A Short Measure of Childbearing Motivations: Development and Psychometric Evaluation in Polish Samples of Adults and Adolescents

A major component of human reproductive decisions is tied to biology: people are naturally predisposed to manifest behaviors and experience feelings that encourage procreation (e.g., sexual drive, responsiveness towards infants; Miller, 1992; Morgan & King, 2001). Although advances in biology have played an important role in understanding human reproduction, over the past decades social theorists have recognized the growing role of rational choice in childbearing decisions (Becker, 1960, 1986; Burch, 1980; Crosbie, 1986; de Bruijn, 1999; Hoffman & Hoffman, 1973; Miller, 1983; Morgan & King, 2001). Along with growing access to modern contraception, as well as effective infertility treatments, humans’ ability to successfully control and regulate their own reproductive processes has increased. In fact, Miller (1983) stated that the human history of reproductive behaviors is a history of “progressive dominance of choice over chance” (p. 1198). In a similar vein, Morgan & King, (2001) noted that both parenthood and childlessness are increasingly the result of an active decision-making process, rather than a natural stage in the life course. Thus, the question arises: what guides people’s conscious decisions regarding childbearing?

Pioneering contributions to this debate have stemmed from micro-economic theories that entered the literature in the late 1950s (Becker, 1960, 1965; Leibenstein, 1957), and the first explicitly psychological conceptualization of fertility decisions was offered over two decades later (Hoffman & Hoffman, 1973). To explain fertility motivation, Hoffman & Hoffman, (1973) introduced the “value of children” concept, which referred to the parents’ needs that children satisfy, such as the need for affiliation, competence or morality. Thereby, Hoffman and Hoffman emphasized the role of emotional aspects of parenthood (e.g., children being a source of novelty, stimulation, and affection) as a major factor determining fertility choices.

Drawing on Hoffmans’ work and own research, Miller (1994); (Miller & Pasta, 1993) presented the TDIB model suggesting that human reproductive behavior is a consequence of a four-step decision-making process (see Fig. 1). Firstly, as a result of one’s genetic predispositions and early life experiences, childbearing motivations (i.e., motivational traits) are formed. Miller (1995) distinguishes two unipolar dimensions of childbearing motivation: positive (overall tendency to react favorably towards various aspects of childbearing) and negative (tendency to react unfavorably). Over the course of a person’s life, these tendencies become realized to produce a psychological state of desire (i.e., a wish to have/not have a child). The strength and direction of the desire depends on which and how much certain motivations prevail. Once the childbearing desire is shaped, it is transformed into an intention that represents a decision-driven commitment to a goal (achievement or avoidance of childbearing) and a feasible plan for its realization. Finally, the intention is implemented through proceptive or contraceptive behaviors.

The TDIB approach has been applied in a variety of research contexts (e.g., Alexander et al., 2019; Gray et al., 2013; Stanford & Porucznik, 2017; Varas & Borsa, 2021) and only recently an attempt has been made to apply this approach in a large-scale research program (Mynarska & Raybould, 2020). However, such attempts might be problematic given the complexity of the measurement of childbearing motivations.

The CBQ (Miller, 1995), designed to measure childbearing motivations, covers a set of 50 items that represent positive and negative childbearing motivations (PCM and NCM). Respondents are asked to evaluate how desirable (for PCM) or undesirable (for NCM) the given consequences of having children are, using a 4-point scale from 1 (not at all) to 4 (very). The items are adjusted for men and women. For example, women evaluate the desirability of: “Feeling a baby move and kick inside me,” and men assess the statement “Feeling a baby move and kick inside my wife.” All items can be found in Miller (1995).

Importantly, the CBQ offers a possibility to measure childbearing motivations at different levels of specificity (Miller, 1995), from the overall strength of the PCM and NCM to specific dimensions of these motivations. The PCM scale comprises five dimensions concerning (a) joys of pregnancy, birth, and infancy; (b) instrumental values of children; (c) feeling needed and connected; (d) satisfaction of childrearing; and (e) traditional parenthood. Similarly, the NCM scales evaluate four negative aspects of having children: (a) discomforts of pregnancy and childbirth; (b) parental stress; (c) negatives of childcare; and (d) fears and worries of parenthood. The PCM/NCM dimensions were developed in an explorative way, based on correlational studies concerning reasons for having or not having a baby. Interpretations of these dimensions, substantiated by the CBQ-based research (summarized recently in Miller, 2021), refer to the positive or negative affects that underlie childbearing motivations (affectional bond for PCM, and fear and anxiety for NCM), and to different developmental periods (Miller et al., 2021). Brief descriptions of the subscales together with their abbreviated names are presented in Table 1.

The CBQ has been translated from English to several other languages (e.g., Italian: Sina et al., 2010; Iranian: Pezeshki et al., 2005; or Polish: Mynarska & Rytel, 2014) and several other scales that draw on it were developed (e.g., Guedes et al., 2015; Matias & Fontaine, 2013). However, the length and complexity of the measure make its application in large-scale surveys problematic. Consequently, to the best of our knowledge, the childbearing motivation and its relation to fertility desires and intentions, have not been tested in such large-scale studies, on representative, population-based samples. Moreover, as the CBQ was developed in an explorative way, the subscales include different numbers of items (from two to eight) and vary in internal consistency (Guedes et al., 2015). The goal of our study was to develop a substantially shorter version of the CBQ and to provide first evidence on its reliability and validity using two independent samples of adults and late adolescents.

To develop the CBQ-SF, we used anonymized secondary data collected in Poland in 2013 (Mynarska & Rytel, 2018, 2020) and 2016 (Mynarska & Rytel, 2022). The data was obtained in the research projects, funded by the Polish National Science Center (grant numbers: 2015/17/B/HS4/02086 and 2011/03/D/HS4/05358). Ethics self-assessment was included in the project proposals in both cases and accepted by the reviewers. Data collection procedures followed institutional ethical guidelines and complied with the principles of the Declaration of Helsinki. For the secondary use of data, no ethical approval was required by the Institutional Review Board.

To assess the reliability of the scales, we tested internal consistency using Cronbach’s α (1951) and McDonald’s ω (1999). We took into consideration that although Cronbach’s α is the most commonly reported reliability coefficient, many methodologists consider McDonald’s ω to be a preferable estimator of a scale’s reliability due to its less restrictive assumptions (Hayes & Coutts, 2020; McNeish, 2018; Peters, 2014; Revelle & Zinbarg, 2009; Trizano-Hermosilla & Alvarado, 2016; Widaman et al., 2011). As recommended for ordinal data (Elosua & Zumbo 2008; Gadermann et al., 2012; Zumbo et al., 2007) we used polychoric correlations to compute ordinal versions of α and ω, as well as ordinal average inter-item correlations.

To verify the underlying structure of the CBQ-SF we conducted confirmatory factor analysis (CFA). As we were most of all interested in subdimensions of the PCM and NCM, we specified two separate models, for the two dimensions. The PCM and NCM are conceptualized as orthogonal dimensions in the TDIB model (Miller, 1995) and their distinctive character has been documented in previous studies, including ones conducted in Poland (Miller et al., 2021; Mynarska & Rytel, 2014, 2018). To confirm the structure of PCM, we tested three possible solutions: (a) a baseline unidimensional model where all 15 items loaded one general factor (i.e., PCM); (b) a five-factor model that contained five dimensions of PCM (i.e., JoysPBI, InstVoC, FeelNaC, SatChR, and TradPar); and (c) a final model with five first-order factors and one second-order factor. Similarly, for NCM we tested: (a) a unidimensional model where 12 items loaded the general factor of NCM; (b) a four-factor model that included four dimensions of NCM (i.e., NegChC, FaWPar, DiscPaC, and ParStr); and (c) a final solution with four first-order factors and one second-order factor. The factors were not constrained for orthogonality. In line with recommendations of Beaujean (2014) and Brown (2015), before proceeding with second-order structures, we evaluated lower-order models to ensure that correlations between factors are strong and therefore a higher-order solution would be appropriate. To account for the ordinal nature of CBQ items, we used polychoric correlations as an input and diagonally weighted least squares (DWLS) estimator to obtain fit measures. The model fit was assessed with the root-mean-square error of approximation (RMSEA), standardized root-mean-square residual (SRMR), Tucker–Lewis index (TLI), and comparative fit index (CFI). Values ≤ 0.08 for RMSEA and SRMR, and values ≥ 0.90 for CFI and TLI, indicated good fit to the data (Byrne, 2010; Gana & Broc, 2019).

Finally, once the goodness of the PCM/NCM measurement was established, we employed structural equation modeling (SEM) to investigate the relationship between childbearing motivations and childbearing desire and intention. We adopted a two-phase strategy in accordance with Mueller and Hancock’s (2008, 2018) recommendations and separated structural equation models into their measurement and structural parts. Given that the measurement model for the PCM and NCM was verified in the previous step, we specified these as latent variables being measured by a single indicator—a composite score created by summing items corresponding to each scale. To take the measurement error into account, the residual variance of each scale was fixed to (1 − α) × Scale Variance (Hayduk, 1987; Sagan & Pawełek, 2014). Next, we specified the structural portion of the model (i.e., PCM/NCM predicting childbearing desire, and desire predicting childbearing intention), as well as the measurement part concerning childbearing desire and intention. In Study 1, we performed SEM using a maximum likelihood estimator since the normality assumption for items and subscales was satisfied (skewness and kurtosis within −2 and +2 range, Gravetter & Wallnau, 2014; see ESM 2a). Due to violations of normality assumption (kurtosis > 2; see ESM 2b) in Study 2 we fitted the model using a robust maximum likelihood estimator with Satorra–Bentler (1994) correction. All analyses were conducted on the final pool of items. Analyses were performed in RStudio (RStudio Team, 2020) with the lavaan (Rosseel, 2012), psych (Revelle, 2021), and Hmisc (Harrell, 2022) packages. To aid transparency of our results, the data, syntax, and other electronic supplementary materials are available at https://​osf.​io/​f73jq/​?​view_​only=​d24b902fbac54ee6​a0a4b299ede23396​.

The purpose of this study was to create an abbreviated version of the CBQ, originally developed by Miller (1995) to measure childbearing motivations. It was crucial that the CBQ-SF permits measurement of the overall strength of PCM and NCM as well as of their subdimensions. Such multidimensionally is theoretically grounded in Miller’s TDIB model (1994, 1995). The importance of distinguishing different aspects of childbearing motivations was also stressed by other researchers (e.g., Guedes et al., 2015).

The first selection of items was based on the theoretical description of subdimensions of PCM and NCM (Miller et al., 2021). To achieve the desired psychometric properties, in the CBQ-SF, we tested several alternative solutions and also introduced new items recently developed in the Polish context (Mynarska & Rytel, 2014). The development and evaluation of the CBQ-SF was carried out on the sample of Polish childless adults (Study 1). Then, we psychometrically cross-validated the final version of CBQ-SF on a sample of childless late adolescents (Study 2).

The internal consistency of PCM, NCM, and their respective subscales was good to excellent, similar to the estimates obtained for the CBQ-PL (Mynarska & Rytel, 2014, 2018) and CBQ (Miller, 1995; Miller et al., 2008). In fact, three of the subscales (SatChR, TradPar, and DiscPaC) in CBQ-SF showed considerably larger reliability estimates than those shown for the full version by Miller et al. (2008). The PCM and its subscales performed slightly better than the scales related to negative motivations and this result is also consistent with earlier studies examining the original and translated versions of the CBQ (Jagannathan, 2006; Miller, 1995; Miller et al., 2008; Mynarska & Rytel, 2014; Pezeshki et al., 2005; Sina et al., 2010; Van Egeren, 2003). Overall, the introduction of new items together with focusing on the central ones for each of the subscales resulted in a highly consistent and economic measure of childbearing motivations. Consequently, the CBQ-SF can be applied to measure childbearing motivation at the most detailed level (Miller, 1995), allowing identification of an individual’s motivational profile (Guedes et al., 2015).

To test the factorial structure of the scale, we performed a series of CFAs. Our results supported the hypothesized multidimensional and hierarchical structure of the CBQ-SF. Childbearing motivation as measured by the selected 27 items comprises two relatively independent unipolar dimensions: PCM and NCM. Within these broad dimensions, specific aspects can be distinguished. In our study, we confirmed the five-factor structure of PCM, as well as the existence of four factors within NCM. The overview of factor loadings in the CFAs provided further support for our item selection for each subdimension.

Finally, we demonstrated that the CBQ-SF can be meaningfully used to study the process of reproductive decision-making within the TDIB model (Miller, 1994), particularly in terms of the motivations-desire-intention relationship. The results of SEM confirmed these links in both samples. The PCM was a positive predictor of childbearing desire, while the NCM was negatively related to it, and childbearing desire predicted intention to become a parent. Several details need to be highlighted in these results.

First, in both analyzed samples, we found a negative, albeit low correlation between PCM and NCM. While this finding is not in line with theoretical assumptions, it is not altogether unexpected. A recent comparison of PCM–NCM correlation coefficients in different samples in the US, Iran and Poland revealed a variation in the PCM–NCM relationship across these countries (Miller et al., 2021). Interestingly, in the US and Poland, the correlations were—depending on the sample—close to zero or negative, while the Iranian data showed close to zero or positive correlations. Although the correlations, if they occurred, were generally low, these findings suggested that the relationship between PCM and NCM needs to be examined in more detail. The question remains open under what (macro or micro level) circumstances people’s motivations towards childbearing are polarized or ambivalent.

Second, we found the NCM to be a considerably weaker predictor of childbearing desire than the PCM, which means that in some cases perceiving negative aspects of parenthood as highly unwanted was not necessarily related to a lower desire to have a child. This finding is consistent with previous results based on the original and translated versions of CBQ (Miller, 1995; Mynarska & Rytel, 2014; Pezeshki et al., 2005). It is possible that expressing concerns about having children (high scores on selected NCM subscales) simply indicates a higher sense of awareness and responsibility in a decision to have a child. In fact, some studies have shown that one NCM subscale (FaWPar) is positively linked to childbearing desire (Miller et al., 2000; Mynarska & Rytel, 2020). A possible explanation for this result is that the FaWPar scale relates to the altruistic fear for children rather than to the narcissistic fear, which is central to other NCM subscales (cf. Table 1). The content of this dimension may account for the slightly lower internal consistency of the NCM scale compared to PCM. It should also be noted that FaWPar showed the lowest reliability coefficients in both our samples (ω: 0.71–0.76). Overall, the FaWPar subscale and its relation to other childbearing motivations should be carefully considered in future studies, using the short as well as full versions of the CBQ.

Finally, as our structural models covered motivations, desire and intention, the relation between the two latter constructs should be noted. Even though this relation is not central for the purpose of our study, it is noteworthy that it was markedly weaker in the cross-validation sample. This finding can be attributed to the age of the participants, who at the time of data collection were at the onset of emerging adulthood (Arnett, 2000). This developmental period is characterized mainly by identity exploration, self-absorption, and instability in terms of work, education, and relationships (Santrock, 2019). Typically, parenthood is postponed until these aspects of an individual’s life are established. Only then is childbearing intention, namely a concrete, feasible plan to have a child in the foreseeable future, formed (Hayford, 2009; Rackin & Bachrach, 2016).

Some limitations to this study need to be considered. First, even though the samples were heterogenous in terms of sex, age, socio-economic background and place of residence, they consisted of childless individuals only. The original CBQ showed good psychometric properties in studies on those with and without children (Miller, 1994, 1995), but the same needs to be verified for the short form. Second, the data collection in both studies was limited to self-reported questionnaires and cross-sectional design. Although addressing these issues goes beyond the scope of this paper, comprehensive, longitudinal investigations are needed to fully validate the causality in the TDIB model. Further, the presented results are limited to the Polish context. Since additional Polish items, absent from the CBQ, were used in the CBQ-SF, it is not possible to corroborate our findings with the existing US or Iranian data. It is crucial that our short measure is validated in other cultural contexts to verify that the aspects of childbearing reflected in the newly introduced items are important among other nationalities.

Finally, as we developed the CBS-SF based on the existing data, we were not able to test potential new items. The subscale related to discomforts of pregnancy and delivery (DiscPaC)—which originally consisted of just two items—was supplemented with the additional Polish item on parents’ lack of sleep when a child is born, as this item was the only one available that relates to early physical consequences of childbearing. While the expanded subscale performed well, another item, directly related to pregnancy experiences could possibly further improve its properties: “Having a pregnancy that interferes with my health/Seeing a pregnancy interferes with my wife’s health.” Moreover, it is likely that since the data collection (2013 and 2016), some new aspects of childbearing have become relevant. In particular, we were not able to consider any new NCM items related to current global changes, for example, fear for the child’s life in times of climate change or a pandemic (Aassve et al., 2020; Lindberg et al., 2020). Future studies are needed to determine how global changes translate into individuals’ decision about becoming a parent.

Despite these limitations, the CBQ-SF has proven to be a feasible and psychometrically sound measure to assess childbearing motivations. It can be successfully used to establish the relation of these motivations to other elements in the process of reproductive decision-making (i.e., childbearing desires and intentions) in different developmental periods (from late adolescence to middle adulthood). Importantly, the CBQ-SF meets the recommended criteria for pragmatic measures in public health research: it is easy to use and broadly applicable, brief, inexpensive, unlikely to cause harm, related to theory, and facilitates research on the topic that is important to various stakeholders (Glasgow, 2013; Glasgow & Riley, 2013). Consequently, it can be successfully used to support the design and implementation of evidence-based policies, preventive actions, or psychological interventions in the area of family planning. Given that sustaining low fertility is one of the main concerns for policymakers (Wilkins, 2019), the CBQ-SF might be particularly useful as it allows to look deeply into psychological determinants of childlessness (or parenthood). Additionally, the CBQ-SF can be applied in a range of psychological interventions. For individuals, it can help to identify the predictors of perinatal depressive and anxiety symptoms. For couples, the CBQ-SF may be valuable for recognizing the underlying roots of the relationship crisis related to inconsistent childbearing motivations between partners. Finally, the CBQ-SF gives insight into specific sources of parental anxiety, anger or dissatisfaction—emotions that may potentially disturb the developing patterns of parent-child interactions. These, in turn, have a crucial impact on a child’s emotional development (Bowlby, 1988; Schore, 2015), specifically in terms of the security of the child’s attachment (Miller et al., 2002; Miller et al., 2008; Miller et al., 2009). Thus, we recommend that the CBQ-SF be used by practitioners in individual and couples/family therapy settings. Once different motivational profiles and their consequences are recognized, these individuals, couples and families may benefit from special guidance and support.