevant to this analysis
+<!-- Describe any prior knowledge you (and co-authors) have about variables, associations, or patterns in this dataset that could influence the current hypotheses or analyses. This includes: - direct knowledge of associations, - insights from earlier analyses or codebooks, - any known issues like restriction of range, reliability, or scaling effects. Be as transparent as possible so readers can assess potential bias. -->
+
+Enter your response here.
+
+
+# Part 5: Analyses
+
+## Statistical models
+<!-- For each hypothesis, describe the statistical model you will use, including: - model type (e.g., linear regression, logistic regression, ANOVA, SEM), - dependent/outcome variables, - predictors and covariates, - any interactions, contrasts, or subgroup analyses, - which analyses are primary vs. secondary. If possible, you may write or link to the analysis code you plan to use. -->
+
+Enter your response here.
+
+
+## Effect sizes of interest
+<!-- For each focal effect, specify: - either a predicted effect size OR - the smallest effect size of interest (SESOI). Explain how these values were chosen (e.g., prior studies, meta-analyses, or judgment). Clarify how you will transform effect sizes (e.g., to standardized coefficients) if needed. -->
+
+Enter your response here.
+
+
+
+
+## Power or precision
+<!-- Using the expected final sample size (after missing data and outliers), present either: - the statistical power to detect your effect size(s) of interest, OR - the expected precision (e.g., width of confidence intervals). Report: - software and version used (e.g., G*Power, R + packages), - input parameters and assumptions (e.g., variance explained, base rate), - any justification for the SESOI or target power. -->
+
+Enter your response here.
+
+
+
+
+## Inference criteria
+<!-- Specify how you will draw conclusions from the analyses, including: - which statistics you will focus on (e.g., p-values, confidence intervals, effect sizes, Bayes factors, model fit indices), - alpha level(s) or other thresholds, - whether tests are one- or two-sided, - how you will handle multiple comparisons (if applicable). Be explicit to avoid changing criteria post hoc. -->
+
+Enter your response here.
+
+
+## Handling assumption violations and analytic problems
+<!-- Describe how you will respond if: - model assumptions are violated (e.g., non-normality, heteroscedasticity), - models do not converge, - you encounter other analytic problems. You may specify: - planned transformations, - alternative models or robust methods, - a decision tree or general strategy for handling such issues. -->
+
+Enter your response here.
+
+
+
+
+## Robustness and sensitivity analyses
+<!-- Describe planned analyses to evaluate the robustness of your conclusions. Examples: - repeating analyses with/without outliers, - using alternative model specifications, - adding/removing covariates, - cross-validation or split-sample checks, - applying sampling weights, - regularization or other overfitting adjustments, - bootstrap or simulation-based checks. Specify which analyses you consider confirmatory robustness checks. -->
+
+Enter your response here.
+
+
+
+
+## Exploratory analyses
+<!-- If you plan to conduct exploratory analyses (e.g., searching for additional patterns, testing extra variables), describe these plans here in broad terms. In the final paper, clearly distinguish these exploratory tests from confirmatory ones. -->
+
+Enter your response here.
+
+
+# Part 6: Statement of integrity
+The authors of this preregistration state that they have filled out this preregistration
+to the best of their knowledge and that no other preregistration exists pertaining
+to the same hypotheses and dataset.
+
+# References
+## 
+\vspace{-2pc}
+\setlength{\parindent}{-0.5in}
+\setlength{\leftskip}{-1in}
+\setlength{\parskip}{8pt}
+\noindent
diff --git a/inst/rmarkdown/templates/vantveer_prereg/skeleton/skeleton.Rmd b/inst/rmarkdown/templates/vantveer_prereg/skeleton/skeleton.Rmd
index 4abe66e..9690766 100644
--- a/inst/rmarkdown/templates/vantveer_prereg/skeleton/skeleton.Rmd
+++ b/inst/rmarkdown/templates/vantveer_prereg/skeleton/skeleton.Rmd
@@ -17,54 +17,64 @@ affiliation:
 
 output: prereg::prereg_pdf
 ---
+<!--For any required question that does not apply to your study put ‘N/A’ in the space for the relevant field. See van ‘t Veer & Giner-Sorolla (2016) or https://osf.io/56g8e/ for additional information. -->
 
 # Hypotheses
 
-## Predictions
-<!-- Describe the (numbered) hypotheses in terms of directional relationships between your (manipulated or measured) variables. For interaction effects, describe the expected shape of the interactions. If you are manipulating a variable, make predictions for successful check variables or explain why no manipulation check is included. -->
+## Description of essential elements*
+<!--
+        1. Describe the (numbered) hypotheses in terms of directional relationships between your (manipulated or measured) variables.
+        2. For interaction effects, describe the expected shape of the interactions.
+        3. If you are manipulating a variable, make predictions for unsuccessful check variables or explain why no manipulation check is included. -->
 
 Enter your response here.
 
 
-## Rationale (optional)
-<!-- A figure or table may be helpful to describe complex interactions; this facilitates correct specification of the ordering of all group means. For original research, add rationales or theoretical frameworks for why a certain hypothesis is tested. If multiple predictions can be made for the same IV-DV combination, describe what outcome would be predicted by which theory. -->
+## Recommended elements
+<!-- A figure or table may be helpful to describe complex interactions; this facilitates correct specification of the ordering of all group means.
 
-Enter your response here.
-
-
-# Methods
-
-## Design
-<!-- List, based on your hypotheses from the hypotheses section A, independent variables with all their levels
-    a) whether they are within- or between-participant
-    b) the relationship between them (e.g., orthogonal, nested).
+File(s) selected for upload:
 
-List dependent variables, or variables in a correlational design and variables acting as covariates or moderators. -->
+        1. For original research, add rationales or theoretical frameworks for why a certain hypothesis is tested.
+        2. If multiple predictions can be made for the same IV-DV combination, describe what outcome would be predicted by which theory. -->
 
 Enter your response here.
 
 
-## Planned sample
-<!-- If applicable, describe pre-selection rules. Indicate where, from whom and how the data will be collected. Justify planned sample size. If applicable, include or refer to a file related to your power analysis here (e.g., a protocol of power analyses from G*Power, a script, a screenshot, etc.). Describe data collection termination rule. -->
+# Methods
 
-Enter your response here.
+## Design*
+<!-- List, based on your hypotheses from section A:
+1. Independent variables with all their levels
+    1. Whether they are within- or between-participant
+    2. The relationship between them (e.g., orthogonal, nested).
+2. List dependent variables, or variables in a correlational design
+3. Third variables acting as covariates or moderators. -->
 
+Enter your response here
 
-## Exclusion criteria
-<!-- Describe anticipated specific data exclusion criteria. For example:
-    a) missing, erroneous, or overly consistent responses;
-    b) failing check-tests or suspicion probes;
-    c) demographic exclusions;
-    d) data-based outlier criteria;
-    e) method-based outlier criteria (e.g. too short or long response times). -->
+## Planned Sample*
+            <!-- 
+1. If applicable, describe pre-selection rules.
+2. Indicate where, from whom and how the data will be collected.
+3. Justify planned sample size
+4. If applicable, you can upload a file related to your power analysis here (e.g., a protocol of power analyses from G*Power, a script, a screenshot, etc.).
+5. Describe data collection termination rule. -->
 
-Enter your response here.
+Enter your response here
 
+## Exclusion Criteria*
+            <!--  Describe anticipated specific data exclusion criteria. For example:
+1. Missing, erroneous, or overly consistent responses;
+2. Failing check-tests or suspicion probes;
+3. Demographic exclusions;
+4. Data-based outlier criteria;
+5. Method-based outlier criteria (e.g. too short or long response times). -->
 
-## Procedure
-<!-- Describe all manipulations, measures, materials and procedures including the order of presentation and the method of randomization and blinding (e.g., single or double blind), as in a published Methods section. -->
+Enter your response here
 
-<!-- Optional: Set fail-safe levels of exclusion at which the whole study needs to be stopped, altered, and restarted. You may pre-determine what proportion of excluded participants will cause the study to be stopped and restarted. If applicable, you can refer to any files related to your methods and procedure here (e.g., a paper describing a scale you are using, experimenter instructions, etc.) -->
+## Procedure*
+            <!--  Describe all manipulations, measures, materials and procedures including the order of presentation and the method or randomization and blinding (e.g., single or double blind), as in a published Methods section. -->
 
 Enter your response here.
 
@@ -72,46 +82,47 @@ Enter your response here.
 # Analysis plan
 
 ## Confirmatory analyses 
-<!-- Describe the analyses that will test the predictions from the hypotheses section A. Include: 
-    a) the relevant variables and how they are calculated
-    b) the statistical technique
-    c) each variable’s role in the technique (e.g., IV, DV, moderator, mediator, covariate)
-    d) rationale for each covariate used, if any.
-
-If using techniques other than null hypothesis testing (for example, Bayesian statistics), describe your criteria and inputs toward making an evidential conclusion, including prior values or distributions. -->
+<!-- Describe the analyses that will test the first main prediction from the hypotheses section. Include:
+1. The relevant variables and how they are calculated;
+2. The statistical technique;
+3. Each variable’s role in the technique (e.g., IV, DV, moderator, mediator, covariate);
+4. Rationale for each covariate used, if any;
+5. If using techniques other than null hypothesis testing (for example, Bayesian statistics), describe your criteria and inputs toward making an evidential conclusion, including prior values or distributions.  -->
 
 Enter your response here.
 
 
-## Details of analysis
-<!-- Specify contingencies and assumptions, such as: 
-    a) Method of correction for multiple tests
-    b) The method of missing data handling (e.g., pairwise or listwise deletion, imputation, interpolation)
-    c) Reliability criteria for item inclusion in scale
-    d) Anticipated data transformations
-    e) Assumptions of analyses, and plans for alternative/corrected analyses if each assumption is violated
-    f) If applicable, you can refer to any files here that are related to your analyses (e.g., syntaxes, scripts, etc.). -->
+## Recommended Elements
+<!--          Specify contingencies and assumptions, such as:
+1. Methods of correction for multiple tests.
+2. The method of missing data handling (e.g., pairwise or listwise deletion, imputation, interpolation).
+3. Reliability criteria for item inclusion in scale.
+4. Anticipated data transformations.
+5. Assumptions of analyses, and plans for alternative/corrected analyses if each assumption is violated.             
+6. Optionally, upload any files here that are related to your analyses (e.g., syntaxes, scripts, etc.).
+                1. You may attach up to 5 files to this question. You may attach files that you already have in this OSF project, or upload a new file from your computer. Uploaded files will automatically be added to this project so that they can be registered. -->
 
 Enter your response here.
 
 
 ## Existing data
-<!-- Has data collection begun for this project? -->
-
-**Data collection has not begun.**
-
-**Data collection is underway or complete but I have not yet looked at the data.**
-
-**Data collection is underway or complete and I have looked at the data.**
+<!-- Has data collection begun for this project?   
+Please choose: -->
+   1. No, data collection has not begun
+   2. Yes, data collection is underway or complete.
+  <!--  If data collection has begun, have you looked at the data? (required)
+        a. Please choose: -->
+   1. Yes
+   2. No 
 
 
-## Project schedule (optional)
+## Project schedule
 <!-- Provide the (estimated) start and end dates for this project. -->
 
 Enter your response here.
 
 
-## Further comments (optional)
+## Further comments
 
 Enter your response here.