This paper presents a tutorial for an R package qsort to analyse Q-sort data as commonly used in developmental psychology research. R (R Core Team 2018) is a free software environment for statistical computing (https://CRAN.R-project.org), and R packages are collections of functions and datasets developed by the R community that extend base R functionalities. Due to its open access philosophy, R packages are an important tool for reproducible research. Bellow we describe how to install this package and how to use it to score Q-sort data. For the moment, the qsort package includes descriptions and scoring procedures for four different Q-sets: Attachment Q-set (version 3.0) (Waters 1995), California Child Q-set (J. H. Block and Block 1969), Maternal Behaviour Q-set (version 3.1) (Pederson, Moran, and Bento 1999), and Preschool Q-set (Baumrind (1968), revised by Wanda Bronson). These Q-sets have been used extensively in developmental psychologicy research and a quick search on google scholar shows that in the past five years these references have been cited 500 plus times. Nevertheless, the core function and datasets of this package can be easily updated to accommodate additional Q-sets. If readers of this paper would like to provide additional Q-set descriptions and scoring procedures, please feel free to contact the authors. We will gladly update the package to expand its capabilities.
Q-sets are instruments, consisting of a comprehensive large set of items (e.g., sentences, images, photos), used to describe subjective views on a specific research domain in a way suitable for quantitative data analysis (J. Block 1961; John, Montgomery, and Tate 2014; Vaughn, Santos, and Coppola 2014). In psychological research a broad range of different Q-sets abound. Their items are usually sentences, printed on separate cards, describing personality and behavioural characteristics. For instance, the California Child Q-set (J. H. Block and Block 1969) is a 100 items Q-set that includes descriptions such as: “Is curious and exploring, eager for new experiences” (item 40), “Appears to have a high intellectual capacity” (item 68), or “Has an active fantasy life” (item 97).
The scoring procedure of Q-set items, usually referred to as Q-sorting, consists of rank ordering items into categories/piles based on their salience as descriptors of an individual. Q-sorting can be done by participants to describe themselves, or by observers who describe the personality and behaviour of others. The number of piles/categories and the number of cards in each pile is typically required to conform to a specific distribution (e.g., quasi-normal, rectangular). Forcing a distribution reduces response biases and makes Q-sorting an ipsative procedure (J. Block 1961); that is, observers have to compare the salience of the different items relative to each other- contrasting, for instance, with Likert type scales where all items can hypothetically receive the same score.
For example, in a 9-category sort of a Q-set with 90 items: (a) least characteristic items for a specific participant would be included in categories 1 to 3 (e.g., most uncharacteristic, very uncharacteristic, and uncharacteristic of participant respectively) and score 1 to 3 accordingly; (b) characteristic items would score 7, 8 or 9 (e.g., moderately characteristic, very characteristic, most characteristic); (c) while low salience items would score 4, 5 or 6 (e.g., somewhat uncharacteristic, neither characteristic nor uncharacteristic, somewhat characteristic). If the sort of a 90 items Q-set follows a rectangular distribution, all the categories from 1 to 9 should include 10 items (i.e., 10 items score 1, 10 items score 2, etc.); if the sort follows a quasi-normal distribution, the distribution of items could be as follows: 4, 7, 10, 15, 18, 15, 10, 7, 4 in categories 1 to 9 respectively (i.e., 4 items score 1, 7 items score 2, etc.). (Vaughn, Santos, and Coppola 2014) (p. 179-180) present a series of figures that illustrate nicely sorting distributions and the sorting process.
(J. H. Block and Block 1980) introduced the use of criterion Q-sorts to compute individual scores for specific psychological domains. To construct a criterion Q-sort, experts in a domain are instructed to sort all Q-set items according to a fixed distribution, having in mind a hypothetical person at the extreme high end of that domain (e.g., the most social competent children). Expert Q-sorts are then aggregated and each item receives a criterion score that corresponds to the average score of all experts. Consequently, (a) items with the lowest criterion scores correspond to personality and behavioural characteristics that are more negatively related with the domain, (b) items with the highest criterion scores correspond to characteristics that are more positively related with the domain, and (c) items in the middle of the distribution are minimally related with the domain. Following (J. H. Block and Block 1980), an individual’s score on a domain corresponds to the Pearson correlation between her or his Q-sort description and the criterion Q-sort of that domain.
Alternatively to correlating individual Q-sorts with criterion Q-sorts, one may compute scores for scales created from subsets of Q-set items. Usually these scales are designed by grouping items that correlate strongly with some outcome variable, and scale scores correspond to the average of those items’ scores (inverting items stated negatively when needed).
qsort is a package that allows scoring Q-sort data, using criteria sorts and derived scales from subsets of items. This package includes descriptions and scoring procedures for four different Q-sets:
The R package qsort is available at https://CRAN.R-project.org/. It can be installed and loaded as follows:
The R package qsort is also available at https://github.com/joaordaniel/qsort.
qsort package includes 7 objects:
qsort_score() a function for scoring Q-sort data;ex_qsort a list containing four example data frames for
the referred Q-sets;print_cards() a function for printing Q-set item
cards.qset_aqs a data frame containing the Attachment Q-set
(aqs; version 3.0);qset_ccq a data frame containing the California Child
Q-set (ccq);qset_mbqs a data frame containing the Maternal
Behaviour Q-set (mbqs; version 3.1);qset_pq a data frame containing the Preschool Q-set
(pq);Each data frame object starting with qset_ contains data
specific of a certain Q-set, including items’ descriptions. For
example:
head(qset_ccq)
#> item scomp_c sest_c egores_c egocont_c sdes_c shields_s shields_s_inv
#> 1 1 4.0 4.0 3.0 5.3 4.4 <NA> 0
#> 2 2 7.0 6.6 7.0 3.3 7.6 <NA> 0
#> 3 3 7.1 7.6 7.7 6.7 7.7 emreg 0
#> 4 4 8.4 6.7 7.0 4.0 8.0 <NA> 0
#> 5 5 8.9 7.9 6.3 3.7 6.4 <NA> 0
#> 6 6 8.3 6.3 6.3 3.7 7.9 <NA> 0
#> description
#> 1 Prefers nonverbal methods of communication.
#> 2 Is considerate of other children.
#> 3 Is warm and responsive.
#> 4 Gets along well with other children.
#> 5 Is admired and sought out by other children.
#> 6 Is helpful and cooperative.Each of these datasets (qset_aqs, qset_ccq,
qset_mbqs, qset_pq) includes:
item);description);In these datasets, column names ending in _c refer to
criteria sorts, while column names ending in _s refer to
scales. When scales exist, column names ending in _inv
indicate items’ scores to be inverted. All criteria scores in the
datasets are scored from 1 to 9, with lower (higher) scores
corresponding to characteristics negatively (positively) related with
the domain being evaluated.
Besides the item, and description columns,
the qset_aqs data frame includes:
sec_c);dep_c);pederson_s; comp- compliance, sb- secure base, fd-
fussy/difficult, epc- enjoys physical contact, as- affective
sharing);posada_s; sim- smooth interactions with mother, pcm-
physical contact with mother, ioa- interactions with other adults, pm-
proximity to mother).The qset_ccq data frame includes scores of five criteria
Q-sorts:
scomp_c);sest_c);egores_c)egocont_c);sdes_c).The data for this dataset was retrieved from Waters et al. (1985) and from Brian Vaughn (personal communication; ego-resiliency and ego-control criteria scores).
The qset_mbqs data frame includes scores of one
criterion Q-sort:
sens_c).The data for this dataset was retrieved from Pederson, Moran, and Bento (1999).
Finally the qset_pq data frame includes scores of three
criteria Q-sorts:
scomp_c);sest_c);sdes_c).The data for this dataset was retrieved from Waters et al. (1985). For more information about about the Q-set data frames read the documentation.
ex_qsort objectqsort package includes example Q-sort datasets for the four different
Q-sets (ex_qsort):
ex_qsort$aqsex_qsort$ccqex_qsort$mbqsex_qsort$pqwith 10 rows each (i.e., 10 participants) and a varying number of
columns depending on the number of Q-set items. The help function of R
package qsort lists the details of these examples: see
?ex_qsort.
qsort_score functionThe qsort_score function uses the criteria scores and/or
scales from Q-set datasets to compute scores for Q-sort data. This
function takes five arguments:
where x is a data frame in wide format containing the
data to be analysed: each participant in one row, with scores for Q-set
items in different columns, ordered sequentially from the first item
(e.g., ccq1 as in ex_qsort$ccq) to the last
(e.g., ccq100 as in ex_qsort$ccq), with no
other columns in between.
It is possible to import different types of data files into R (e.g.,
.txt, .csv, .xlsx, .sav) and several on-line tutorials detail how to
accomplish this task. If you are using R through RStudio (R Studio Team 2016) see for example https://support.rstudio.com/hc/en-us/articles/218611977-Importing-Data-with-RStudio}
for further details. Alternatively, we suggest checking
import function of rio package (Becker et al. 2018) (see https://CRAN.R-project.org/package=rio for further
details).
The second argument of the qsort_score function takes a
Q-set data frame such as the data frame objects provided with the
package and explained above. The qsort_score function will
compute scores for the corresponding Q-set.
The third argument, item1, must correspond to the name
of the column in x that contains the scores of the first
item of the selected Q-set. R is case sensitive, so you need to type the
exact name of this column. item1 is a character vector and
should be in quotes.
The argument qsort_length must be used to specify the
number of items in the qsort.
The arguments subj_id and group_id of
theqsort_score function have a default value of NULL and do
not have to be mandatorily specified (the function will compute scores
either way). We included them in the function if users want the output
data frame of the qsort_score function to include a
subjects’ identification variable (e.g., participant as in
ex_qsort) and/or a groups’ identification variable (e.g.,
classroom as in ex_qsort). Again, names passed into these
arguments should correspond exactly to column/variable names in data
frame x. These identification variables can be particularly
handy if you later want to merge/join different datasets together - join
functions of the dplyr package (H. Wickham et al.
2018) are quite useful for this (see for example H. Wickham and Grolemund (2017), pp. 178 onwards
for further details; or http://r4ds.had.co.nz/relational-data.html#outer-join
for an online version of the same text). The help function of R package
qsort lists the details of qsort_score function: see
?qsort_score.
The following applies the qsort_score function to
ex_qsort$ccq and stores the output data frame in
data_ccq:
data_ccq <- qsort_score(x = ex_qsort$ccq, qset_ccq, qsort_length = 100, item1 = "ccq1", subj_id = "participant", group_id = "classroom")
data_ccq
#> participant classroom scomp_c sest_c egores_c egocont_c sdes_c
#> 1 1 1 -0.074 -0.093 -0.145 -0.012 -0.137
#> 2 2 1 -0.023 0.008 0.090 0.157 0.053
#> 3 3 1 0.092 0.086 0.112 -0.021 0.132
#> 4 4 1 -0.105 -0.113 -0.182 -0.128 -0.160
#> 5 5 1 -0.010 -0.039 -0.092 -0.092 -0.053
#> 6 6 2 -0.104 -0.079 -0.042 0.156 -0.089
#> 7 7 2 0.051 0.079 0.168 0.169 0.124
#> 8 8 2 0.049 0.066 0.153 0.185 0.118
#> 9 9 2 -0.024 -0.007 0.009 -0.026 0.007
#> 10 10 2 -0.039 -0.046 -0.033 0.098 -0.042
#> partial_scomp_c partial_sest_c partial_egores_c partial_egocont_c
#> 1 0.113 0.047 -0.054 -0.007
#> 2 -0.160 -0.073 0.086 0.155
#> 3 -0.061 -0.053 -0.002 -0.026
#> 4 0.088 0.047 -0.089 -0.123
#> 5 0.085 0.012 -0.090 -0.090
#> 6 -0.054 -0.004 0.068 0.160
#> 7 -0.139 -0.053 0.121 0.166
#> 8 -0.130 -0.068 0.101 0.182
#> 9 -0.069 -0.026 0.004 -0.026
#> 10 -0.003 -0.019 0.005 0.100
#> shields_s_emreg
#> 1 4.3
#> 2 5.9
#> 3 5.1
#> 4 3.7
#> 5 4.4
#> 6 5.7
#> 7 5.9
#> 8 5.9
#> 9 5.0
#> 10 5.2The first two columns of data_ccq correspond to
participant and classroom identification
variables; the remaining columns refer to correlation values computed
from correlating individual Q-sorts with the criteria Q-sorts’ scores
(present in qsets$ccq). The names of these columns follow
the column names in the Q-set data frame (e.g., social competence-
scomp_c, self-esteem- sest_c, etc.). Variable
names are detailed in the help file of each Q-set data frame object. The
subset built-in R function can be used to select specific
columns from the output data frame:
subset(data_ccq, select = c("participant", "classroom", "scomp_c", "sest_c", "egores_c", "egocont_c"))
#> participant classroom scomp_c sest_c egores_c egocont_c
#> 1 1 1 -0.074 -0.093 -0.145 -0.012
#> 2 2 1 -0.023 0.008 0.090 0.157
#> 3 3 1 0.092 0.086 0.112 -0.021
#> 4 4 1 -0.105 -0.113 -0.182 -0.128
#> 5 5 1 -0.010 -0.039 -0.092 -0.092
#> 6 6 2 -0.104 -0.079 -0.042 0.156
#> 7 7 2 0.051 0.079 0.168 0.169
#> 8 8 2 0.049 0.066 0.153 0.185
#> 9 9 2 -0.024 -0.007 0.009 -0.026
#> 10 10 2 -0.039 -0.046 -0.033 0.098The following applies the qsort_score function to
ex_qsort$aqs, and stores the output data frame in
data_aqs:
data_aqs <- qsort_score(x = ex_qsort$aqs, qset_aqs, qsort_length = 90, item1 = "aqs1")
data_aqs
#> sec_c dep_c pederson_s_as pederson_s_comp pederson_s_epc pederson_s_fd
#> 1 -0.060 -0.019 3.667 5.500 5.0 6.071
#> 2 0.024 0.197 8.000 5.167 4.6 5.286
#> 3 0.003 -0.041 5.667 4.500 5.0 5.214
#> 4 -0.155 -0.017 4.667 4.500 5.0 5.571
#> 5 -0.021 0.079 3.000 7.333 4.4 5.929
#> 6 -0.070 -0.061 3.667 6.500 4.2 5.429
#> 7 -0.101 0.133 6.000 4.000 3.8 5.214
#> 8 -0.114 -0.062 6.333 5.167 4.0 5.571
#> 9 -0.034 0.070 6.667 4.167 4.2 5.714
#> 10 -0.197 -0.089 4.333 5.500 3.4 5.786
#> pederson_s_sb posada_s_ioa posada_s_pcm posada_s_pm posada_s_sim
#> 1 5.286 5.231 5.000 4.0 5.235
#> 2 4.643 4.231 5.714 5.2 5.353
#> 3 5.714 5.231 5.143 6.1 4.647
#> 4 5.643 3.231 4.857 4.4 4.588
#> 5 5.000 4.154 5.571 5.1 5.529
#> 6 5.143 5.385 5.429 3.7 5.412
#> 7 4.571 4.846 4.571 6.3 4.471
#> 8 5.500 4.923 3.571 5.9 4.529
#> 9 4.643 4.846 3.571 5.8 4.471
#> 10 4.071 3.692 4.714 3.6 4.588In this example subj_id and group_id
arguments were not specified. Notice that in this case we did not
specify subj_id and group_id. These are not
mandatory arguments. Both have a default value of NULL ,
and thus, the data_aqs output data frame will not include a
columns with group and subject identification codes.
The data in data_aqs refers to the correlation values
computed from correlating individual Q-sorts with the criteria Q-sorts’
scores (sec_c and dep_c), or to scales scores
(pederson_s_as to posada_s_sim). Column names
starting with pederson_ refer to Pederson and Moran (1995) Attachment Q-Set
scales, and column names starting with posada_ refer to
Posada et al. (1995) Attachment Q-Set
scales. Again, subset function can be used to select
specific columns from the output data frame.
Besides the qsort_score function, the qsort R package
also includes print_cards function. This function creates a
.pdf file where items’ descriptions are printed in separate cards. This
function takes three arguments:
where desc_col refers to the column name of the Q-set
data frame containing items’ descriptions, and dir.print to
the directory path where the .pdf file will be saved. For example:
creates a .pdf file, in in the working directory, with Attachment Q-set item cards.
The goal here was to introduce readers to an R package that computes scores for Q-sort data, using either criteria scores or scales derived from subsets of items. As far as we know, there are no other tools that perform similar tasks. Both R software and this package are freely available, making this tool easily accessible to researchers using Q-sort data. At the moment, qsort package only analyses data for four different Q-sets, but it can be easily updated to extend its capabilities to other Q-sets.
Even if the reader is not interested in the scoring options provided
by this package, the Q-sets provided can be quite useful to aggregate
Q-set items’ descriptions. Finding these descriptions is not always easy
because Q-set authors often fail to provide them in supplementary
materials. Additionaly, the print_cards() function can be
used to easily create cards with items’ descriptions for the Q-sorting
procedure.
For the moment we only include Q-sets that are widely used in developmental psychological research. We invite users to contribute with new or updated versions of existing Q-sets, criterion scores and derived scales. To contribute to this package consult the contribution guidelines at https://github.com/joaordaniel/qsort.