Reporting of Socioeconomic Status in Pediatric Language Research.

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Reporting of Socioeconomic Status in Pediatric Language Research Ella Inglebret,a Shana Bailey,b Jeanne Amie Clothiaux,c Amy Skinder-Meredith,a Kayla Monson,a and Lesli Clevelandc

Purpose: This study examined language-focused research articles published in 3 American Speech-Language-Hearing Association journals to: (a) determine the proportion that reported the socioeconomic status (SES) of pediatric participants and (b) identify the indicators used to represent SES in these articles. Method: Researchers reviewed articles published from 2000–2015 in Language, Speech, and Hearing Services in Schools, the American Journal of Speech-Language Pathology, and the Journal of Speech, Language, and Hearing Research (language section) that involved pediatric participants and focused on language development, as well as on assessment and intervention for language disorders.

Results: For the 3 journals combined, 417 out of the total 652 (64%) pediatric language articles reported SES of the participants. Over the 16-year period there was an increase in SES reporting of 31.8% (55.6% to 73.3%). The types of SES indicators used represented education, income, and occupation. Conclusion: Although SES reporting for pediatric participants in language-based studies increased over the 16-year period examined, over 1 quarter of studies published in the 3 journals combined still do not report SES. This is a concern. When determining the generalizability of research findings to specific children, it is important for speech-language pathologists to be able to identify the SES background of research participants.

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For decades, researchers have focused on identifying universals in the language development process (Goldfield, Snow, & Willenberg, 2017). This emphasis was grounded in a need for basic understanding of the various dimensions of language acquisition (Brown, 1973; Chomsky, 1957; Slobin, 1985). In recent years, it has been increasingly recognized that language development varies across culturally and linguistically diverse groups, inclusive of children from different SES backgrounds (ASHA, 2004). The widely cited work of Hart and Risley (1995) indicated that by 3 years of age children from upper-middle SES families heard three times as many words and produced twice as many words, as compared to children from lower SES families. Variations in vocabulary and expressive language in relation to the SES of children’s families have been subsequently supported by other researchers (Dollaghan et al., 1999; Engle, Santos, & Gathercole, 2008; Hoff, 2003; Horton-Ikard & Weismer, 2007). Additional research has shown differences in SES background to be associated with variations in phonological awareness, literacy development, reading, and verbal problem solving (Connor & Zwolan, 2004; Dodd & Carr, 2003; Lundberg, Larsman, & Strid, 2012; McDowell, Lonigan, & Goldstein, 2007; Nittrouer, 1996; Pappas, Ginsburg, & Jiang, 2003).

n 2007, the American Psychological Association (APA) Task Force on Socioeconomic Status (SES) recommended that the SES background of research participants be identified in publications examining social behaviors. In 2011, this recommendation was echoed by Hammer in relation to research articles published by the American Speech-Language-Hearing Association (ASHA). The inclusion of SES data can provide critical insight into the pediatric research participants’ access to health care, nutrition, prenatal care, quality and quantity of neurocognitive stimulation, educational opportunities, and language stimulation (Asch et al., 2006; Bradley & Corwyn, 2002; Duncan & Magnuson, 2014; Roseberry-McKibbin, 2013). Any of these factors can influence language development, as well as the effectiveness of language assessment and intervention practices.

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Washington State University, Spokane, WA Youthful Horizons, Spokane, WA c Eastern Washington University, Spokane, WA Correspondence to Ella Inglebret: [email protected] b

Editor: Krista Wilkinson Associate Editor: Carol Miller Received November 21, 2016 Revision received March 24, 2017 Accepted May 5, 2017 https://doi.org/10.1044/2017_AJSLP-16-0229

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Disclosure: The authors have declared that no competing interests existed at the time of publication.

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Performance on norm-referenced tests of language ability and academic achievement has also been the subject of investigations. Several studies have resulted in SESbased variations on tests of receptive and expressive vocabulary (Allison, Robinson, Hennington, & Bettagere, 2011; Dollaghan et al., 1999; Horton-Ikard & Weismer, 2007; Qi, Kaiser, Milan, & Hancock, 2006). Performance on norm-referenced tests designed to examine language more comprehensively has also varied in relation to the SES background of children (Arriaga, Fenson, Cronan, & Pethick, 1998; Locke, Ginsborg, & Peers, 2002; Stanton-Chapman, Chapman, Kaiser, & Hancock, 2004). Based on analysis of data from a range of national-level sources, Reardon (2014) concluded that the disparity between performance on assessments of academic achievement in reading and mathematics for children from low-income and high-income families has increased substantially in recent years and continues to do so. Implementation of evidence-based practice calls for speech-language pathologists (SLPs) to consider the values, interests, and background, inclusive of SES, of the individuals they serve (ASHA, 2005). Existing research evidence establishes the need to consider the SES background of pediatric research participants in making decisions about the applicability of specific research findings to each child served. For SLPs to consider the SES-related characteristics of participants involved in particular studies, these characteristics must be identified in associated journal articles. Furthermore, Hammer (2011) advocates for a thorough description of participants’ characteristics to aid researchers in replicating studies and synthesizing findings across studies. She goes on to assert that this can then lead to identification of gaps in the research literature, as well as add to the professional knowledge base of both universals and variations that occur within and across groups. At this time, there has been no systematic examination of the SES reporting practices used in speech-language pathology research. The phenomenon of SES can be represented by a variety of indicators.

SES Indicators SES is a complex, multidimensional phenomenon (Grusky & Weisshaar, 2014). As a consequence, there is not clear agreement on how to measure it (Cowan et al., 2012; Hoff, Laursen, & Bridges, 2012). The APA Task Force on SES (2007) has described three approaches used to conceptualize SES. These three approaches involve examination of: (a) resource access, (b) gaps between groups along a continuum, and (c) power and privilege associated with social standing. The first approach, resource access, focuses on material and structural inequality and describes individuals and groups of people based on available resources using standard indicators, such as education, income, and occupation. These resources are viewed as shaping the experiences and opportunities of members of particular groups in daily life. The second approach involves a continuum that emphasizes the position of particular individuals

and groups relative to each other so that associated gaps and disparities in access to resources and associated consequences are identified. Indicators, including levels of education, income, and occupation, serve as the anchor points on a continuum. In addition, qualitative measures of status and inequality, including self-perceptions, are used, such as asking a student or family member if they have “enough money to get along” (Cowan et al., 2012, p. 17). The third approach focuses on power and privilege associated with the reproduction of social standing. Proponents of this approach advocate for unearthing the covert assumptions, attitudes, and behaviors underlying existing institutional policies and practices that perpetuate the social status of particular groups. However, SES indicators associated with this third approach have yet to be developed (Cowan et al., 2012). As an initial step in the systematic examination of SES reporting in the speech-language pathology professional literature, the current study primarily adhered to the resource access approach and the associated indicators representing education, income, and occupation (APA Task Force on SES, 2007). This approach aligns with Coleman’s (1988) conceptualization of SES as involving human capital (education), financial capital (income), and social capital (occupation). The extent to which basic human needs are met depends on an individual’s or group’s access to resources, such as knowledge gained through education, material goods and services gained through financial income, and social networks mediated through occupation. These three resource types provide the foundation for the overall development of children, including language development (Duncan & Magnuson, 2014; Heckman, 2014). Various SES indicators have traditionally been used to represent the fundamental resources of education, income, and occupation (Grusky & Weisshaar, 2014; Massey, 2007; Putnam, 2015). Education According to Massey (2007), education is “the most important resource in today’s knowledge-based economy” (p. 252). Higher levels of educational attainment relate to employment potential (Putnam, 2015), more expansive formal and informal social networks and support (Grusky & Weisshaar, 2014; Putnam, 2015), and increased health and well-being (APA Task Force on SES, 2007; Grusky & Weishaar, 2014). Various systems have been devised for categorizing education level. The focus might be on maternal, paternal, parental, and/or primary caregiver education levels. Education may be indexed as a continuous variable by reporting total years of education or categorized relative to high school graduation and attainment of higher education degrees (Hoff et al., 2012). For example, in a study examining the language environment for migrant, bilingual children participating in either a Head Start or a summer migrant preschool program, Wood, Diehm, and Callender (2016) used six categories of maternal education level. These included: (a) some high school, (b) high school diploma, (c) some college, (d) associate’s degree, (e) bachelor’s degree, and (f ) graduate degree. Using education as an SES

Inglebret et al.: Reporting of SES in Pediatric Language Research


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indicator carries the advantage of it being quantifiable and stable. Years of education will not decrease, and once a high school diploma or a higher education degree is awarded, it is retained for life. Income Income is another indicator commonly used to represent SES (APA Task Force on SES, 2007; Cowan et al., 2012; Grusky & Weisshaar, 2014). Income can determine the extent to which families have access to a range of basic resources, including food, shelter, and a safe living environment, as well as health care services (Putnam, 2015; Roseberry-McKibbin, 2013). Position relative to federal poverty guidelines, based on thresholds for before-tax cash income for families of various sizes and compositions (U.S. Census Bureau, 2016), is commonly used to categorize income levels. For example, in a study of print-concept knowledge of preschoolers, Justice, Bowles, and Skibbe (2006) obtained income data for each child’s household and compared it to federal poverty limits. In other cases, however, parents or caregivers of child research participants may be unwilling to share income-related information, due to its sensitive nature (Berkowitz, Traore, Singer, & Atlas, 2015; Braveman et al., 2005). One means to overcome this challenge is to use publicly available information regarding enrollment in programs designed to serve children from low-income families. Some government-sponsored programs restrict eligibility for enrollment, based on family income relative to the federal poverty guidelines. At the early learning level, low-income families are served by Early Head Start (pregnant mothers and infants and toddlers birth to age 3 years) and Head Start (children 3 to 5 years of age) (U.S. Department of Health and Human Services, 2016). At the K–12 school level, the National School Lunch Program (NSLP) provides free and reduced-price lunches for children from low-income backgrounds (U.S. Department of Agriculture, 2013). Schools with high enrollment of children from lowincome families also qualify for extra resources through Title I, Part A—Improving Basic Programs Operated by Local Educational Agencies (U.S. Department of Education, 2015). As a consequence, enrollment in these programs can be used as an SES indicator for income. Using this type of publicly available data bypasses the need to acquire income information directly from a member of a research participant’s household. Another limitation involved in using income as an SES indicator is its potential to fluctuate due to a family member’s catastrophic health event or broad economic conditions that result in loss of a job (Braveman et al., 2005). It has been suggested that wealth is a better SES indicator, as it is more stable over time (APA Task Force on SES, 2007). Accumulated wealth is inclusive of income, savings, and asset ownership (e.g., a car or a house) and is often transferred across generations. Wealth can determine the influence a family has over service providers in a community and, thus, the likelihood that particular services can be accessed, as well as provide a buffer for short-term

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economic and health-related events (Braveman et al., 2005). However, conceptualizations of wealth differ across cultures. For example, many Native Americans view wealth as the capacity to give back to their communities, as opposed to accumulation of material goods (Cleary & Peacock, 1998). In addition, similar to income, information about a family’s wealth is considered sensitive information and may be difficult to acquire from family members. An additional alternative for determining income is to access data at the neighborhood level. Neighborhoods play an important role in shaping the learning environment for children through social and physical resources that are both positive (e.g., social networks, quality childcare, libraries, parks) and negative (e.g., low levels of trust, drug use, abandoned buildings) (Braveman et al., 2005; Cowan et al., 2012; DeLuca & Rosenbaum, 2014; Putnam, 2015). Neighborhood characteristics, such as median household income, percentage of residents living in poverty, and percentage of unemployed individuals, can be determined by linking geographic areas, such as census block groups, census tracts, or ZIP code tabulation areas, to U.S. Census data (Berkowitz et al., 2015; Cowan et al., 2012). For example, in a study involving children with specific language impairment, Brinton, Spackman, Fujiki, and Ricks (2007) used 2000 census block group data to determine the mean income levels for families living in a particular neighborhood. In a study of narrative discourse abilities of African American children, Horton-Ikard (2009) combined the use of U.S. Census data with school district reports regarding the demographics of neighborhood school zones to determine the SES of a specific community. Another publicly available resource for obtaining community level income data is the American Community Survey (ACS; U.S. Department of Commerce, 2013), for which data are collected on a yearly basis. Because U.S. Census data, school demographic reports, and ACS data are in the public domain, they carry the advantage of being readily available to researchers interested in using neighborhood characteristics as an SES indicator for income. Occupation A third type of SES indicator is occupation (APA Task Force on SES, 2007; Grusky & Weisshaar, 2014). Occupational status is difficult to measure because underlying theoretical and political orientations vary widely, resulting in disagreement on how occupational characteristics should be operationalized and ranked (APA Task Force on SES, 2007; Featherman & Hauser, 2014; Hauser & Warren, 2014). One conceptualization of occupational status indicates that it represents the level of “prestige, skills, social influence, and/or power” (Braveman et al., 2005, p. 2883) associated with particular employment. In general, higher status occupations require more skills and abilities, involve more control over decision making, and are more psychologically demanding, whereas lower status occupations tend to be more hazardous, involve less autonomy, and are characterized by routine tasks (APA Task Force on SES, 2007; Braveman et al., 2005). According to

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Hauser and Warren (2014), occupational status data are often based on categories used by the U.S. Census Bureau. These categories are updated each decade to reflect changes in the U.S. labor market. In a study of grammatical treatments for preschoolers with specific language impairment, Yoder, Molfese, and Gardner (2011) reported use of a formal system for classifying occupation that was developed by Stevens and Cho (1985). Other studies involving a smaller number of participants in studies of communication interventions identified occupations of parent participants using categories, such as childcare providers, retailers, or homemakers (Kashinath, Woods, & Goldstein, 2006) or professionals (Kent-Walsh, Binger, & Buchanan, 2015). The ACS (U.S. Department of Commerce, 2013) also serves as a publicly available resource for gathering community level data regarding occupations and employment status.

Composites and Combinations of Individual SES Indicators Another approach to identifying the SES of research participants has been to use a composite score to account for various demographic dimensions. For example, the Hollingshead Four-Factor Index of Social Status (Hollingshead, 1975) amalgamates the four factors of education, occupation, gender, and marital status into one score. The Hollingshead Index has also been modified to include only the two factors of parent education and occupation (Hollingshead, 1983). In a study of canonical infant babbling, Eilers et al. (1993) adapted the two-factor Hollingshead Index and synthesized it with the work of Nam and Powers (1983) to develop a five-level composite scoring system that combined parental education, source of family income (occupation), and family structural stability. Characteristics of families ranged from Level 1 (i.e., a two-parent home, completion of college by both parents, and employment as a professional or a high-level manager) to Level 5 (i.e., a single parent, highly unstable family structure, less than high school graduation, and employment as an unskilled worker). These demographic factors were selected because they were viewed as reliable and easily obtained from the participant families. More recently, Cowan et al. (2012) explored the possibility of creating a composite index of SES to be used in the National Assessment of Educational Progress (NAEP). They conceptualized an SES composite that would represent the three standard measures (i.e., family income, parental education, and occupational status) and add measures of neighborhood SES (e.g., linking U.S. Census data to ZIP code) and school SES (e.g., school eligibility for Title I funds and/or percentage of student eligibility for free or reducedprice lunches through NSLP). This team concluded that use of a composite score had both advantages and disadvantages. A composite score combined data from all of the individual SES indicators into a single variable, making analysis and reporting of the relationship to academic achievement more straightforward. However, they acknowledged that any differences in the relationships between individual SES indicators and academic achievement would potentially be masked.

Recognizing the multidimensional nature of SES, the APA Task Force on SES (2007) has advocated for using combinations of indicators that are considered and analyzed separately in research studies. They assert that “it is generally more informative to assess the different dimensions of SES and understand how each contributes to an outcome under study rather than merge the measures” (p. 11). For example, Zubrick, Taylor, Rice, and Slegers (2007) describe a study of children’s academic outcomes where there were stronger relationships of the outcomes to caregiver education and employment, as compared to relationships to family structure and income. These authors point out that differences in the strength of relationships would have been missed if a composite score was used. The APA Task Force on SES (2007) emphasizes that SES indicators cannot be used interchangeably. Braveman et al. (2005) point out that correlations between standard measures of education and income are not strong, with the majority being less than 0.50. Furthermore, these authors emphasize that each type of indicator (e.g., education, income, and occupation) measures a different component of SES; thus, they should not be used as proxies for each other. Because each indicator links to an outcome through different mechanisms, the APA Task Force on SES highlights the need to consider multiple SES indicators in relationship to the outcome of interest. In this way, researchers potentially maximize knowledge gained in understanding the complex and multidimensional nature of SES in relation to a phenomenon, such as language development. Interactions Among Factors Although it is beyond the scope of this article to examine interactions among various demographic characteristics, it is important to note that SES does not stand alone. Important interactions exist among such factors as race/ethnicity, gender, and SES. For example, the proportion of African American, American Indian, and Hispanic children who live in low-income families ranges from 62%–65%, whereas 30%–31% of children who are Caucasian or Asian live in low-income families (Jiang, Ekono, & Skinner, 2016). Women still have lower wages than men, “even when they have the same levels of education and occupation. This, in part, accounts for the relatively high rates of poverty among children living in single-parent, mother-headed households” (APA Task Force on SES, 2007, p. 12). Furthermore, Fazio, Naremore, and Connell (1996) explain that lack of money solely does not put a child at risk for language delays or depressed academic achievement. “It is only when lack of money is associated with inadequate nutrition, inadequate medical care, or unstable living conditions that poverty becomes a risk factor” (p. 623).

Summary and Purpose of This Study SES is a complex, multifaceted phenomenon that can influence various aspects of child language development, as well as influence the effectiveness of various language



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assessment and treatment approaches. When viewed through the lens of the resource access approach (APA Task Force on SES, 2007), SES is generally reported using indicators that represent education, income, and/or occupation. These three types of indicators can be used singly, in combination with each other, or consolidated into a composite score. As SLPs make decisions about the generalizability of particular language research findings, they will need to first determine if the SES characteristics of the research participants are reported. If so, they will then need to consider if the educational, income, and/or occupational characteristics of the participants’ families align with those of the children they serve. In addition, to build the professional knowledge base of both universals and variations across SES groupings, it will be necessary for other researchers to have access to specific SES data (Hammer, 2011). In other words, researchers interested in synthesizing findings from across studies will need to be able to determine the types of SES indicators used in a study and if they are used singly, in combinations, or as part of a composite scoring system. It is currently unclear how consistently studies of pediatric language report and describe the SES of participants in the field of speech-language pathology. Therefore, the purpose of this study was to identify patterns used to report SES characteristics of pediatric participants described in research articles published in three ASHA journals, Language, Speech, and Hearing Services in Schools (LSHSS), the American Journal of Speech-Language Pathology (AJSLP), and the Journal of Speech, Language, and Hearing Research (JSLHR, language section). The following research questions were posed: (a) What is the proportion of research articles that report SES of pediatric participants? (b) When SES indicators are used, to what extent do they represent education, income, and occupation in describing pediatric participants in research articles? (c) When SES indicators are used, to what extent do they involve single indicators, a combination of indicators, or composite scoring systems?

Method A team of researchers (one faculty member and three students) reviewed articles published in LSHSS, AJSLP, and JSLHR (language section) from 2000 through 2015 to identify patterns of SES reporting for pediatric research participants. This period was selected due its increased focus on addressing health and education disparities across diverse populations. In addition, language was the focus of this study based on the robust literature base supporting a relationship between language and SES. Specific articles were selected for inclusion in this study using the following criteria: (a) the research focused on language development or identification, assessment, and/or treatment of language disorders, (b) the participants were children ages birth to 18 years, (c) a research method section described participants, (d) original data were examined, and (e) data were collected for participants living in the United States. Studies were excluded if they focused on: (a) stuttering, (b) speech sound

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disorders, and (c) cognitive processes, such as attention and memory, involving nonlinguistic stimuli. Studies were included if all of the participants were between birth to 18 years of age or if the research involved parent–child dyads (e.g., an equal number of parents and children). In addition, selected studies were limited to those conducted in the United States because SES is conceptualized differently depending on the political, economic, and social systems existing in individual countries. Application of selection criteria resulted in inclusion of a total of 652 articles: 154 from LSHSS, 145 from AJSLP, and 353 from the language section of JSLHR. Each selected article was independently reviewed by two researchers (one faculty member and one student) who used a specially designed data sheet to record whether or not SES indicators were reported. If the article reported SES, the specific indicators used were coded using predetermined categories listed on the data sheet. The coding categories were derived from the literature review, as well as preliminary review of 722 articles, as part of a broader study examining the reporting of various demographic characteristics in research articles. The coding categories used were as follows: (a) maternal education level, (b) paternal education level, (c) parent/ primary caregiver education level, (d) family or household income, (e) occupation, (f ) from low, middle, and/or upper class neighborhoods, (g) position relative to federal poverty guidelines, (h) eligibility for Head Start, (i) eligibility for the NSLP, (j) Hollingshead Index, and (k) other. When indicators were identified under the other category, the specific indicator(s) used were noted on the data sheet. An additional step involved consolidation of indicator types under the broad categories of education, income, and occupation and determining if SES indicators were reported singly, in combinations, or through composite scores. To ensure interrater reliability, each article reviewer went through a training session led by the first author that focused on use of the data recording sheets and the coding key. In addition, each article was coded independently by two raters. The independent reviews were then compared. Any differences in the data sheet records were resolved by consensus to achieve 100% point-by-point agreement between the two raters.

Results Proportion of Articles Reporting SES of Participants Frequency of occurrence of SES reporting for research participants ages birth to 18 years in articles examining language development and identification, assessment, and treatment of language disorders was 64.0% (417/652) for articles published from 2000–2015 in LSHSS, AJSLP, and JSLHR (language section) combined. Table 1 presents the extent of SES reporting for each of the three journals individually. To determine if there was any change over time, the data were also analyzed in four-year increments: 2000–2003,



Table 1. Articles reporting socioeconomic status (SES) of pediatric research participants. Total years 2000–2015 Journal

Total # of pediatric language articles

# of articles reporting SES of the participants

% of articles reporting SES of the participants

LSHSS AJSLP JSLHR Total

154 145 353 652

106 99 212 417

68.8 68.3 60.1 64.0

Note. LSHSS = Language, Speech, and Hearing Services in Schools; AJSLP = American Journal of SpeechLanguage Pathology; JSLHR = Journal of Speech, Language, and Hearing Research (language section).

2004–2007, 2008–2011, and 2012–2015. Figure 1 summarizes changes in the number and percentage of research articles that reported SES of pediatric participants. Reporting of SES for all three journals combined showed a 14.4% increase from Time Period 1 to Time Period 2 (55.6% to 63.6%), a 3.1% decrease from Time Period 2 to Time Period 3 (63.6% to 61.6%), and a 19.0% increase from Time Period 3 to Time Period 4 (61.6% to 73.3%). From Time Period 1 to Time Period 4, there was an overall increase of 31.8% (55.6% to 73.3%) in SES reporting for pediatric participants in the three journals combined. Figure 1 also presents changes in reporting of SES indicators for pediatric research participants for the three individual journals over the four-year increments. All three journals displayed increases in reporting of SES from Time Period 1 to Time Period 4. LSHSS made the greatest gain Figure 1. The percentage of pediatric language–related journal articles that reported socioeconomic status (SES) indicators for each 4-year period from 2000–2015. LSHSS = Language, Speech, and Hearing Services in Schools; AJSLP = American Journal of Speech-Language Pathology; JSLHR = Journal of Speech, Language, and Hearing Research (language section).

of 63.6% (50.0% to 81.8%); AJSLP made the next greatest gain of 55.5% (55.1% to 85.7%); and JSLHR made a gain of 11.0% (57.5% to 63.8%) in SES reporting.

Indicators Used for SES A total of 417 articles reported the SES of the pediatric research participants. These were analyzed to determine the extent to which SES indicators represented the three overarching categories of education, income, and occupation. Overall, indicators of education level were used 69.3% (289/417) of the time. This was inclusive of maternal, paternal, and parent/primary caregiver education levels used singly, in combination with other indicators, and in composite scores. Indicators of income level were used 39.8% (166/417) of the time. This was inclusive of household/family income levels, position relative to federal poverty guidelines, eligibility for the NSLP and Head Start, neighborhood income levels, shelter/homelessness status, and health insurance type used singly or in combination with other indicators. Indicators signifying occupation were used 18.5% (77/417) of the time. This was inclusive of occupation reported singly, in combination with other indicators, and in composite scores. Single indicators were used to describe the SES background of pediatric participants 67.1% (280/417) of the time for all three journals combined. Of the SES indicators used singly, education was represented most often at 56.8% (159/280), followed by income at 36.1% (101/280) of the time. Occupation was used the least often at 1.8% (5/280). A notation should be made regarding use of the indicator from low, middle, or upper class neighborhoods. When this indicator was used, only 12 out 27 studies stated that the designation was associated with income. The other 15 studies (5.3% of the total using single indicators) only used the terminology low, middle, or upper class neighborhoods or low, middle, or upper SES neighborhoods. Therefore, the overarching categories for these 15 studies could not be identified. Table 2 presents the frequency of occurrence of specific indicators used singly to denote SES in pediatric research articles for the three journals. Various combinations of two, three, or four SES indicators representing education, income, and occupation were used 21.6% (90/417) of the time. Composite scores were used to represent the SES background of pediatric research



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Table 2. Frequency of use: Single socioeconomic status (SES) indicators in pediatric research articles. SES indicator Maternal education level Parent/primary caregiver education level Eligible for National School Lunch Program From low, middle, or upper class neighborhoods Eligible for Head Start Family or household income Occupation Position relative to federal poverty guidelines Paternal education level Total

LSHSS

AJSLP

JSLHR

Total

16 7 29 10 6 6 1 1 0 76

20 11 8 9 10 4 1 2 1 66

73 30 10 8 4 8 3 1 1 138

109 48 47 27 20 18 5 4 2 280

Note. LSHSS = Language, Speech, and Hearing Services in Schools; AJSLP = American Journal of Speech-Language Pathology; JSLHR = Journal of Speech, Language, and Hearing Research (language section).

participants 11.3% (47/417) of the time. Two composite scoring systems were used. The Hollingshead Four-Factor Index of Social Status (Hollingshead, 1975) or an adaptation of this composite scoring system was used 9.8% (41/417) of the time, and a composite scoring system developed by Eilers et al. (1993) was used 1.4% (6/417) of the time. Table 3 presents the frequency of use of composite scoring systems, as well as specific combinations of multiple SES indicators.

Additional Observations Although the specific measures used to represent educational, income, and occupational SES indicators were not formally examined in this study, it was observed that

there was wide variation in the way each was operationalized. For example, it was noted that in some studies education was represented on a continuum (i.e., number of years of education). Other studies used various categories framed around educational junctures, such as high school graduation or degrees attained, that ranged in number from three to eight. Furthermore, the terms low, middle, and high income were sometimes used without an explanation of how these terms were defined. Income categories that were used varied across studies. The greatest consistency in identifying specific income levels appeared to be associated with guidelines for child enrollment in government-sponsored programs for low income families. Similar to education and income, occupational categories used varied across studies.

Table 3. Frequency of use: Composite scores and combinations of socioeconomic status (SES) indicators in pediatric research articles. SES indicators Composite score: Hollingshead Index (education, occupation, gender, and marital status) Maternal education and household/family income Parent/primary caregiver education and household/family income Parent/primary caregiver education and occupation Maternal education and National School Lunch Program (NSLP) eligibility NSLP eligibility and Hollingshead Index Maternal education and Hollingshead Index Maternal education and Head Start eligibility Parent/primary caregiver education and NSLP eligibility Composite score: parent education, occupation, and family structural stability (Eilers et al., 1993) Maternal education and paternal education Maternal education and occupation Maternal education and shelter/homelessness Maternal education and health insurance type Parent/primary caregiver education and neighborhood Parent/primary caregiver education and Hollingshead Index Neighborhood and NSLP eligibility Specific combinations of 2, 3, or 4 indicators that occurred only once* Total

LSHSS

AJSLP

JSLHR

Total

10

6

25

41

1 3 1 2 2 0 2 2 0

6 3 3 2 1 1 1 0 2

11 5 6 4 2 3 1 2 4

18 11 10 8 5 4 4 4 6

1 1 0 0 1 0 0 3 29

1 0 2 0 1 0 1 3 33

1 1 0 2 0 2 1 5 75

3 2 2 2 2 2 2 11 137

Note. LSHSS = Language, Speech, and Hearing Services in Schools; AJSLP = American Journal of Speech-Language Pathology; JSLHR = Journal of Speech, Language, and Hearing Research (language section). *Combinations of two, three, or four of the following: maternal education, paternal education, parent/primary caregiver education, income, NSLP eligibility, Head Start eligibility, Title I school participation, lower, middle, or upper class neighborhood, mother’s employment status, years lived in an area, position relative to poverty guidelines.

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Discussion Research evidence suggests that consideration of SES is important to language development and outcomes in pediatric language service delivery. (See for example, Dollaghan et al., 1999; Horton-Ikard & Weismer, 2007; McDowell et al., 2007; Qi et al., 2006). However, as indicated by Hoff et al. (2012), the mechanisms through which SES shapes child language development are not adequately understood. The current study represents the first examination of SES reporting patterns in pediatric language articles in ASHA journals. From 2000–2015, overall reporting of SES for pediatric participants in language research articles published in LSHSS, AJSLP, and JSLHR (language section) occurred 64.0% of the time. An increase in SES reporting over time was noted beginning with a rate of 55.6% for the period 2000–2003 and ending with a rate of 73.3% for the time period 2012–2015. This represents an increase of 31.8%. Although this increase is commendable, over one fourth of language research articles in these three journals combined still do not provide information about the SES background of pediatric participants. As a tenet of evidencebased practice, ASHA (2005) guides SLPs to consider research evidence in the clinical decision-making process. When determining the generalizability of research findings to children from specific SES backgrounds, it is important for SLPs to be able to identify the SES background of research participants. As emphasized by Hammer (2011), inclusion of information about demographic characteristics, such as SES, is also important to other researchers interested in replicating or synthesizing findings across multiple studies. Examination of SES reporting for pediatric participants in language research published in ASHA journals revealed that educational indicators were used most often at 69.3% occurrence, followed by income indicators at 39.7%, and occupational indicators at 18.5%. This means that when SES is reported, researchers will have access to data pertaining to education about two thirds of the time, but access to income and occupational data will be much more limited. Increased reporting of SES indicators can potentially lead to greater fidelity in the replication of studies. It is also important for researchers to consider the manner in which SES indicators are operationalized, as inconsistency across studies will serve as a limitation in how associated findings can be synthesized. Of further importance, the APA Task Force on SES (2007) points out that greater understanding of SES as a complex and multidimensional phenomenon will be gained through the use of multiple indicators that are considered and analyzed separately. Various SES indicators have been associated with different forms of capital, with education representing human capital (knowledge and skills), income representing financial capital (money), and occupation representing social capital (social networks) (Coleman, 1988; Hoff et al., 2012). Braveman et al. (2005) point out that the three major indicator types are not interchangeable and that they reflect different aspects of SES. In the current

study, SES indicators were used singly in the majority of research articles that reported SES for pediatric participants (67.1%). Combinations of two, three, or four indicators were used 21.6% of the time, and composite scores were used 11.3% of the time. Thus, it can be seen that only a limited proportion of the studies reporting SES used multiple indicators. Dependence on one indicator type in describing research participants may lead to limitations in professional understanding of the full ramifications of SES in relation to child language development. It should be recognized that researchers who seek to account for multiple SES indicators in child language studies have likely encountered barriers. One potential barrier would be reluctance on the part of family members to share SES information. For example, collection of data regarding household income can be a sensitive topic about which families choose to maintain privacy (Berkowitz et al., 2015; Braveman et al., 2005). Serving as another barrier, researchers may have limited awareness of the various resources that can be used to obtain demographic information. A range of SES resources available at the community, neighborhood, or school levels have been identified through the process of conducting the current study. For example, publicly available information regarding the characteristics of a particular community can be accessed through the ACS database (U.S. Department of Commerce, 2013). This database is updated yearly and provides information regarding a wide range of community attributes, including family income, occupation, employment status, health insurance, and educational attainment. Neighborhood level data, pertaining to median household income, poverty status, and employment status, can also be accessed by linking geographic areas, such as census blocks or ZIP code tabulation areas, to U.S. Census data (Berkowitz et al., 2015; Cowan et al., 2012). Household income information can be obtained indirectly, based on child enrollment in governmentsponsored programs that are limited to low-income families, such as Head Start (U.S. Department of Health and Human Services, 2016), the NSLP (U.S. Department of Agriculture, 2013), or Title I public school programs (U.S. Department of Education, 2015). This research serves as an initial step in documenting the reporting of SES in pediatric research articles published by ASHA. As such, it has several limitations. SES can serve as a primary variable of interest or as a background descriptive variable in research (APA Task Force on SES, 2007; Cowan et al., 2012). This study did not distinguish the SES variable types used in the research articles examined. In addition, the way in which SES measures were operationalized was not systematically determined. It remains unclear how researchers make decisions about which SES indicators to use in studying various aspects of language. Further examination of SES reporting is warranted as one means to enhance understanding of the relationship between various SES indicators and child language. The current study aligns with the resource access approach for conceptualizing SES (APA Task Force on SES, 2007). Future research might extend beyond a resource



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access approach to foster increased understanding of disparities between groups using a continuum approach. Based on findings from the current study, it is unclear how the categories of low, middle, and high income households or, more broadly, how low, middle, and high SES categories have been delineated in child language studies. Further research could be conducted to better understand where these groups fall along a continuum. To facilitate more indepth understanding of disparities that exist along a continuum, self-perceptions of status and inequality could also be examined (APA Task Force on SES, 2007). Cowan et al. (2012) identified qualitative methods for determining selfperceptions that might be used by researchers. One method is the SES ladder technique that involves asking family members to indicate where they would stand on a picture of a ladder relative to SES (Demakakos, Nazroo, Breeze, & Marmot, 2008). An additional method that has been used in Gallup polls asks a family member if they have “enough money to get along” (Cowan et al., 2012, p. 17). Examination of the intersections of SES with other demographic characteristics, such as race/ethnicity or gender, can further add to our professional knowledge of disparities, reflected through multiple variables that exist along a continuum. In summary, evidence-based practice, as advocated by ASHA (2005), guides professionals to consider the best available research along with the background of each client served. Consequently, there is a need for SLPs to have access to information regarding the demographic characteristics of pediatric research participants, including their SES background, to determine the generalizability of particular research findings for specific children (ASHA, 2004; Hammer, 2011). In addition, a thorough description of participant characteristics can facilitate replication and synthesis of research findings across child language studies, as well as lead to identification of existing gaps in the professional literature (Hammer, 2011). This study provides a starting point for understanding the current status of SES reporting for pediatric participants in language studies published in ASHA journals. As child language researchers move forward with more consistent and comprehensive inclusion of SES indicators in their research articles, there is potential for gaining greater insights into the relationship between SES and language development.

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