Home resources as a measure of socio-economic status in Ghana
Bofah and Hannula Large-scale Assess Educ
Home resources as a measure of socio‑economic status in Ghana
Emmanuel Adu‑tutu Bofah emmanuel.bofah@hotmail
Markku S. Hannula
In large scale international assessment studies, questionnaires are typical used to query students' home possessions. Composite scores are computed from responses to the home resource questionnaires and are used as a measure of family socioeconomic background in achievement comparison or for statistical control. This paper deals with profiling the socio‑ economic status (SES) of Ghanaian students' in the context of the TIMSS 2011 study. Latent class analysis was used to profile students into respective SES classes based on the students' responses to 11 questions concerning their home resources. The results showed three clearly distinct socio‑ economic profiles: high‑ , middle‑ and low‑ SES. Moreover, a discriminate analysis was conducted to explore the degree to which the groups are accurately classified. The discriminant analysis was able to correctly classify 92.20% of the individual students into their appropriate SES group. A gender comparison of these classes suggested stable measurement invariance for the latent class indicators. This article contributes to addressing the composition of SES by providing statistical criteria to evaluate SES using empirical data.
TIMSS; Ghana; Socio‑ economic status/profiles; Latent class analysis; Discriminant analysis; Home possessions/resources
Ghana is a sub-Saharan African country with a medium-level human development
index, placing it above the regional average (United Nations Development Programme
2013). In Ghana, children can be from vastly different cultures, and have very different
backgrounds experiences. Quality education for all students has been the main objective
of most policymakers; however, years of research have shown that family socioeconomic
status indicates the available educational opportunities (e.g., Aikens and Barbarin 2008;
Parker et al. 2012; Siegler 2009). For instance, according to UNESCO’s Education for All
Global Monitoring Report 2015, one in six children in low- and middle income
countries will not complete primary school in 2015 (UNESCO 2015). In Ghana, for
example, 87% of students from low socioeconomic homes enter primary school, but only 72%
graduates, compare to 100% enrolment for children from high socioeconomic homes, of
which 80% graduates. Moreover, 60% of children from low socioeconomic homes enter
primary school at least two years older than the official age, compared to 32% of children
from high socioeconomic homes (UNESCO 2013).
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Research on the link between achievement and Socio-economic status (hereinafter
SES) has consistently indicated that students from high socioeconomic backgrounds
have higher academic achievement than their peers from low socioeconomic
backgrounds (Bofah 2015; Erberber et al. 2015; Jurdak 2014; Sirin 2005; Wang et al. 2014).
The association between higher SES and achievement is universal across nations, school
subjects (e.g., mathematics and science), and grades (e.g., from primary to
secondary education) (Bofah 2015; Erberber et al. 2015; Martin et al. 2012; Mullis et al. 2012;
Although it is well documented that students from low-SES backgrounds perform
below expectation, studies have shown that some are academically successful despite
their challenging backgrounds (Erberber et al. 2015; OCED 2011). For example, in the
Trends in International Mathematics and Science (TIMSS 2011) study, Ghana reported
the highest percentage of students from low-income homes, of whom 4% scored above
the Intermediate International mathematic Benchmark (475) (Erberber et al. 2015).
Using latent class analysis (LCA) (Goodman 1974; Lazarsfeld and Henry 1968), we
classify students into socioeconomic groups based on their responses to questions
concerning 11 household items from the TIMSS 2011 study. Due to the impact of
socioeconomic background on educational achievement, our study draws on theories from other
academic discipline, and focuses on a question of broad importance: To what extent can
home possessions be used to profile students’ SES?
SES has been the most widely used latent construct for measuring family background.
The SES concept encompasses many variables (Filmer and Pritchett 1999; Hauser and
Warren 1996; Hauser 1994; Ormrod 2011; Schulz 2005), but the most common
indicators of SES include parental education, parental occupation; family income/wealth, and
prestige; home literacy resources; and certain activities such as participation in social,
cultural, or political life (Buchmann 2002; Hauser and Warren 1996; House 1981;
Mueller and Parcel 1981; Schulz 2005). Other indicators include tangible possessions such as
houses, cars, boats, appliances, and digital equipment (Hauser and Warren 1996; Park
2008; Xu and Hampden-Thompson 2012).
High-SES background is positively associated with educational outcomes in addition,
subtraction, ordinal sequencing, and numeracy, as well as mathematics word problems
(Coley 2002; Siegler 2009), cognitive development (Paxson and Schady 2007; Yeung
and Conley 2008), language development (Fernald et al. 2013; Hoff 2003), educational
choices (Parker et al. 2012), achievement (Bofah 2015; Erberber et al. 2015; Jurdak 2014;
Kupari and Nissinen 2013; Michaelowa et al. 2001; Mullis et al. 2012; Sirin 2005; Wang
et al. 2014; Williams and Williams 2010), mathematics-related affect (Bofah 2015, 2016;
Hannula et al. 2014; Williams and Williams 2010) and attainment (Filmer and Pritchett
1999; Teachman 1987; UNESCO 2013).
Studies have shown that SES shapes children’s language learning environments and
their language development (Fernald et al. 2013; Hoff 2003). Hoff (2003) found that
language development such as lexical richness of speech produced in conversation differ by
SES, and that SES shapes children’s language learning environments and influences the
development of their language. Fernald and colleagues, found a significant disparities
in vocabulary and language processing efficiency to be already evident at 18 months
between infants from higher- and lower-SES families, and by 24 months there was a
sixmonth gap between SES groups in processing skills critical to language development.
Moreover, lower parental involvement (e.g., parent–child communication and
parent–child discussion) (McNeal 1999; Park 2008), school absenteeism, enrollment and
dropout (Langhout et al. 2009; McKenzie 2005; National Center for Education Statistics
2008; Zhang 2003), as well as poor teacher quality (Akiba et al. 2007) are associated with
students from low-SES families. This is explained by such families often having limited
financial resources, which restrict their ability to provide their children with learning
materials such as books and computers (Orr 2003), and consequently a cognitively
stimulating learning environment (Klebanov et al. 1994; Orr 2003; Yeung et al. 2002).
The relationship between parental involvement and SES has been found to be
culturally specific (Desimone 1999; McNeal 1999; Park 2008). For instance, in some countries
high-SES students’ may benefit from parent–child communication, while in others they
may benefit from other forms of parental involvement such as help with homework. In
Ghana, a common phenomenon among high-SES parents who wish to support their
children education is to hire a private home-tutor or send the child to extra lessons after
the normal school day. However, the literature indicates that middle- and high-SES
parents are more likely to participate in their children’s educational activities compared to
their peers from low-SES backgrounds (e.g., Coley 2002; Teachman et al. 1997). McNeal
(1999) found that parental-child discussion was significantly lower in low-SES than
highSES homes. Park (2008) found that parent–child communication is greater for
highSES than low-SES students. Moreover, “this greater parental participation, support and
investment in their children’s education is driven by the recognition that educational
success is the main route for reproducing their class status” (Perry 2012, p. 22).
Children who live and are educated in vicinities with well-financed schools are more
likely to have higher educational aspirations (Madarasova Geckova et al. 2010;
Teachman and Paasch 1998), and numerous studies have indicated that family SES influences
the educational aspirations of the children (Bowden and Doughney 2009; Teachman
1987). For instance, Bowden and Doughney (2009) found that students from
highSES backgrounds have greater educational aspirations than their peers from low-SES
Summing these together, the research on SES have provided an insight into inequality
associated with educational outcomes.
Questionnaires have typically been used to obtain data on students’ socio-economic
backgrounds and majority of SES measures is known to be obtained through interviews
or surveys with parents (Ensminger and Fothergill 2003). Other less frequent sources
of SES information is self-report from the youth (Ensminger and Fothergill 2003). The
three variables normally asked about and used for measuring student/family SES in
educational research (either as single indicators or in combinations) are as follows: (1)
parental education, (2) parental occupation and (3) household resources or possessions
Investigating the ways that SES is utilized and measured in research over a ten year
period in North American Journals on children and adolescents, Ensminger and
Fothergill (2003) found that overall, education was the most common indicator of SES, it was
used 45% of 359 articles with SES included. Income was used in 28% of the articles,
occupation was used in 14% of the articles, and participation in various means tested
programs was used in 12% of the articles. Home resources or possession was not as a
measure. However, home possession data collected from young children have been
found to be much more reliable compared to information children provide about their
parents’ education, jobs, and income (Buchmann 2002; Keeves and Saha 1997;
Postlethwaite 1999; Yang and Gustafsson 2004). For instance as Buchmann (2002, pp. 181–182)
[A] careful assessment of the reliability and validity of home possessions as a
measure of SES within countries and as a construct that holds cross-nationally may
determine that home possessions data can provide better and more comparable
measures of socioeconomic status than parental education and occupation.
Moreover, using PIRLS 2006 data, Caro and Cortés (2012) found that parental
occupational status was a better indicator of SES in the wealthier societies, whereas home
possessions was a stable and reliable measure of SES in poorer societies. Irrespective
of home possession, “home possessions play a less important role in measuring SES for
wealthier societies” (Caro and Cortés 2012, p. 26).
In this study, we used data on home amenities to profile students’ SES. We chose this
approach because in addition to the above reasoning, student response when
reporting parental education and occupation in educational research has been associated with
high levels of non-response patterns and also a lack of comparability across countries
(Schulz 2005). For instance, in TIMSS 2011 high percentage of Ghanaian students were
unaware of their parents’ education (IEA 2012). Moreover, home possessions indicate a
family’s lifestyle and socio-economic well-being, and more often than not are not
influenced by a sudden change in income, education, or occupation (Yang and Gustafsson
2004). Furthermore, home support and demographic variables have been found to
significantly reduce the effect of poverty on literacy development and children’s academic
growth (Entwistle et al. 1997; Lee and Croninger 1994).
The present study
The purpose of this study is to profile students’ SES on the basis of their reported home
resources, based on the TIMSS 2011 study. The following hypotheses guide the study:
more than one student SES profile exists, and membership in the different profile groups
is associated with several demographic home resources. The study also assumes that if
we want to compare the distribution of educational achievement across/within society,
a sound measurement of the SES of a person, group, or geographical region is important
so as to capture and understand changes to the structure of a society, to understand the
level of stratification or inequality in or between societies, and to understand the
intergenerational change of social status over time (Oakes n.d.; Oakes and Rossi 2003; Wong
This paper extends the literature on SES because indigenous research and theorizing
are integral part of establishing a more useful and universal theories. Moreover, cultural
differences in SES can challenge the foundations of current theories and provide new
ways of looking at the relationship between SES and educational outcomes. This paper
uses TIMSS 2011 data, which is a more representative sample of developing country
However, a deeper understanding of the complex interplay between home resources/
possesses is paramount for helping society formulate policies that will assist children
from disadvantaged home in particular. Moreover, there is a paucity of SES studies in the
The study focuses on Ghanaian eighth graders who participated in the TIMSS 2011. The
sample consists of 7323 students (47% girls, average age of 15.81) involving 161 schools/
classrooms. Participation coverage was 100 percent, with school-level exclusions
consisting of special education schools and small schools with fewer than 10 students
(Martin and Mullis 2012).
Weighting and clustering
The analysis was based on TIMSS TOTWGT, which ensures that the weighted sample
corresponds to the actual sample size. Another reason for using the sampling weights
was to avoid bias (Bosker and Snijders 2011). Because class was used to uniquely identify
the sampled classrooms in the data, it was used as the clustering variable.
The SES measures involve survey questionnaire concerning 11 household resources
from the TIMSS 2011 (Table 1). The 11 items were used in the LCA, and were selected
on the basis of responses from students on a number of general household items. The
question was “Do you have any of these things at your home?” The items are shown in
Table 1. Items 1–5 were common to all participating countries but items 6–11 were
specific to Ghanaian students.
Table 1 Variables used in the study
1. Female 2. male Yes 1 1
Data analysis strategy
The analysis proceeded as follows. First, LCA was used to classify students into groups
based on their reported 11 home resources (see Table 1: socioeconomic measures). We
used a discriminant analysis to verify the degree to which groups were accurately
classified (Hair et al. 2010). Measurement invariance of item thresholds and class probabilities
across gender were evaluated. We used IBM SPSS version 22 (IBM Corp 2013) for the
discriminate analysis. For other analysis, we used the statistical package Mplus, Version
7.2 (Muthén and Muthén 1998–2012).
For the LCA, the analysis was based on the Mplus robust maximum likelihood
estimator (MLR) with robust standard errors. Mplus complex mixture data analysis was
employed to account for the clustering (hierarchical structure) of the data. For the LCA,
2000 random sets of start values and 100 initial stage iterations were used, to address
any problem of local maxima, (Geiser 2013; Muthén and Muthén 1998–2012;
Uebersax 2000). In the LCA process, missing data were treated using the Mplus feature of full
information maximum likelihood (FIML) (Asparouhov and Muthén 2010; Rubin 1987;
Schafer and Graham 2002; Schafer 2010).
Once the best LCA model was obtained, we then tested for the gender invariance of
class proportions and probabilities. An acceptable invariance model meant that male
and female students have been sampled from the same population, have similar class
proportions and conditional probabilities, and have responded similarly to the items.
Discriminant function analysis was used to determine which variables discriminated
between the groups and how accurately individuals were classified into groups on the
basis of selected variables (Tabachnick and Fidell 2001). Thus, the purpose of the
discriminant function analysis was to evaluate the validity of the SES groups.
Classifying students into socio‑economic profiles and goodness of fit
The first step in an LCA is to determine the number of groups, which should be well
defined by well-differentiated profiles (Marsh et al. 2009; Pastor et al. 2007). In LCA
research, the literature advises against using goodness-of-fit as a “golden rule” in
identifying the number of latent class (Markland 2007; Marsh et al. 2004, 2009). Opinions
differ on best to arrive at the appropriate number of groups in LCA analysis.
Consistent with the LCA norm, in this study, solutions with varying numbers of classes/groups
were estimated, and the one that make sense in relation to substantive theory, common
sense, the nature of the groups, and group interpretability was used (Collins and Lanza
2010). In addition, the goodness-of-fit indexes and tests of statistical significance were
taken into account (Collins and Lanza 2010; Marsh et al. 2009).
To compare the models’ fit with the different number of classes; a
Vuong–Lo–Mendell–Rubin (VLMR) (VLMR: Lo et al. 2001) test in addition to Bayesian information
criterion (BIC) and sample-size-adjusted BIC (SSA-BIC) were used. These have been
shown to help identify the correct number of latent profiles/classes (Nylund et al. 2007;
Tofighi and Enders 2008). The VLMR test is based on the same principle as the LR
difference test. The significant values of the VLMR test show that the estimated model fits
significantly better than the model with one class less (Nylund et al. 2007).
Moreover, the latent class probabilities (Table 3), which indicates how individuals
are assigned to their respective classes, were used for the class profiling. Furthermore,
the average latent class assignment probabilities were accessed with values on the main
diagonal being equal to or greater than 0.80 (Geiser 2013). As a guideline, the size of the
smallest group of an acceptable solution should at least exceed 5% of the sample (Chow
et al. 2012; Marsh et al. 2009). Table 2 lists the fit information for the models with one
through five groups/classes. The BIC and the SSA-BIC indexes continue to decrease
across the range of models considered, suggesting no specific number of groups. This
may be due to the large sample size, as BIC is sample size dependent (Marsh et al. 2009).
The VLMR results were inconsistent, being highly significant (p < .001) for the two-class
solution but only marginally significant (p < .05) for the three-class solution. The VLMR
results for the three-class solution were the best because the four-class solution was not
as interpretable as the three-class solution. Average latent class probabilities for the most
likely latent class membership were above the accepted cut-off mark (>0.70).
Moreover, an inspection of the log likelihood values indicated a sharp decrease from
the 2-class solution to the 3-class and a very smooth decrease thereafter. The four-class
solution contained a boundary estimate (two response probabilities were estimated to
be exactly 0). The three-class solution had the highest entropy estimate (0.633 vs. 0.569
for the 3- and 4-class models, respectively), suggesting greater classification uncertainty
with the extraction of one additional class. In addition, the log likelihood increased
smoothly to reach a stable maximum in the 3-class solution compared to the 4-class
model. The three-class solution was identified as the most optimal, because it appeared
to provide a more reasonable representation of the data. The three-class solution was
easy to interpret (and more parsimonious), and was further confirmed by the unique
characteristics across the groups of the three-class model. Table 3 shows the latent class
probabilities and Fig. 1 the estimated probability plots for both responses. The group
membership information on each student was saved and used for further statistical
The latent class analysis
Table 3 contains the response probabilities—the probability of being in a particular
latent class and responding yes or no to the 11 latent class indicators obtained in the
3-class model. The first column (class 1) shows approximately 24% of the sample having
a high item response. The students in this class have higher probabilities endorsement
Table 2 Indices for the latent class analysis
1 11 −47,836.032 95,769.878 95,734.922 0.850 0.003
2 23 −44,162.136 88,528.793 88,455.704 0.710 0.000 0.002 0.862
3 35 −43,715.660 87,742.546 87,631.324 0.633 0.035 0.104 0.104
4 47 −43,584.907 87,587.747 87,438.391 0.569 0.460
5 59 −43,470.071 87,464.782 87,277.293 0.614 0.414
BIC Bayesian information criterion, SSA-BIC sample‑size ‑adjusted Bayesian information criterion, VLMR Vuong–Lo–Mendell–
Rubin, p Number of parameter estimates
Es mated Probaility plots
Fig. 1 Estimated probability plots indicating the socio‑ economic profiles of the students
for all items (electricity [0.943], dictionary [0.880], books [0.865], calculator [0.795], tap
water [0.704], study desk [0.744], computer [0.686], car/motorbike/bicycle[0.662], chalk/
blackboard [0.640] own room [0.478], internet [0.314],). For “own room” and “internet”
the probabilities were lower than expected but not surprising for the sample per say. Yet,
this class still had the highest probability endorsement. Due to this class’s unique
characteristics, it was named the ‘high-SES’.
In the second column (class 2) approximately 45% (4-items) have a high probabilities
endorsement (i.e. books [0.792], electricity [0.709], study desk [0.626], dictionary [0.528],
and calculators [0.456]). Other items had a moderate endorsement probability except
having a computer and internet access. Given this modest endorsement, the class was
named ‘middle-SES’. In the third column (class 3), approximately 30% of the sample fell
within this category and had very low item response endorsement probabilities. The two
highest probabilities across this class were electricity [0.445] and books [0.359]. Due to
the pattern of endorsement, the class showed a pattern of students with a very low-SES,
and was thus named ‘low-SES’. Most students fell within the middle-SES class, followed
by low-SES and high-SES. The class profile plot (Fig. 1) shows how the classes differ from
The LCA was followed by a discriminant analysis, used to determine which variables
discriminated between the groups and to verify the degree to which groups were
accurately classified. The discriminant analysis revealed two discriminant functions. Because
there were three groups, only two discriminant functions were possible. The
discriminant analysis based on the eleven household items was able to correctly classify 92.2%
of the individual students into their appropriate SES group (based on the three LCA
groupings). The two discriminant functions were statistically significant. However, the
Table 3 Latent class probabilities for the three SES classes
first one account for 89.73% of the between-group (explained) variance while the second
accounts for the remaining between-group variance (i.e., 10.27%). The squared canonical
correlations, and the effect sizes for the discriminant functions, were 0.823 and 0.347,
The stability of the classification procedure was checked by a cross-validation run.
Approximately 25% of the cases were withheld from the calculation of the classification
function in this run. For 75% of the cases from which the functions were derived there
was a 92.2% correct classification rate. For the cross-validation cases, correct
classification was 92.1%. This indicates a high degree of consistency in the classification scheme.
The discriminant function plot (Fig. 2) shows that the first function differentiated
students in the high-SES from those in the low-SES group, and the second function
differentiated the middle-SES group from the two other groups. In other words, it took both
discriminant functions to separate the three groups from each other. This finding
supports the validity of the three groups derived from the LCA. Most of the variance could
be explained in terms of two discriminant functions.
Test of invariance across students’ gender
The gender invariance of the class probabilities was tested to ascertain if the class
probabilities (Table 3) were the same across students’ gender and to help generalize the
findings. Two models were tested, one freely estimating item thresholds and class
probabilities across students’ gender (M1) and another freely estimating item thresholds across
the groups, fixing class probabilities and classes across the groups (M2) to be invariant.
The entropy (M1 vs. M2: 0.793 vs. 0.791), BIC (M1 vs. M2: 97,865.560 vs. 97,864.250),
and L2 (M1 vs. M2: −48,617.107: scaling correction factor 2.971 vs. −48,625.344: scaling
correction factor 2.870) were much the same.
The difference between the unconstrained (M1) and constrained model (M2) was not
significant, ∆L 2 (2) = 2.553, p = .279, suggesting the constraints did not significantly
affect the model fit. The two models were therefore not significantly different. The
models indicated that the three SES classes were the same across students’ gender. We can
then generalize that in Ghana students socio-economic background can be categorized
into low, middle and high, based on students’ home resources.
The reality of profiling SES is a complex enterprise far beyond TIMSS and other large
scale studies. Using TIMSS 2011, we first use LCA to profile students into various SES
based on their reported home resources. We then used discriminant analysis to verify
the degree to which these groups are accurately classified and gender invariance was
used to test if the class probabilities were the same across gender. It can be concluded
from the results of the present research, that students’ reported household resources
provide comprehensive data on family background. We think the approach considered
here will serve as a practical guide for educational researchers seeking to construct a
reliable SES measure in low-income societies and in studying educational inequalities
related to family background when using large scale international studies.
The analysis identified three classes of students based on reported home resources
namely: high-SES, middle-SES, and low-SES. These classifications accord with the
literature (e.g., Sirin 2005). The discriminant analysis was able to correctly classify 92.20% of
the individual students into their appropriate SES group. A cross-validation run was
carried out and the classification was 92.10%, which indicating a high degree of consistency
in the classification scheme.
High-SES students’ were those with access to all the listed home amenities. The items
that differentiated students from the high- and middle-SES backgrounds were access to
computers and the internet, and having electricity at home. Those in the low-SES class
were students with a high probability having none or very limited access to the listed
household items. Students’ from low-SES homes lacked basic access to educational
materials (e.g. books). This finding is in line with the literature in that low-SES families
have limited financial resources, which restricts their ability to provide their children
with learning materials (e.g., Orr 2003). We need to recognize that access to these
amenities is an element of students’ SES, which is also affected by parents’ financial resources.
The most significant limitation of the study is that all measures are self-reports and
thus subject to desirability biases. Another limitation is that in developing countries
such as Ghana, goods are frequently purchased through nonmonetary systems, which
makes it difficult to validate respondents’ claims about home possessions. However,
the meaning of home possessions differs across cultures even within a country.
Moreover, the home resources used as a measure of SES were in this study not exhaustive
enough, although the resources listed were sufficiently broad to allow for a
differentiation of living standards across all households. For example, items such as tablets should
be included in the next round of the survey. One practical problem, however, is the
current lack of standardization across countries with respect to the core group of
household items in the TIMSS data set. For instance, in the TIMSS 2011, six of the 11 home
resources were country-specific whereas five were common to all participating
countries. However, in most cultural settings different meaning are attached to these
common household items. To adopt the SES approach universally, large scale international
studies and survey developers should consider defining a set of socioeconomic variables
that can be collected evenly across countries. The strength of the study is that the data
set is a country representation and the robust methodology allows for a generalization of
the results to Ghanaian grade eight students.
This study serves as a practical reference for education researchers and policy-makers
in their efforts to better understand the SES composition in Ghana and to provide equal
educational opportunities for all. Organizations may also use the findings of the study as
a tool for understanding student composition in order to form better educational policy.
For instance, to help improve schools in low-SES vicinities, governments and
policymakers should focus on teaching and learning, creating a positive school culture, and
seeking external support and resources (Muijs et al. 2005).
In accordance with awareness of the educational and achievement disparities between
different SES groups, the present findings can help educators and policy-makers make
informed decisions and provide the right incentives to under privileged families. The
findings can also help researchers explore other factors that might have an influence
on students’ SES. Most importantly, the study makes an important contribution to the
field, because where reliable measures of SES are not available; as the case of our present
data set (e.g., parental education and parental occupation), home resources are the most
practical alternative. Moreover, the variables chosen embody strong theoretical
consideration (Filmer and Pritchett 1999; Schulz 2005), and a most robust method was used to
EAB drafted the manuscript. MSH was EAB doctoral supervisor and shared his expertise during the preparation and the
development of the manuscript. The work as a whole is an extensive collaboration and discussion between EAB and
MSH. Both authors read and approved the final manuscript.
Emmanuel Adu‑tutu Bofah had his Ph.D. at University of Helsinki, Finland, under the supervision of Professor Markku S.
Hannula. He obtained his MA (Educational Science) and MSc (Social Science) from the University of Turku, and Helsinki
respectively, Finland. His research interest is mathematics affect and its relationships to students’ achievement. He
has also written methodology papers on cross‑ cultural research on affect. With Markku S. Hannula, he has published
on methodological aspects of cross‑ cultural studies for both Sage and Springer. Markku S. Hannula is a professor of
mathematics education and the Director of the Research Centre for Mathematics and Science Education (RCMSE) in the
Department of Teacher Education at the University of Helsinki. His main research interests are the affective domain and
problem solving in mathematics and he uses both qualitative and quantitative methods. He serves currently as a board
member for the European Society for Research in Mathematics Education.
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