Data were collected between December 8, 2013 and July 26, 2014.
The SAPA Project is a collaborative online data collection tool for assessing psychological constructs across multiple domains of personality. These domains – temperament, cognitive abilities, and interests – have been chosen based on historical and current prominence in the field of individual differences research. The primary goal of the SAPA Project is to determine the combined and independent structures of each of these domains based on the collection of large, cross-sectional, online samples. Secondary goals include (1) the identification of additional domains (e.g., motivation, character) which may also provide insight into the ways that individuals differ; and (2) an improved understanding of the demographic and psychographic correlates of individual differences in personality.
The data described here were collected in order to evaluate the structure of personality constructs in the temperament domain. In the context of modern personality theory, these constructs are typically construed in terms of the Big Five (Conscientiousness, Agreeableness, Neuroticism, Openness, and Extraversion). While several large scale studies of personality have been conducted using trait descriptive adjectives and nouns (see  and ), relatively few attempts have been made to evaluate large sets of phrased items even though phrased item types are more typically used in personality assessments.
Items from 92 public-domain personality scales were included in this data set; most of these were chosen explicitly because they are among the more widely-used personality measures. No a priori hypotheses were made regarding the underlying structure of these items, nor should it be expected that these items represent an unbiased or representative snapshot of human personality; the structure of these items will, to some extent, reflect the shared characteristics of the scales from which they were taken.
Participants (N = 23,681) completed the online survey in exchange for feedback about various aspects of their personality. No active advertisements or marketing efforts were used to attract participants for this data collection; web traffic statistics (collected through Google Analytics) suggest that participants who did not come to the website directly were directed to it through links from various other websites about personality, personality research, general psychology topics, and psychometrics. Many of these websites were academic/educational in nature.
Many demographic and psychographic variables are included in the data. These include: gender (64% of the participants were female); age (see Figure 1); marital status (see Table 1); body mass index (see Figure 2); country (172 countries were represented in total; 69.5% of participants were from the United States, and 12 countries had more than 100 participants); state/region (for 32 countries); zip code (postal codes for U.S. participants only); race/ethnicity (see Table 2); educational attainment level (see Table 3); employment status (see Table 4); parental educational attainment level (for 1 or 2 parents); and parental field of employment (for 1 or 2 parents). Participants were not required to provide any of these data except age and gender.
|Widowed & Remarried||769|
|Divorced & Single||300|
|Divorced & Remarried||123|
|Widowed & Single||14|
|Two Or More Ethnicities||809|
|Other Pacific Islander||22|
|Less than 12 years||2,725|
|High school graduate||1,913|
|Currently in college/university||8,457|
|Some college/university, but did not graduate||1,249|
|Currently in graduate or professional school||1,108|
|Graduate or professional school degree||2,007|
|Currently a student||8,073|
|Not employed, seeking work||892|
Items from eight sets of self-report personality scales were administered. Seven of these are based on items from the International Personality Item Pool [7; 9]: the 100 IPIP items corresponding to the Big Five factor markers , the 100 items of the Big Five Aspect Scales , the 240 items of the IPIP-HEXACO inventory , the 48 items of the Questionnaire Big Six scales , the 300 items of the IPIP-NEO , the 127 items of the IPIP-Multidimensional Personality Questionnaire [“MPQ” 8; 17], and the 40 items of the Plasticity/Stability scales . The eighth set of scales included 79 items from the Eysenck Personality Questionnaire – Revised . Note that the format of these items was modified to match that of the IPIP items and that the 21 “lie” scale items were intentionally omitted. Administration of these scales also implies the administration of several other measures which are abbreviations of these scales, including the 24 and 36 item Questionnaire Big Six scales , the 50 item IPIP scales corresponding to the Big Five factor markers , and the 20 item “mini-IPIP” scales .
The 1,034 items from these measures contain 338 duplicates, resulting in a total set of 696 unique items. Of these, 473 items are in only one set of scales, 126 items are included in two sets of scales, 54 items are in three, 22 items in four, 17 items in five, and 4 items are in six of the seven sets of IPIP-based scales (“Have little to say”, “Worry about things”, “Like order”, and “Have a rich vocabulary”). All of the items were administered with the same six response options (“Very Inaccurate”, “Moderately Inaccurate”, “Slightly Inaccurate”, “Slightly Accurate”, “Moderately Accurate”, “Very Accurate”).
The items were administered using the Synthetic Aperture Personality Assessment (“SAPA”) technique , a variant of matrix sampling procedures discussed by Lord . This method produces data which contain “massive missingness” by design . This missingness qualifies for classification as missing completely at random [“MCAR”, 10] and it is further described as massively missing because the mean level of missingness by participant was approximately 86%. The personality items were presented to participants in random order, and participants responded to as many items as they wished. The mean number of personality items to which participants responded was 86.1 (sd = 58.7; median = 71). The number of items administered to each participant was procedurally independent of participant response characteristics. Participants were encouraged to complete approximately 100 items but were able to complete up to 330. The number of administrations for each item varied considerably (median = 2554; m = 2931; sd = 781) as did the number of pairwise administrations between any two items in the set (median = 519; m = 528; sd = 117).
The available data are presented largely as they were collected with only two exceptions:
- Partial removal of data collected from participants who completed the survey more than once in a single browser session. This was done by assigning participants a random user ID that was persistant as long as their current browser session remained active. In those cases where more than 1 response set was entered in a single browser session, only the first response set was kept.
- Removal of participants with self-reported ages younger than 14 and older than 90. The survey is not intended for participants younger than 14. Self-reported ages over 90 were removed on the grounds that they were deemed to be unlikely.
No personally identifying information were collected from participants in these data.
3 Dataset description
For use with R statistical software systems , the data file is named: ‘sapaTempData696items08dec2013thru26jul2014.rdata’
The data file is named to indicate the domain (temperament), the number of items included (696), and the time period over which the data were collected (08dec2013 through 26jul2014). The file can be found at: http://dx.doi.org/10.7910/DVN/SD7SVE
The data file includes four objects. The most pertinent of these is the raw data object (‘sapaTem-pData696items08dec2013thru26jul2014’). The remaining three objects are helper files for data analysis: ‘ItemInfo696’ is a data dictionary which provides the text for each of the temperament items and a listing of the scales with which it is associated, ‘ItemLists’ is a list object that provides an index of all the temperament items associated with each measure, and ‘superKey696’ is a scoring matrix for the many personality scales which can be scored based on these data.
The data are also available in a csv format, available at the same location listed above. Each of the four objects described above has been saved as a separate csv file.
Self-report, cross-sectional survey data from 23,681 participants.
Format names and versions
The data are stored as an rdata file (approximately 4.9 MB). This file includes the four objects described above: the main data object, ‘sapaTempData696items08dec2013thru26jul2014’, as well as ‘ItemInfo696’, ‘ItemLists’, and ‘superKey696’. It should be noted that several of the scales in these measures require reverse coding of some items; see the original documentation of each measure for more details.
In addition to the rdata file containing these four objects, there is also an associated text file that provides full information on the demographic codes (‘demographic codes.txt’).
In addition to the authors, Jason French, Lorien Elleman and Zara Wright contributed by helping to maintain the website and increase its visibility.
All aspects of the survey and website were written in English. Data collected about the website through Google Analytics suggests that some participants used browser-based translation software, but no specifics are available about the extent and effect of these translations.
The data have been deposited under the open license CC0 (Public Domain Dedication).
The data are freely available for use with appropriate citation.
The data were published on Dataverse and are located at http://dx.doi.org/10.7910/DVN/SD7SVE.
The dataset was published on 13/04/2015.
4 Reuse potential
The data are well-suited for many types of structural and correlational analyses of personality. These might include evaluations based on one of the many measures independently, the ways in which these measures relate to one another, exploratory evaluations of their shared structure, and evaluations of structural relationships across constructs in various groups of participants (e.g., based on age, gender, country/region, educational attainment levels, etc.). The large number of both participants and items also make it possible to construct novel scales based on the empirical correlations between items and criterion variables (see the ‘bestScales’ function and related help pages in the psych package  for examples of these techniques). Additional, non-overlapping data sets from the SAPA Project are also available for use; contact the authors for more information.
The authors declare that they have no competing interests.