Special Issue: Psychometrics and Variation in Human Life History Indicators
The K-SF-42: A New Short Form
of the Arizona Life History Battery
Evolutionary Psychology
January-March 2017: 1–12
ª The Author(s) 2017
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DOI: 10.1177/1474704916676276
journals.sagepub.com/home/evp
Aurelio José Figueredo1, Rafael Antonio Garcia1, J. Michael Menke2,
W. Jake Jacobs1, Paul Robert Gladden3, JeanMarie Bianchi4, Emily Anne Patch1,
Connie J. A. Beck1, Phillip S. Kavanagh5, Marcela Sotomayor-Peterson6,
Yunfan Jiang7,8, and Norman P. Li8
Abstract
The purpose of the present article is to propose an alternative short form for the 199-item Arizona Life History Battery
(ALHB), which we are calling the K-SF-42, as it contains 42 items as compared with the 20 items of the Mini-K, the short
form that has been in greatest use for the past decade. These 42 items were selected from the ALHB, unlike those of the
Mini-K, making direct comparisons of the relative psychometric performance of the two alternative short forms a valid and
instructive exercise. A series of secondary data analyses were performed upon a recently completed five-nation crosscultural survey, which was originally designed to assess the role of life history strategy in the etiology of interpersonal
aggression. Only data from the ALHB that were collected in all five cross-cultural replications were used for the present
analyses. The single immediate objective of this secondary data analysis was producing the K-SF-42 such that it would
perform optimally across all five cultures sampled, and perhaps even generalize well to other modern industrial societies
not currently sampled as a result of the geographic breadth of those included in the present study. A novel method, based
on the use of the Cross-Sample Geometric Mean as a criterion for item selection, was used for generating such a crossculturally valid short form.
Keywords
life history theory, psychometrics, cross-cultural research
Date received: May 07, 2016; Accepted: September 29, 2016
The Psychometric Assessment of Life History (LH)
LH theory is a mid-level evolutionary theory governing optimal resource allocation among different components of fitness.
The theory implicitly relies on regarding both the parent and
the offspring as partially interchangeable vehicles for the ultimate function of genetic propagation. Selection upon the genome therefore allocates bioenergetic and material resources
among those dedicated to parental survival (somatic effort) and
those dedicated to offspring production and preservation
(reproductive effort). Sexually reproducing organisms further
partition reproductive effort among those resources needed for
obtaining and retaining sexual partners (mating effort) and
those needed for the longer term survivorship of the offspring
produced (parental/nepotistic effort).
Although LH selection is ultimately based on genetic propagation, selection acts by shaping the proximate adaptive
1
Department of Psychology, College of Science, University of Arizona,
Tucson, AZ, USA
2
A. T. Still Research Institute, A. T. Still University, Mesa, AZ, USA
3
Department of Psychology, Sociology, and Criminal Justice, Middle Georgia
State University, Macon, GA, USA
4
Division of Integrated Sciences, Wilson College, Chambersburg, PA, USA
5
School of Psychology, Social Work and Social Policy, University of South
Australia, Adelaide, South Australia, Australia
6
Department of Psychology and Communication, University of Sonora,
Sonora, Mexico
7
Singapore Prison Service, Singapore
8
School of Social Sciences, Singapore Management University, Singapore
Corresponding Author:
Aurelio José Figueredo, Department of Psychology, School of Mind, Brain, and
Behavior, College of Science, University of Arizona, 1503 East University
Boulevard, PO Box 210068, Tucson, AZ 85721, USA.
Email: ajf@u.arizona.edu
Creative Commons CC-BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License
(http://www.creativecommons.org/licenses/by-nc/3.0/) which permits non-commercial use, reproduction and distribution of the work without further
permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
2
profiles of resource allocations. Bioenergetic and material
resources need to be mobilized to achieve the functional objectives of survival and reproduction. These resource mobilizations power the expression of the biological and behavioral
adaptations that are the individual tactical elements deployed
by the governing LH strategies.
The rationale that undergirds the psychometric approach to
the assessment of LH strategies is to tap into the dynamics of
these proximal resource allocations, rather than monitoring the
attainment of the distal achievements to which they are
directed. This is based upon the evolutionary psychological
conception of organisms as adaptation executers rather than
as fitness maximizers, where the resource allocations are the
adaptations being executed and the distal achievements are the
specific components of fitness being maximized. In contrast,
the biodemographic approach directly samples the distal
achievements eventually produced by these resource allocations, such as age of puberty, age of first sexual contact, number of sexual partners, age of first birth, interbirth interval,
number of offspring at completed fertility, and so on.
Although the biodemographic approach has also proven
useful in the study of LH strategies, it suffers from various
limitations, as all single methods ultimately do. Among them
is the well-known fallibility of the relation between motivation and performance in psychology. According to one formulation of this problem (Mitchell, 1982), performance can
be modeled as the multiplicative product (or statistical interaction) of the individual’s motivation, the individual’s ability,
and the individual’s environment. This model states explicitly
that, aside from the individual’s motivation, the individual
requires the physical and mental ability to perform the task
as well as an environment that affords the requisite resources
for the goal’s attainment.
Applying this specifically to LH strategies, a human adolescent male’s age of first sexual contact and number of sexual
partners are not uniquely determined by his motivational state,
which in most cases we can safely presume to be quite high. It
is also codetermined by a number of intrinsic individual characteristics, such as his sexual attractiveness, social skills, and
social status, and additionally by a number of extrinsic ecological characteristics, such as the availability of willing female
sexual partners, the degree of direct access to such willing
female sexual partners afforded by the environment, and the
socially imposed restrictions (or lack thereof) governing adolescent sexual activity. A human adolescent male’s age of first
sexual contact and number of sexual partners are thus not very
pure measures of his either slower of faster LH strategy, which
is a highly heritable trait and thus not very contingent upon
such transient ambient circumstances (h2 * .65; Figueredo &
Rushton, 2009; Figueredo, Vásquez, Brumbach, & Schneider,
2004). Similarly, a human female’s age of first birth, interbirth
intervals, and number of offspring at completed fertility is
codetermined by a number of individual difference factors,
such as her physical condition and a number of ecological and
sociocultural factors, such as environmental resource abundance and any socially imposed regulation of either the timing
Evolutionary Psychology
or the relational context of reproductive activity. At the present
time, we believe that there is insufficient empirical evidence
reported in the published literature to determine whether there
might or might not be any sex differences in the heritability of
LH strategy.
This is not to say that the psychometric approach is somehow more valid than the biodemographic approach or vice
versa. The idea is that they are complementary, as they each
provide windows into different successive stages of the same
causal processes (Garcia et al., 2016). The psychometric
approach is based on sampling behavioral and cognitive indicators of the LH resource allocations among different component domains of fitness. For example, the Arizona Life History
Battery (ALHB) contains seven subscales (not including the
Mini-K, which is designed as domain general) that sample the
following domain-specific psychosocial resource allocations:
(1) Insight, Planning, and Control; (2) Mother/Father Relationship Quality; (3) Family Social Contact and Support;
(4) Friends Social Contact and Support; (5) Romantic Partner
Attachment; (6) General Altruism; and (7) Religiosity. This
sampling of resource allocation domains is not represented as
completely exhaustive. However, we believe that it provides
reasonably adequate coverage of the major areas of psychosocial resource investment that LH theory would predict as characterizing a slower LH strategy; faster LH strategists are thus
predicted to score lower on the latent common factor that can
be extracted from these seven more domain-specific subscales.
The Mini-K and the K-SF-42
Since it was first published 10 years ago (Figueredo et al.,
2006), the Mini-K has been a highly successful 20-item short
form for the psychometric measurement of LH strategy. It has
fared very well in meta-analyses (e.g., Figueredo et al., 2014),
with a mean bivariate correlation of r ¼ :75 and a disattenuated
synthetic correlation of ρ = .91 with its corresponding long
form, the 199-item ALHB. Such meta-analytic work, however,
has been limited to English-speaking North American undergraduate student populations. The concern is that the Mini-K
has actually been applied to a much wider variety of populations cross-culturally including such geographically diverse
places such as Chile, Costa Rica, Israel, Mexico, Poland, Singapore, Sweden, the Netherlands, and the United Kingdom (e.
g., Abed et al., 2012; Buunk & Hoben, 2013; Buunk, Pollet,
Klavina, Figueredo, & Dijkstra, 2009; Cabeza de Baca, Figueredo, & Ellis, 2012; Cabeza de Baca, Sotomayor-Peterson,
Smith-Castro, & Figueredo, 2014; Egan et al., 2005; Figueredo, Andrzejczak, Jones, Smith-Castro, & Montero-Rojas,
2011; Figueredo, Tal, McNeill, & Guillén, 2004; Figueredo
& Wolf, 2009; Frías-Armenta et al., 2005; Frías-Armenta,
Valdez-Ramirez, Nava-Cruz, Figueredo, & Corral-Verdugo,
2010; Gaxiola-Romero & Frías-Armenta, 2008; Jonason, Li,
& Czarna, 2011; Sotomayor-Peterson, Cabeza de Baca, Figueredo, & Smith-Castro, 2013; Sotomayor-Peterson, Figueredo,
Hendrickson-Christensen, & Taylor, 2012; Tal, Hill, Figueredo,
Frías-Armenta, & Corral-Verdugo, 2006; Tifferet, Agrest, &
3
Figueredo et al.
Shlomo Benisti, 2011; van der Linden, Figueredo, de Leeuw,
Scholte, & Engels, 2012; Woodley of Menie & Madison,
2015).
An acknowledged disadvantage of the Mini-K is its limited
reliability (
α ¼ :75 in the cited meta-analysis), which is due the
fact that it is a short form for an entire multivariate latent
variable (the K-Factor), comprising several component subscales, rather than a single uniform scale. The latter fact inevitably leads to some irreducible degree of heterogeneity among
the items, as they were designed to reflect conceptually distinct
but convergent indicators of the multivariate latent construct.
A measure of internal consistency among items, such as
Cronbach’s α, will therefore return an appropriately low estimate. Also worth noting is that the test–retest reliability of the
Mini-K is similarly low, which can only be attributed to the
relatively low number of items.
The 20 items on the Mini-K are not a subset of the 199
items on the ALHB but are instead unique items written to
assess its content areas more globally. For example, 2 items
(“While growing up, I had a close and warm relationship with
my biological mother” and “While growing up, I had a close
and warm relationship with my biological father”) were written
to summarize the substantive content of the 26-item Mother/
Father Relationship Quality subscale (adapted from Brim et al.,
2000; Figueredo, Vásquez, Brumbach, & Schneider, 2004,
2007) of the ALHB. This strategy was deemed optimal for
producing a 20-item short form in comparison with selecting
the “best” items from the subscale, as even the 2 bestperforming individual items (by whatever criterion) were
unlikely to cover the entire substantive content of the subscale
and reflect the full breadth of the construct. The Mini-K was
originally developed for use in situations wherein time constraints made use of the full ALHB prohibitive. It was never
recommended for situations where the psychometric assessment of LH strategy was intended as a cornerstone of the
research; in such cases, the full ALHB was to be preferred and
was presumed to be worth the cost in added response burden.
Additionally, the recent completion of a large cross-cultural
survey using the ALHB (Figueredo et al., 2016) presents an
opportunity to create a short form that can also stand up to tests
of cross-cultural validity. We illustrate a novel method for generating such a cross-culturally valid short form.
Description of the Present Study
These characteristics of the Mini-K offer an opportunity, after a
decade of productive use, to propose a somewhat longer short
form that might retain some of its advantages, avoid its major
disadvantages, and allow for more flexibility in research application. The purpose of the present article is to propose such an
alternative short form for the ALHB, which we are calling the
K-SF-42, as it contains 42 items (see Appendix for a complete
listing). These 42 items are directly selected from the ALHB,
unlike the 20 items of the Mini-K, making direct comparisons
of the relative psychometric performance of the two alternative
short forms a potentially valid and instructive exercise.
The purpose of this article is not, however, to present a
review of the construct validity of the ALHB itself, as we have
already published a rather extensive meta-analysis on both the
predictive (“nomological”) and convergent validity of the MiniK and the ALHB (Figueredo et al., 2014). We have also published a more complete treatment of the construct validity of
the hierarchical latent structure of the Mini-K, the ALHB, and
related measures using multiple data sets and applying confirmatory factor analyses as well as other methods (Olderbak,
Gladden, Wolf, & Figueredo, 2014). Furthermore, we have also
expounded eloquently elsewhere upon the epistemological and
methodological rationales underlying the psychometric
approach to assessing LH strategy (Figueredo, Cabeza de Baca,
& Woodley, 2013; Figueredo et al., 2015).
To maintain the breadth of the construct at each level of
aggregation, 6 items were selected from each of the seven
subscales of the ALHB (not including the Mini-K), representing several domains of potential allocation of bioenergetic and
material resources in a manner consistent with the execution of
a slow LH strategy. Where appropriate, these 6 items were
balanced across the multiple subdomains sampled by each subscale. As novel item selection procedures were applied to
obtain items and subscales that functioned at the highest levels
of cross-cultural generality, we followed up with item response
theory (IRT) Rasch modeling (Bond & Fox, 2001) of both
individual items and aggregated subscales to ascertain whether
our procedures performed as intended. We also performed a
generalizability theory (GT) analysis (Figueredo & Olderbak,
2008) on the IRT-generated subscale difficulty estimates to test
for the degree of cross-cultural invariance that our procedures
managed to achieve.
This series of secondary data analyses were performed upon
a recently completed five-nation cross-cultural survey, which
was originally designed to assess the role of LH strategy in the
etiology of interpersonal aggression (Figueredo et al., 2016).
Only data from the ALHB, collected in all five cross-cultural
replications, were used for the present analyses. The single
immediate objective was producing the K-SF-42, such that it
would perform optimally across all five cultures sampled, and
perhaps even generalize well to other cultures not currently
sampled as a result of the geographic breadth of those included
in the present study.
Method
Participants and Procedures
A total of 738 undergraduate students participated in the study,
enrolled in introductory psychology courses in five different
major universities located in Australia (AU; N = 131), Italy (IT;
N = 172), Mexico (MX; N = 160), Singapore (SG; N = 115),
and the United States (US; N = 160). In the original study from
which these data are derived (Figueredo et al., 2016), participants completed a series of computerized self-report questionnaires assessing the following constructs: LH strategies;
executive functioning; mating strategies; mate values; mating
4
Evolutionary Psychology
effort; revenge ideologies; psychopathic and aggressive attitudes; and their coercive, aggressive, and possibly violent behavioral interactions over the past year with any and all members
of their same sex and with any and all members of the opposite
sex (whether or not they were intimate romantic partners) with
which they might have interacted. Participants signed up for the
study, provided informed consent, and completed the questionnaires through a secured Internet website.
Certain theoretically motivated decisions were made at the
beginning of this process regarding items that would not be the
candidates for inclusion in the new short form, the K-SF-42.
One such decision was to eliminate from consideration the set
of items in the General Altruism scale sampling Altruism
Toward Children, as it tended to produce an overabundance
of missing data in younger samples of participants, and was
not deemed theoretically essential to the construct being
measured.
Measures
The cross-sample geometric mean (CSGM) method for optimal
item selection. To maintain the breadth of the construct at each
level of aggregation, 6 items were selected from each of the
seven subscales of the ALHB (not including the Mini-K), representing several domains of potential allocation of bioenergetic and material resources in a manner consistent with the
execution of a slow LH strategy. Where appropriate, these 6
items were balanced across the multiple subdomains sampled
by each subscale. For the Mother/Father Relationship Quality
subscale, the 6 items were selected by sampling the best 3
items from each of the parental (Mother and Father) subdomains. Similarly, for the General Altruism scale, the 6 items
were selected by sampling the best 2 items from each of the
following three subdomains: Altruism Toward Kin, Altruism
Toward Friends, and Altruism Toward Community.
The way that we operationalized which items were “the
best” defined the novelty of this approach and has the beauty
of its great simplicity. We first calculated the part-whole correlation of each item score with the unit-weighted (UW) factor
score for the subscale in which it was nested, conceptually
representing the UW factor loadings of each item. We did this
separately for each of the five cross-cultural replications and
pasted these as columns on a single MS Excel spreadsheet. We
used the spreadsheet to compute the geometric mean of these
part-whole correlations across all five cross-cultural replications. Next, we selected the 6 items with the highest geometric
means from each subscale (within the subdomain constraints
outlined above) for inclusion in the K-SF-42, by simply using
the spreadsheet to sort the data by the columns containing the
subscales and the geometric means in descending order.
The rationale for using the geometric rather than arithmetic
mean is that it involves the multiplication rather than the addition of the parameter estimates for each of the five crosscultural replications. In the case of the arithmetic mean, the
sum is divided by n; in the case of the geometric mean, the
nth root of the product is instead calculated. Multiplication is
equivalent in Boolean algebra to a logical “AND” statement or
logical conjunction; whereas addition is equivalent in Boolean
algebra to a logical “OR” statement or logical disjunction. A
high product term is thus only produced when the values in all
the coefficients being multiplied are high; a high additive term,
on the other hand, can still be obtained if one or more of the
coefficients being summed are sufficiently high to compensate
for any that might be lower. Thus, a higher geometric mean is a
stronger filter than a high arithmetic mean for selecting items
that perform sufficiently well across all (rather than mere some)
The ALHB. The ALHB (Figueredo et al., 2007) is a set of cognitive and behavioral indicators of LH strategy compiled and
adapted from various original sources. These self-report psychometric indicators measure graded individual differences along
various complementary facets of a coherent and coordinated
LH strategy, as specified by LH theory, and converge upon a
single multivariate latent construct. These were scored and
aggregated directionally to indicate a slow (high-K) LH strategy,
on the fast–slow (r–K) continuum. Not including the Mini-K,
the seven subscales of the ALHB are as follows: Insight, Planning, and Control (20 items; adapted from Brim et al., 2000;
Figueredo et al., 2004, 2007); Mother/Father Relationship Quality (26 items; adapted from Brim et al., 2000; Figueredo et al.,
2004, 2007); Family Social Contact and Support (15 items;
adapted from Barrera, Sandler, & Ramsay, 1981; Figueredo
et al., 2001); Friends Social Contact and Support (15 items;
adapted from Barrera et al., 1981; Figueredo et al., 2001);
Experiences in Close Relationships (ECR: 36 items; adapted
from Brennan, Clark, & Shaver, 1998; Alonso-Arbiol, Balluerka, & Shaver, 2007; Figueredo et al., 2005); General Altruism (50 items; adapted from Brim et al., 2000; Figueredo et al.,
2004, 2007); and Religiosity (17 items; adapted from Brim et al.,
2000; Figueredo et al., 2004, 2007). As the internal consistency
reliabilities of the ALHB subscales are a major subject of the
present analyses, presentation of these psychometric properties
will be deferred to the Results section.
The Mini-K. The Mini-K (Figueredo et al., 2006) is a 20-item
short form of the ALHB. Meta-analytically (e.g., Figueredo
et al., 2014), the Mini-K has shown a high mean bivariate
correlation (r ¼ :75 ) and a higher disattenuated synthetic correlation of (ρ = .91) with its corresponding 199-item long form.
The items on the Mini-K are not directly sampled from the
seven other subscales comprising the ALHB, but are instead
designed to provide more global assessments within each of the
LH domain-specific resource allocations sampled by the
ALHB. As with the ALHB, the Mini-K is scored directionally
to indicate a slow (highly K-selected) LH strategy on the “fast–
slow” (r–K) continuum.
Statistical Analyses
All statistical analyses were performed using MS Excel 2003
(Microsoft, 2003), SAS 9.3 software (SAS Institute Inc., 2011),
and WINSTEPS 3.68.1 (Linacre, 2016).
Figueredo et al.
of the multiple cultures sampled. We have therefore named this
the CSGM method for optimal item selection, having been first
pioneered in a previous study but gone without any special
designation (Patch, Garcia, Figueredo, & Kavanagh, 2016).
In contrast, other short form construction methods that rely
on empirical selection of variables will often experience problems of replication when applied to independent samples, especially cross-cultural ones. Part of this problem arises from
model fitting to sample-specific characteristics that are not
present in other samples. For example, empirical selection
methods may capitalize on chance differences (stochastic fluctuations) among item characteristics that are purely due to
sampling error; as a result which items are truly the best may
not replicate between one independent and another even if they
are drawn from the same population, being analogous to statistical “Type I Errors.” Furthermore, empirically based selection of locally optimal items can lead to disastrous results when
applied outside of the population upon which the new scale
was originally developed. Although this latter circumstance is
obviously less of a problem the more alike the samples are to
each other, between-sample differences on relevant characteristics can be quite large in the case of cross-cultural research.
After implementing the CSGM procedures, we had to assess
whether this seemingly clever tactic actually worked in practice
by performing each of the following psychometric assessment
procedures: (1) We compared the internal consistency reliabilities of the short form with the long form for each of the seven
subscales of the ALHB, (2) we compared the part-whole correlations (UW factor structures) of the short form with the long
form for each of the seven subscales of the ALHB, (3) we
examined the simple bivariate correlations of the short form
with the long form for each of the seven subscales of the
ALHB, (4) we examined the relative internal consistency
reliabilities of the two alternative short forms (Mini-K and
K-SF-42) of the ALHB, each as a unitary scale irrespective
of internal structure, as well as the correlations of those
UW scale scores with the UW factor score of the full ALHB,
and (5) we examined the relative incremental validities of the
two alternative short forms (Mini-K and K-SF-42) of the
ALHB in multiple regression models predicting the UW factor
score of the full ALHB.
IRT Rasch modeling of item and subscale difficulties. To validate
the results obtained by means of this novel method by more
conventional means, we ran a one parameter Rasch model to
obtain IRT parameter estimates on the K-SF-42 items and subscales. The IRT parameter of primary interest was the so-called
item difficulty, which indicates how high a factor score on the
underlying latent variable (θ) is required to produce a “correct”
response on any given item (Bond & Fox, 2015). The latent
variable of interest is typically cognitive ability (g), but in the
present case it is simply LH speed (K). The IRT method chosen
was the single parameter Rasch model and analyzed with WINSTEPS. First, countries were combined within subscales, and
each subscale was analyzed with countries as items. This was
done as a way to see whether countries differed in their relative
5
rank within each subscale, a rough indication of differential
item functioning of countries within subscales. Second, subscale totals were computed and then designated as items in an
overall total scale score, also in a Rasch analysis. This allowed
for subscales to be ranked in terms of “difficulty” in terms of
their ease of endorsement in their overall LH strategy. The
easiest subscales would be those with the highest priority of
endorsement—a strategy most commonly shared in the population. After computing subscale difficulties by country, they
could be inspected for relative difficulty or relative importance
to each population.
The rationale behind these analyses was that it was important to determine whether our novel item selection procedure,
based on classical test theory (CTT), might have selected
items that were too similar on item difficulty as a result of
being highly correlated with each other. CTT item selection
procedures have been criticized for having this bias (Bond &
Fox, 2015), as a scale composed of items of similar difficulties will not adequately measure individual persons throughout
the extent of the latent trait, and thus may incorrectly assess
the latent trait score of persons either substantially above or
substantially below that central tendency. In the case of the KSF-42, we had endeavored to preserve the autonomy among
these subscales by the hierarchical design of our item selection procedure, as we understood that limitation in advance
and took that step to hopefully avoid it. That is why the CTT
procedure that we used only selected the best (most highly
correlated) items within each subscale. We hypothesized that
although the items within subscales might have similar item
difficulties, each of the aggregated 6-item subscales should
nonetheless have different difficulties. Assessing difficulties
was in this case assessing their range of sensitivity of each
of the subscales to the latent construct of LH speed (K). This
latter prediction was based on the premise underlying the
development of the ALHB, where each of the subscales represents a distinct domain-specific opportunity for an LH-based
resource allocation decision to differentially invest (or not) in
that particular system of social relationships. As each specific
resource allocation domain should have a different item difficulty in terms of LH speed, we should therefore be able to
observe substantial variance among the subscale difficulties.
The hierarchical item selection procedure that we used should
therefore enable the K-SF-42 to measure the full range of LH
speed (K) along the broader difficulty distribution of
subscales.
Generalizability analyses of item and subscale difficulties. The other
important question that we hoped to address by these analyses
was whether the subscale difficulties differed substantially
across the sampled cultures. As we did not have enough data
in each sample for a formal differential item functioning analysis (Acar, 2011; Linacre, 2013, 2016; Scott et al., 2009;
Tristan, 2006), we instead subjected the individual-level subscale difficulty estimates to general linear modeling (GLM) and
GT analyses (Figueredo & Olderbak, 2008). We restricted these
analyses to aggregate subscale scores, as the cross-cultural
6
Evolutionary Psychology
Table 1. Internal Consistency Reliabilities for Convergent Indicators of Life History Strategy in Australia (AU), Italy (IT), Mexico (MX),
Singapore (SG), and the United States (US).
Long Form Cronbach’s
Short Form Cronbach’s
Scale/Subscale
AU
IT
MX
SG
US
AU
IT
MX
SG
US
The Arizona Life History Battery
Insight, Planning, and Control
Parental Relationship Quality
Family Contact and Support
Friends Contact and Support
Romantic Partner Attachment
General Altruism
Religiosity
.91
.90
.91
.90
.93
.92
.95
.85
.90
.92
.88
.92
.90
.95
.95
.94
.89
.90
.89
.94
.96
.88
.85
.94
.86
.92
.93
.96
.89
.90
.92
.90
.91
.90
.95
.86
.83
.92
.89
.83
.77
.91
.79
.83
.90
.87
.81
.75
.93
.92
.85
.91
.89
.84
.77
.94
.82
.80
.93
.90
.85
.78
.96
.86
.81
.92
.90
.81
.71
.94
Table 2. Unit-Weighted (UW) Factor Structures for Convergent Indicators of Life History Strategy in Australia (AU), Italy (IT), Mexico (MX),
Singapore (SG), and the United States (US).
Long Form Part-Whole Correlations (UW l)*
Short Form Part-Whole Correlations (UW l)*
Scale/Subscale
AU
IT
MX
SG
US
AU
IT
MX
SG
US
The Arizona Life History Battery
Insight, Planning, and Control
Parental Relationship Quality
Family Contact and Support
Friends Contact and Support
Romantic Partner Attachment
General Altruism
Religiosity
.64
.60
.60
.40
.40
.70
.22
.58
.61
.60
.50
.44
.72
.44
.77
.61
.59
.61
.39
.68
.58
.54
.70
.52
.43
.30
.57
.37
.63
.49
.62
.58
.38
.66
.47
.56
.55
.62
.45
.38
.63
.29
.58
.55
.61
.51
.43
.62
.46
.69
.66
.65
.53
.36
.61
.60
.56
.62
.49
.45
.33
.54
.47
.60
.45
.65
.58
.41
.62
.49
*All part-whole correlations (UW factor loadings) are statistically significant at p < .05.
functioning of the individual items within subscales was of
lesser interest, for reasons already stated.
A GLM was constructed, analogous to a two-way analysis
of variance, where we tested for effects on the person-level
difficulty estimates of: (1) the different cross-cultural samples,
(2) the different subscales, and (3) the two-way interaction
between them. We then followed up with estimated mean
squares and variance components analysis using the same
design to obtain estimates of the latent variance components
hypothetically underlying these observed variances, first using
Type 1 and then using reweighted maximum likelihood
(REML) estimation for comparison. Finally, we used these
variance component estimates to calculate generalizability
coefficients for each of the two random effects of interest with
respect to their interaction with each other, based on the Type 1
and REML estimates obtained.
Results
Table 1 presents side-by-side comparisons of the internal consistencies of the K-SF-42 and the full ALHB for all five crosscultural sites. All Cronbach’s α for both the K-SF-42 and the
full ALHB met the criterion for “acceptable” (α ≥ .70) with
64 of 70 meeting the criterion for “good” (α ≥ .80) and 40 of 70
meeting the criterion for “excellent” (α ≥ .90). Across all five
cross-cultural sites, only one scale (General Altruism) demonstrated an appreciable decrease in internal reliability when
going from the full ALHB to the K-SF-42 (mean
Δα = −.16). This difference, however, did not drop the value
of Cronbach’s α below the acceptable threshold (range
α = .71–.78, median = .77). The other six scales showed a
decrease of no more than .11 in Cronbach’s α when using the
K-SF-42 (range Δα = −.11 to .04, median Δα = −.03).
Table 2 presents the UW factor structures of the K-SF-42
and the full ALHB for all five cross-cultural sites. Only two
part-whole correlations showed a change of greater than .10
when going from the full ALHB to the K-SF-42, Religiosity in
SG (ΔUW λ = +.10) and General Altruism in IT (ΔUW λ =
−.10). Nevertheless, these changes do not appear to decrease
the part-whole correlations into a range of questionable validity. The statistical nonsignificance of average scale part-whole
change between forms seems to support this (AU mean
ΔUW λ = −.01, p > .05; IT mean ΔUW λ = −.02, p > .05;
MX mean ΔUW λ = −.02, p > .05; SG mean ΔUW λ = .00,
p > .05; US mean ΔUW λ = −.00), p > .05).
Table 3 presents the short–long correlations between the
K-SF-42 and the full ALHB for all five cross-cultural sites.
All short–long correlations are strong and positive
(range = .74–.98, median = .90), indicating a high degree of
agreement between the short and long form when measuring
each domain-specific component of slow LH.
Table 4 presents side-by-side comparisons of the internal
consistencies and short–long form correlations of the Mini-K
Short Form and the K-SF-42 for all five cross-cultural sites. On
7
Figueredo et al.
Table 3. Short-Long Form Correlations for Convergent Indicators
of Life History Strategy in Australia (AU), Italy (IT), Mexico (MX),
Singapore (SG), and the United States (US).
Short-Long Form Correlations*
Scale/Subscale
AU
IT
MX
SG
US
The Arizona Life History Battery
Insight, Planning, and Control
Parental Relationship Quality
Family Contact and Support
Friends Contact and Support
Romantic Partner Attachment
General Altruism
Religiosity
.91
.89
.91
.93
.93
.85
.98
.81
.86
.93
.91
.86
.83
.97
.92
.90
.86
.87
.90
.87
.98
.83
.78
.95
.90
.92
.88
.98
.87
.90
.91
.89
.89
.88
.98
*All short-long form correlations are statistically significant at p < .05.
all metrics and for all sites, the K-SF-42 outperformed the
Mini-K Short Form. Cronbach’s α comparison tests showed a
statistically significant improvement of the K-SF-42 over the
Mini-K Short Form, AU χ2(1) = 8.74, p < .05; IT χ2(1) = 30.30,
p < .05; MX χ2(1) = 3.94, p < .05; SG χ2(1) = 13.64, p < .05;
US χ2(1) = 25.79, p < .05, by a median of .12 (range = [.04,
.18]). The same was true for short–long correlation comparison
tests, AU t(130) = −4.81, p < .05; IT t(171) = −4.22, p < .05;
MX t(159) = −8.4, p < .05; SG t(115) = −6.18, p < .05; US
t(159) = −6.97, p < .05, which showed improvement by a
median of .13 (range = [.10, .18]).
Table 5 presents the incremental validities of the Mini-K Short
Form and the K-SF-42 for all five cross-cultural sites. When both
short forms were entered into a regression to predict the full
ALHB, the total explained variance was high (R2 range = range
= [.89, .96] median = .93), indicating that the two short forms taken
together capture most of the variance of the full ALHB. The partial
R2 for each short form represents the proportion of the variance
that is uniquely explained by that short form. In other words,
it is the incremental validity of that short form over the other.
All partial R2s were statistically significant; however, the
partial R2s of the K-SF-42 were approximately between 3.5
and 11.5 times greater than those of the Mini-K Short Form
(median = 4 times). This means that while the Mini-K Short
Form and the K-SF-42 captured much of the same variance
of the full ALHB, the K-SF-42 alone accounted for more
uniquely; it had greater incremental validity.
Column 1 of Table 6 displays the standard deviations of the
raw item difficulties within subscales. We believe that these
were adequate dispersions, with the possible exception of the
General Altruism subscale. Although we do not know why the
dispersion among items for that one subscale was lower than
the others, we believe that these standard deviations generally
indicate a reasonably adequate dispersion of the items within
subscales. Columns 2–5 of Table 6 display the standardized
subscale Infit/Outfit Mean Squares and standardized subscale
Infit/Outfit Standard Deviations for each of the subscale scores.
For efficiency of presentation, subscale scores were aggregated
across all five cross-cultural samples. We believe these to be
excellent indices of both infit and outfit for all of the subcales,
indicating an adequate data–model fit.
Table 7 displays the results of our generalizability analyses of
the IRT-based subscale difficulties for the K-SF-42. We did not
estimate the generalizability across cultures of individual item
scores as these were of lesser interest given that using the CSGM
method as a criterion for item selection does not guarantee preserving optimal ranges of difficulties among items within subscales.
First, the preliminary GLM shows that the preponderance of
the systematic variance in difficulties is accounted for by the
main effect of subscale (73.9%), supporting the greater variance among subscales we predicted, with only relatively trivial
amounts accounted for by the main effect of Sample (7.9%)
and the Sample × Subscale interaction (18.1%), indicating that
the variance in difficulties among the different cross-cultural
samples was not very substantial and that the variance in the
rank ordering of subscale difficulties different among crosscultural samples was also relatively minor.
Second, we performed an expected mean squares analysis,
modeling both Sample and Subscale as random factors, such
that the interaction term (Sample × Subscale) intruded on
each of them and thus served as the common denominator
term for both main effects in all further calculations. This
permitted the estimation of the hypothetical variance components by two convergent methods, hierarchical Type 1 sums
of squares (conservatively assigning causal priority to
Sample) and REML, and these are tabulated for comparison
within the last two columns.
Third, these estimated variance components were used to
compute the following two generalizability coefficients, using
each of these two estimation methods: (1) for Sample as the
focal facet, being generalized across the Sample × Subscale
interaction as the random facet and (2) for Subscale as the
focal facet, being generalized across the Sample × Subscale
interaction as the random facet. These indicated that the variance in difficulties among cross-cultural samples did not
2
generalize very well across the subscales (EType1
¼ :186;
2
EREML
¼ :186), whereas indicated that the variance in difficulties among the subscales did generalize very well across the
2
2
cross-cultural samples (EType1
¼ :753; EREML
¼ :753). These
results were identical to three decimal places across estimation
methods and entirely in line with our predictions, supporting our
claim that the relative difficulties of the subscales are quite
consistent across cultures, indicating that the subscales function
in similar ways in measuring the construct of interest.
As a visual illustration of these results, Figure 1 shows a
consistency in the relative difficulty of the seven subscales
across the five countries, a consistency observed in the “all”
countries category. The one apparent inconsistency is the family scale difficulty for the Singapore sample. Specifically, the
Singapore family score was rated as very easy in comparison to
the other four countries. This especially low score on that subscale, as compared with the other cross-cultural samples, may be
attributable to traditional Asian family values ultimately based
on the “filial piety” preached by the Eastern Sage Confucius
(孔夫子 Kǒng Fūzǐ, 551–479 BC; Soothill, [1910] 1937).
8
Evolutionary Psychology
Table 4. Internal Consistency Reliabilities of Alternative Short Forms (Mini-K and K-SF-42) for Latent Multivariate Construct of Life History
Strategy in Australia (AU), Italy (IT), Mexico (MX), Singapore (SG), and the United States of America (US).
Alternative Short Form Cronbach’s
Alternative Short-Long Form Correlations*
Scale/Subscale
AU
IT
MX
SG
US
AU
IT
MX
SG
US
The Arizona Life History Battery
Mini-K Short Form (20 Items)
K-SF-42 (Standardized Items)†
.76
.84
.66
.84
.85
.89
.73
.85
.72
.86
.83
.93
.81
.91
.78
.96
.76
.93
.81
.94
*All short-long form correlations are statistically significant at p < .05. †All Cronbach’s and short–long correlations for the K-SF-42 are statistically greater than
those of the Mini-K at p < .05.
Table 5. Incremental Validities of Alternative Short Forms (Mini-K
and K-SF-42) for Latent Multivariate Construct of Life History Strategy in Australia (AU), Italy (IT), Mexico (MX), Singapore (SG), and the
United States (US).
pR2
Site
Total R2
Mini-K Short Form
The Arizona Life History Battery
AU
.93
IT
.89
MX
.96
SG
.91
US
.93
K-SF-42
.06
.07
.03
.05
.05
.25
.25
.35
.33
.28
Note. All partial correlations are statistically significant at p < .05.
Discussion
The new and slightly longer short form of the ALHB (as compared with the Mini-K), which we named the K-SF-42, performed very well with respect to various psychometric
standards against which it was evaluated. The K-SF-42 reduced
the number of items needed to assess slower LH from 199 items
in the original ALHB to only 42 items in the new short form,
and it did so with minimal losses of internal consistency reliability and convergent validity among the constituent subscales.
We believe that these psychometric indices of performance
would have been deemed adequate had we submitted the original interpersonal aggression study for publication using the
K-SF-42 in place of the full ALHB; it is doubtful that any
reasonable editor or peer reviewer would have been concerned
with the adequacy of reported psychometric benchmarks had
they not seen the slightly better ones provided by the full ALHB.
In addition, we found high correlations between the UW
factor scores for the reduced K-SF-42 subscales and the full
ALHB subscales, between the UW factor scores for the overall
K-SF-42 and ALHB, and even between the UW factor scores
for the overall K-SF-42 and Mini-K. Treating the K-SF-42 as a
unitary scale, irrespective of internal subscale structure, we
found small but statistically significant improvements in the
K-SF-42 with respect to the Mini-K in their internal consistency reliabilities and predictive validity coefficients in relation
to the full ALHB. Testing for incremental validity of the K-SF42 with respect to the Mini-K in predicting the full ALHB
score, we found their overlap to be quite substantial, but the
unique proportions of variance predicted by the K-SF-42 to be
substantially greater in magnitude predicted than those predicted by the Mini-K, although these latter variance components were also statistically significant.
We therefore conclude that the K-SF-42 has been developed
such that it performs optimally across all five cultures sampled,
and that these results will perhaps prove to generalize similarly
to most other modern industrial societies that were not currently sampled as a result of the geographic breadth of those
included in the present study. We would therefore recommend
the K-SF-42 for use over the Mini-K for situations in which
the total acceptable response burden of the study can bear the
weight of more than 20 items but less than 199 items for the
psychometric assessment of LH strategy.
As the K-SF-42 has been newly introduced with the present
article, there are as yet no data on its predictive validity. Nevertheless, we believe that the magnitudes of the reported correlations of the K-SF-42 with both the Mini-K and the full
ALHB are sufficiently high that we can confidently expect that
its predictive validities would be comparable to the latter, lying
somewhere between that of the Mini-K and the full ALHB, and
moderated only by its intermediate reliability. As cited above,
the meta-analytically disattenuated correlation coefficient (ρ)
between the Mini-K and the full ALHB was .91 (Figueredo
et al., 2014); although we do not yet have sufficient data on the
K-SF-42 to make such a precise estimate, there is not reason to
believe that it would not be even higher, given the evidence
that we presented in the present article. Under that assumption,
it is a straightforward logical inference to predict that the K-SF42 should have stronger predictive power than the Mini-K for
all criterion variables that have been assessed so far.
Finally, we have validated a novel methodology for producing cross-culturally valid short forms of longer measures using
the CSGM method for optimal item selection. We have also
cross-validated this novel method using IRT Rasch analyses of
the item and subscale difficulty levels, followed by GT analyses that showed acceptable levels of consistency in the rank
ordering of aggregate subscale difficulties across the cultures
sampled. Thus, we have shown that the CSGM method can
produce psychometrically acceptable and robust results, at least
in application to the present data.
We understand that we have not conformed to the conventional “best practices” psychometric standards in these
9
Figueredo et al.
Table 6. Standard Deviations (SD) of Raw Item Difficulties Within Standardized Subscale Infit/Outfit Mean Squares (MNSQ) and Standardized
Subscale Infit/Outfit SD for Convergent Indicators of Life History Strategy.
Subscales
SD of Item Difficulties
Within Subscales
Subscale
Z-Infit MNSQ
Subscale
Z-Infit SD
Subscale
Z-Outfit MNSQ
Subscale
Z-Outfit SD
1.45
1.48
2.65
2.03
1.09
0.64
1.67
0.78
0.84
0.85
0.89
1.10
0.79
0.94
–3.68
–2.63
–3.34
–1.95
1.75
–3.97
–1.04
0.76
0.84
0.85
0.88
1.13
0.78
0.97
–3.91
–2.56
–3.05
–1.99
2.21
–4.06
–0.53
Insight, Planning, and Control
Parental Relationship Quality
Family Contact and Support
Friends Contact and Support
Romantic Partner Attachment
General Altruism
Religiosity
Table 7. Generalizability Analysis of IRT-Based Subscale Difficulties for the K-SF-42.
General Linear Model (Observed Variances)
Source
Sample
Subscale
Sample Subscale
DF
Semipartial Z2
F-Ratio
p (Ho)
4
6
24
.079
.739
.182
2.60*
16.26*
.061
<.0001
Type 1
Estimates
REML
Estimates
Expected Mean Squares Analysis and Estimated Variance Components Analysis
Source
DF
Expected Mean Squares
4
6
24
s2 (Error) + s2 (Sample Subscale) + 7 s2(Sample)
s2 (Error) + s2 (Sample Subscale) + 5 s2 (Subscale)
s2 (Error) + s2 (Sample Subscale)
Random Facet
Type 1 E2 Estimates
s2 (Sample Subscale)
s2 (Sample Subscale)
.186
.753
Sample
Subscale
Sample Subscale
.071
.941
.309
.071
.941
.309
Generalizability Coefficients
Focal Facet
s2(Sample)
s2(Subscale)
REML E2 Estimates
.186
.753
Note. IRT = item response theory; REML = reweighted maximum likelihood.
*Statistically significant at p < .05.
empirical demonstrations, mostly due to limitations in the sample size available from each cross-cultural replication, but we
emphasize that one of our major objectives in writing this
article was to introduce a novel method for creating short forms
out of long forms by using data from multiple independent
samples in a creative new way. Nevertheless, we have compared the performance of this novel method with those of a
more conventional one, and we interpret the results of the IRT
Rasch analyses and associated GT analyses as generally supporting the efficacy of the CSGM method for creating the
K-SF-42, in that: (1) The dispersion of item difficulties within
each subscale sufficiently covered each content domain, (2)
the dispersion among aggregate subscale difficulties within
the overall K-SF-42 scale also showed a satisfactory degree
of coverage, and (3) the rank ordering of subscale difficulties
across samples showed a relatively high degree of crosscultural invariance.
Figure 1. Item response theory subscale difficulties for K-SF-42.
10
Evolutionary Psychology
Appendix
The K-SF-42.
Please indicate how strongly you agree or disagree with the following statements. Use the scale below and write your answers in the spaces
provided. For any item that does not apply to you, please enter “0.”
Disagree Strongly Disagree Somewhat Disagree Slightly Don’t Know/Not Applicable Agree Slightly Agree Somewhat Agree Strongly
–3
–2
–1
0
+1
+2
+3
1._____When faced with a bad situation, I do what I can to change it for the better
2._____When I encounter problems, I don’t give up until I solve them.
3._____I find I usually learn something meaningful from a difficult situation.
4._____When I am faced with a bad situation, it helps to find a different way of looking at things.
5._____Even when everything seems to be going wrong, I can usually find a bright side to the situation.
6._____I can find something positive even in the worst situations.
7._____I spend a great deal of time per month giving informal emotional support to my blood relatives.
8._____I contribute a great deal to the welfare and well-being of my blood relatives in the present.
9._____I spend a great deal of time per month giving informal emotional support to casual acquaintances (such as neighbors or people at
church).
10._____I contribute a great deal to the welfare and well-being of my friends these days.
11._____I spend a great deal of time per month doing formal volunteer work at school or other youth-related institution.
12._____ I often contribute to any other organizations, causes, or charities (including donations made through monthly payroll deductions).
13._____ I’m a very religious person.
14._____ Religion is important in my life.
15._____ Spirituality is important in my life.
16._____ I closely identify with being a member of my religious group.
17._____ I frequently attend religious or spiritual services.
18._____ When I have decisions to make in my daily life, I often ask myself what my religious or spiritual beliefs suggest I should do.
19._____ I worry that romantic partners won’t care about me as much as I care about them.
20._____ I don’t feel comfortable opening up to romantic partners.
21._____ I want to get close to my partner, but I keep pulling back.
22._____ I often want to merge completely with romantic partners, and this sometimes scares them away.
23._____ I am nervous when partners get too close to me.
24_____ I find that my partner(s) don’t want to get as close as I would like.
The following are some questions about means of help that people offer each other. Use the scale below and write your answers in the spaces
provided, indicating about how often any parent, family member, or friend has helped you in each of the following ways. For any item that does
not apply to you, please enter “0.”
Not At All
0
A Little
Some
A Lot
1
2
3
While you were growing up…
25._____
26._____
27._____
28._____
29._____
30._____
How
How
How
How
How
How
much
much
much
much
much
much
time and attention did your biological mother give you when you needed it?
effort did your biological mother put into watching over you and making sure you had a good upbringing?
did your biological mother teach you about life?
love and affection did your biological father give you while you were growing up??
time and attention did your biological father give you when you needed it?
did your biological father teach you about life?
11
Figueredo et al.
During the last month…
31._____
32._____
33._____
34._____
35._____
36._____
37._____
38._____
39._____
40._____
41._____
42._____
How
How
How
How
How
How
How
How
How
How
How
How
much
much
much
much
much
much
much
much
much
much
much
much
have
have
have
have
have
have
have
have
have
have
have
have
your
your
your
your
your
your
your
your
your
your
your
your
relatives helped you get worries off your mind?
relatives told you that you had done something well?
relatives told you that they liked the way you are?
relatives shown you affection?
relatives listened to you when you talked about your feelings?
relatives shown interest and concern for your well-being?
friends helped you get worries off your mind?
friends told you that you had done something well?
friends told you that they liked the way you are?
friends shown you affection?
friends offered to take you somewhere?
friends shown interest and concern for your well-being?
Acknowledgments
We wish to thank Marco Del Giudice and Romina Angeleri, formerly
of the Biology of Social Behavior Laboratory, University of Turin,
Italy, for having collected the Italian Sample for the original study.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
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