Prescreeners are used to select participants for a prolific_study that meet certain characteristics.
In most cases, this selection is done with regard to the answers the participants gave in a survey conducted by Prolific across all its members.
Choosing a prescreening variable
At the moment, there are 265 variables which can be used to recruit specific subgroups from Prolific.
To obtain a list of all available prescreening variables, use
table_of_prescreeners <-
prescreeners(prolific_api_access)
where prolific_api_access is an api_access object with a valid api_token.
A prescreening variable is determined by the title field of the prolific_prescreener object.
To be valid, this title must appear in the title column
of the resulting table_of_prescreeners.
Setting constraints for a particular prescreening variable
The constraints are specified in the form
name_1 = value_1,
...,
name_n = value_n
or
name_1,
...,
name_n
For most prescreeners, the values
value_1 ... value_n
are logical values to select participants that gave a certain answer in some pre-screening question.
In this case, specifying
name_i = TRUE
for the prescreener means that participants who gave answer name_i are eligible for the study.
However, keep in mind there are some prescreeners that work in the opposite way, e.g. to specify a list of participants to be exluded
(see the sections 'Ex- or include a list of specific participants' and 'Ex- or include all participants from previous studies' below).
For all cases where the values
value_1 ... value_n
are logical,
name_1,
...,
name_n
is an equivalent shortcut for
name_1 = TRUE,
...,
name_n = TRUE
.
Yet, the constraint values are not always of type logical.
In particular, there are prescreeners that allow to select participants lying within a certain range of a numerical variable.
For example, this is the case when selecting participants who are in a certain age bracket, where lower and upper boundary for a person's age are specified in the constraints.
In this case,
value_1, ..., value_n
in the above specification need to be numeric as well, and must be named e.g. as in
min_age = 50,
max_age = 60
for selecting participants between age 50 and 60 for the study.
The names
name_1, ..., name_n
are always taken literally. This means that they are not automatically evaluated.
Enclosing a name in an eval() command forces it to be evaluated rather than taken literally.
This is important for example in cases where the categories are stored in a list
(see the section 'Examples for prolific_prescreeners' for an example).
To obtain the list of possible constraints for a particular prescreener with a valid title "the_title" as described above, use
table_of_constraints <-
prescreeners(prolific_api_access,
filter=expression(title==c("the_title")),
show_full=TRUE)
The names
name_1, ..., name_n
of the constraints list should come from a single (typically the name) column of the resulting table_of_constraints,
the respective list elements represent the values that participants have to meet.
To make this a bit clearer, the following section provides examples for setting up prescreening requirements.
Examples for prolific_prescreeners
- Nationality requirements
For example, a study can be set to exclusively target participants who currently live in the UK or the USA by using
residential_prescreener <- prolific_prescreener(
title = "Current Country of Residence",
"United Kingdom", "United States"
)
or equivalently
list_of_countries <- list(
country_1="United Kingdom",
country_2="United States") residential_prescreener <- prolific_prescreener(
title = "Current Country of Residence",
eval(list_of_countries$country_1),
eval(list_of_countries$country_2)
)
Note that "Current Country of Residence" appears in the title column of table_of_prescreeners, and
"United Kingdom" as well as "United States" appear in the name column
of the resulting table_of_constraints described in the previous sections.
Furthermore, note the use of eval() to force evaluation of list_of_countries$country_1 and list_of_countries$country_2.
Age requirements
Similarly, selecting participants who fall in the age range between 50 and 60 can be achieved through
age_prescreener <- prolific_prescreener(
title = "Age",
"min_age" = 50,
"max_age" = 60
)
Ex- or include a list of specific participants
Specific participants can be in- or excluded from a study, for example if they participated in previous studies.
This can be done in form of black- or whitelists.
Consider two fictional participants
with Prolific id's
111 and 222.
These can be specifically excluded by using the exclusion list defined by
exclude_list_participants <- prolific_prescreener(
title = "Custom Blacklist",
"111","222"
)
To exclusively recruit exactly these two participanty, use the include list defined by
include_list_participants <- prolific_prescreener(
title = "Custom Whitelist",
"111","222"
)
Note: The IDs for these constraints need to be valid Prolific IDs when creating a study. The above example for fictional IDs 111 and 222 will therefore always fail.
Ex- or include all participants from previous studies
You can not only blacklist single participants, but also the group(s) of participants who
participated in of one or multiple of your previous studies.
To exclude all participants from two fictional studies with IDs ABC and DEF, specify the prescreener
exclude_list_studies <- prolific_prescreener(
title = "Exclude participants from previous studies",
"ABC","DEF"
)
To exclusively recruit participants from these studies, use
include_list_studies <- prolific_prescreener(
title = "Include participants from previous studies",
"ABC","DEF"
)
Note: The IDs for these constraints need to be valid Study IDs when creating a study. The above example for fictional IDs ABC and DEF will therefore always fail.