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Filter Cart

The Filter Cart provides a powerful way to query and access data for which you may be interested.  

A few points related to the filter cart are important to understand with the NDA Query/Filter implementation: 

First, the filter cart is populated asyncronously.  So, when you run a query, it may take a moment to populate but this will happen in the background so you can define other queries during this time.  

When you are adding your first filter, all data associated with your query will be added to the filter cart (whether it be a collection, a concept, a study, a data structure/elment or subjects). Not all data structures or collections will necessarily be displayed.  For example, if you select the NDA imaging structure image03, and further restrict that query to scan_type fMRI, only fMRI images will appear and only the image03 structure will be shown.  To see other data structures, select "Find All Subject Data" which will query all data for those subjects. When a secord or third filter is applied, an AND condition is used.  A subject must exist in all filters.  If the subject does not appear in any one filter, that subjects data will not be included in your filter cart. If that happens, clear your filter cart, and start over.  

It is best to package more data than you need and access those data using other tools, independent of the NDA (e.g. miNDAR snapshot), to limit the data selected.  If you have any questions on data access, are interested in using avaialble web services, or need help accessing data, please contact us for assistance.  

Frequently Asked Questions



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Leiter International Performance Scale Third Edition

Download Definition as
Download Submission Template as
Element NameData TypeSizeRequiredDescriptionValue RangeNotesAliases
subjectkeyGUIDRequiredThe NDAR Global Unique Identifier (GUID) for research subjectNDAR*
src_subject_idString20RequiredSubject ID how it's defined in lab/projectrandid
interview_dateDateRequiredDate on which the interview/genetic test/sampling/imaging/biospecimen was completed. MM/DD/YYYYRequired field
interview_ageIntegerRequiredAge in months at the time of the interview/test/sampling/imaging.0 :: 1260Age is rounded to chronological month. If the research participant is 15-days-old at time of interview, the appropriate value would be 0 months. If the participant is 16-days-old, the value would be 1 month.
sexString20RequiredSex of the subjectM;FM = Male; F = Femalegender
medprob_eyecolorblString50RecommendedColor blindness?
psysoc_62String5RecommendedVision problems:Yes;No
gradeString50RecommendedCurrent Grade
leit_full_sssIntegerRecommendedNonverbal IQ Sum of Scaled Scores999=missing
leit_full_iqIntegerRequiredNonverbal IQ Score999=missing
leit_full_perFloatRecommendedNonverbal IQ Percentile
leit_brief_sssIntegerRecommendedBrief IQ Sum of Scaled Scores999=missingiqt_q4e1
leit_brief_iqIntegerRequiredBrief IQ Score999=missingiqt_q4e2
leit_fg_ssIntegerRecommendedFigure Ground (FG) Subtest Scaled Scoreiqt_q4a2
leit_fc_ssIntegerRecommendedForm Completion (FC) Subtest Scaled Scoreiqt_q4b2
leit_so_ssIntegerRecommendedSequential Order (SO) Scaled Scoreiqt_q4c2
leit_rp_ssIntegerRecommendedRepeated Patterns (RP) Scaled Scoreiqt_q4d2
leit_c_ssIntegerRecommendedClassification Analogies (CA ) Scaled Score
leit_vp_ssIntegerRecommendedVisual Pattern (VP) Scaled Score
leit_fg_rawIntegerRecommendedFG Raw Score0 :: 33; 999999 = Missing valueiqt_q4a1
leit_fc_rawIntegerRecommendedFC Raw Score (Sum of FC 1-8 and FC 9-15 Raw Scores)0 :: 36; 999999 = Missing valueiqt_q4b1
leit_so_rawIntegerRecommendedSO Raw Score0 :: 51; 999999 = Missing valueiqt_q4c1
leit_rp_rawIntegerRecommendedRP Raw Score0 :: 27; 999999 = Missing valueiqt_q4d1
leit_c_rawIntegerRecommendedCA Raw Score0 :: 32; 999999 = Missing value
leit_vp_rawIntegerRecommendedVP Raw score0 :: 29; 999999 = Missing value
leit_as_ssIntegerRecommendedAttention Sustained AS Scaled score
leit_fm_ssIntegerRecommendedForward Memory FM Scaled score
leit_rm_ssIntegerRecommendedReverse Memory RM Scaled score
leit_nsic_ssIntegerRecommendedNonverbal Stroop Incongruent Correct NSic Scaled score
leit_nscc_ssIntegerRecommendedNonverbal Stroop Congruent Correct NScc Scaled score
leit_nseff_ssIntegerRecommendedNonverbal Stroop Effect NSeff Scaled score
leit_as_rawIntegerRecommendedAS Raw score0 :: 20; 999999 = Missing value
leit_fm_rawIntegerRecommendedFM Raw score0 :: 20; 999999 = Missing value
leit_rm_rawIntegerRecommendedRM Raw score0 :: 20; 999999 = Missing value
leit_nsic_rawIntegerRecommendedNSic Raw score0 :: 20; 999999 = Missing value
leit_nscc_rawIntegerRecommendedNScc Raw score0 :: 20; 999999 = Missing value
leit_nseff_rawIntegerRecommendedNSeff Raw score0 :: 20; 999999 = Missing value
leit_as_psssIntegerRecommendedAS Processing Speed Scaled score
leit_fm_nmssIntegerRecommendedFM Nonverbal Memory Scaled score
leit_rm_nmssIntegerRecommendedRM Nonverbal Memory Scaled score
leit_nsic_psssIntegerRecommendedNsic Processing Speed Scaled score
leit_nm_sssIntegerRecommendedNonverbal Memory Sum of Scaled Scores
leit_nm_csIntegerRecommendedNonverbal Memory Composite Score
leit_nm_percIntegerRecommendedNonverbal Memory Percentile
leit_nm_cirString20RecommendedNonverbal Memory Confidence Interval range
leit_ps_sssIntegerRecommendedProcessing Speed Sum of Scaled Scores
leit_ps_csIntegerRecommendedProcessing Speed Composite Score
leit_ps_percIntegerRecommendedProcessing Speed Percentile
leit_ps_cirString20RecommendedProcessing Speed Confidence Interval range
daysrzIntegerRecommendeddays since randomizationvisitdt
siteString101RecommendedSiteStudy Sitesiteid
visitString50RecommendedVisit nameint1
completedIntegerRecommendedChecks if completed0::30=No; 1=Yes; 2=Yes, lost; 3=Unverifiedformcomp
subtrial_nameIntegerRecommendedName of subtrial1::31=Crossover; 2=Guanfacine; 3= Open Label MPH
leit_brief_perFloatRecommendedBrief IQ Percentileiqt_q4e3
iqt_q4e4FloatRecommendedLEITER: Brief IQ - % Confidence Interval
iqt_q4fyFloatRecommendedLEITER: Brief IQ mental age years
iqt_q4fmFloatRecommendedLEITER: Brief IQ mental age months
Data Structure

This page displays the data structure defined for the measure identified in the title and structure short name. The table below displays a list of data elements in this structure (also called variables) and the following information:

  • Element Name: This is the standard element name
  • Data Type: Which type of data this element is, e.g. String, Float, File location.
  • Size: If applicable, the character limit of this element
  • Required: This column displays whether the element is Required for valid submissions, Recommended for valid submissions, Conditional on other elements, or Optional
  • Description: A basic description
  • Value Range: Which values can appear validly in this element (case sensitive for strings)
  • Notes: Expanded description or notes on coding of values
  • Aliases: A list of currently supported Aliases (alternate element names)
  • For valid elements with shared data, on the far left is a Filter button you can use to view a summary of shared data for that element and apply a query filter to your Cart based on selected value ranges

At the top of this page you can also:

  • Use the search bar to filter the elements displayed. This will not filter on the Size of Required columns
  • Download a copy of this definition in CSV format
  • Download a blank CSV submission template prepopulated with the correct structure header rows ready to fill with subject records and upload

Please email the The NDA Help Desk with any questions.

Distribution for DataStructure: leiter301 and Element:
Chart Help

Filters enable researchers to view the data shared in NDA before applying for access or for selecting specific data for download or NDA Study assignment. For those with access to NDA shared data, you may select specific values to be included by selecting an individual bar chart item or by selecting a range of values (e.g. interview_age) using the "Add Range" button. Note that not all elements have appropriately distinct values like comments and subjectkey and are not available for filtering. Additionally, item level detail is not always provided by the research community as indicated by the number of null values given.

Filters for multiple data elements within a structure are supported. Selections across multiple data structures will be supported in a future version of NDA.