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. Author manuscript; available in PMC: 2023 Jun 8.
Published in final edited form as: J Aging Health. 2021 Aug 11;34(2):147–157. doi: 10.1177/08982643211036131

The Everyday Ageism Scale: Development and Evaluation

Julie Ober Allen 1,2, Erica Solway 3, Matthias Kirch 3, Dianne Singer 3,4, Jeffrey T Kullgren 3,5,6, Preeti N Malani 3,6
PMCID: PMC10249361  NIHMSID: NIHMS1901317  PMID: 34376066

Abstract

Objectives:

Older adults regularly encounter age-based discrimination and stereotyping in their day-to-day lives. Whether this type of routine ageism negatively affects their health and wellbeing is unclear, in part due to the absence of validated scales that comprehensively measure this phenomenon and distinguish it from other sources of everyday discrimination.

Methods:

This study describes the development of a novel scale, the Everyday Ageism Scale, and its psychometric evaluation using a nationally representative sample of U.S. adults age 50-80 from the December 2019 National Poll on Healthy Aging (N=2,012).

Results:

Exploratory factor analysis indicated a 3-factor structure comprised of ageist messages, ageism in interpersonal interactions, and internalized ageism. The ten-item scale was psychometrically sound and demonstrated good internal reliability.

Discussion:

Everyday ageism is a multidimensional construct. Preliminary evaluation of the Everyday Ageism Scale suggests its utility in future studies examining the prevalence of everyday ageism and its relationships with health.

Keywords: Ageism, discrimination, scale development, psychometrics

Ageism has been described as the most common, socially acceptable, and institutionalized form of discrimination in United States (Allen, 2016; Angus & Reeve, 2006; Hagestad & Uhlenberg, 2005). Ageism refers to discrimination, prejudice, and stereotyping of older adults based on age. Older adults are discriminated against in employment, health care, housing, and in routine interactions with friends, family, service providers, and strangers. They are subjected to ageism in the media and in their own assumptions about aging processes, old age, and older adults. We know that major incidents of age-based discrimination (e.g., age-related organ transplant denials or being forced out of the workforce) can negatively affect the lives of older adults (Abecassis et al., 2012; Bender, 2012; Kydd & Fleming, 2015). Less is known about the consequences of more minor, but still potentially harmful, forms of ageism that older adults routinely encounter in their day-to-day lives. We define everyday ageism as brief verbal, nonverbal, and environmental indignities that convey hostility, a lack of value, or narrow stereotypes of older adults. These types of everyday ageism may also negatively affect the health and wellbeing of older adults.

Limitations within Ageism Research

While experiences of ageism are reported by more than 80% of older U.S. adults (Allen, Solway et al., 2020; Palmore, 2004), ageism and its ramifications for health are understudied relative to other forms of discrimination (Allen, 2016). A recent systematic review found evidence to support negative relationships between ageism and a range of health outcomes in observational studies (Hu et al., 2020). More research is needed, however, to confirm and better understand how ageism can affect health. This would inform recommendations for more targeted approaches to health promotion among older adults in clinical practice, community health inventions, and policy change. Two major issues have been identified in recent systematic reviews on ageism research that may account, in part, to the modest number and quality of population-level observations studies on ageism (Ayalon et al., 2019; Hu et al., 2020). First, there is a lack of established ageism measures that have undergone systematic development and psychometric testing. These reviews found that most existing measures used to investigate ageism have been used in few studies, were not psychometrically validated, and were of questionable quality (e.g., lacked justification, were not developed using systematic methods, single-item measures) (Ayalon et al., 2019; Hu et al., 2020; Warmoth et al., 2016). Second, most existing scales used in ageism research were designed to capture one or two key dimensions of ageism. Ageism, however, is a multidimensional construct that encompasses the numerous manifestations of ageism that older adults may experience. The Risk of Ageism Model (Swift et al., 2017) proposes three primary mechanisms of ageism: stereotype embodiment (internalization of self-relevant ageist stereotypes and attitudes), being a target of ageism (discrimination), and stereotype threat (older adults’ concern about and responses to ageist stereotypes). Iverson and colleagues’ (Iverson et al., 2009) framework emphasizes stereotypes, prejudice, and discrimination, and notes the importance of recognizing that ageism can be positive or negative, explicit or implicit, and occur at multiple levels: within individuals, in interpersonal interactions, and within institutions/society. Existing scales used in ageism research capture people’s own aging-related stereotypes, attitudes/ prejudices about aging and older adults, and, less commonly, endorsement of/engagement in discrimination against older adults. These scales all measure ageism at the individual level. Notably absent are scales assessing the ways in which older adults are affected by the ageist stereotypes, prejudices, and discriminatory acts of other people and in society, more generally. Accordingly, more comprehensive ageism measures are needed to investigate the collective and potentially synergistic effects of multiple dimensions of ageism on older adult health and other outcomes.

One commonplace, but understudied, form of ageism is exposure to routine ageism in older adults’ day-to-day lives. These incidents, also called microaggressions, can be intentional or unintentional, readily identified as discriminatory and offensive, or more subtle and ambiguous. A characteristic of everyday ageism, relative to other contemporary responses to other forms of everyday discrimination, is that ageism is especially likely to be overlooked or discounted because it is perceived as humorous (e.g., aging jokes in birthday cards), considerate (e.g., talking loudly in case of hearing loss), or complimentary (e.g., presuming the devoted grandmother stereotype). Yet, everyday ageism serves to strip older adults of their individuality and instead identify them as belonging to as a group that is decidedly different from mainstream society and, by extension, not guaranteed the same rights, privileges, and respect. If findings from parallel research on the adverse health outcomes attributed to everyday experiences of racism, sexism, and homophobia (Allen et al., 2019; Allen, Watkins, Chatters et al., 2020; Allen, Watkins, Mezuk et al., 2020; Geronimus et al., 2006; James, 2009; Juster et al., 2010; Meyer & Frost, 2013) can be extrapolated to ageism, these socially-condoned, commonplace examples of ageism may be more harmful to the health and wellbeing of older adults than we realize (Allen, 2016). These bodies of research suggest that everyday forms of discrimination can function as sources of chronic stress. As such, they can lead to dysregulation of multiple biological systems involved in the stress response over time. This, in turn, is believed to cause accelerated aging, increased risk for developing a range of chronic diseases, and premature mortality (Adam et al., 2017; Allen, 2016).

Existing research related to instances of everyday ageism and their relationships with health is largely experimental in nature and conducted in controlled laboratory settings (Lamont et al., 2015; Meisner, 2012). In a series of studies, Levy and colleagues found that triggering negative aging-related stereotypes among older adults was associated with poor cognitive and physical health outcomes in memory, small motor control, balance, walking speed, cardiovascular stressor response, and will to live (for a review, see Levy, 2003; Levy & Leifheit-Limson, 2009). Hehman and Bugental (2015) found that older adults demonstrated worse cognitive performance and an exaggerated physiological stressor response (cortisol) after receiving instructions delivered using patronizing speech patterns associated with stereotypic assumptions about older adults (i.e., speaking slowly, loudly, using a high pitch, and with exaggerated intonation). While this body of research has generated important insight on the consequences of ageism and theoretical and mechanistic models linking ageism to health and other outcomes, it leaves many questions unanswered. Population-level survey research is needed to ascertain the prevalence of everyday ageism in broader society and whether its frequency and forms vary for different demographic groups. Survey research is also needed to evaluate the generalizability, strength, and variability of associations between everyday ageism and various health and other outcomes, while enhancing the evidence on mechanisms and mediating pathways through which ageism influences health.

The Everyday Discrimination Scale (Williams et al., 1997) is the only existing survey measure used to better understand everyday ageism, though its origination and applications are closely tied to research on everyday forms of racism (Paradies, 2006). This scale asks people about several broadly applicable forms of routine discrimination (e.g., In your day-to-day life, how often have “you been treated with less respect than other people” or “people acted as if they think you are not smart”). It then asks respondents to identify which of their demographic characteristics they attribute their experiences of everyday discrimination to (e.g., race, nationality, sex, age, sexual orientation, appearance). A major limitation of this measure is that it does not clearly distinguish between everyday ageism and other forms of routine discrimination (e.g., racism, sexism, ageism, classism). This lack of precision may be especially problematic among members of other socially marginalized groups, who may be more likely to attribute everyday discrimination to lifelong sources of disadvantage (e.g., minority race, female sex) than a more recent source, such as increasing age. Finally, the Everyday Discrimination Scale does not include common, age-specific forms of everyday discrimination. A new scale is needed that captures everyday ageism distinct from other forms of routine discrimination, is multidimensional, and has undergone rigorous psychometric evaluation. This study fills this gap.

Study Purpose

This study developed and evaluated a new scale measuring routine instances of everyday ageism, distinct from other forms of everyday discrimination, in collaboration with the University of Michigan National Poll on Healthy Aging. Our study involved two complementary phases. The aim of Phase 1 was to use a systematic process to develop items and a preliminary multidimensional scale of everyday ageism. In Phase 2, we refined the scale and evaluated its psychometric properties in a nationally representative sample of U.S. adults ages 50-80.

Methods

The University of Michigan National Poll on Healthy Aging (NPHA) is a recurring, nationally representative household survey of adults age 50 to 80 that taps into the insights, experiences, and perspectives of older adults on a variety of health topics. Polls are conducted two to three times annually, and each includes four to five modules exploring selected health topics in greater depth. Poll findings are disseminated widely and rapidly to inform national dialogues about timely issues, policy, and research-informed clinical care relevant to the lives of older adults. Poll reports, infographics, press releases, and blog posts, including those related to the current study, are available on the NPHA website (www.healthyagingpoll.org). The poll is directed by the University of Michigan Institute for Healthcare Policy and Innovation and sponsored by AARP and Michigan Medicine. This study was IRB exempt due to the anonymous nature of data collection; participant informed consent was required. The current study reports on the development and psychometric testing of a module exploring experiences of Everyday Ageism created for the December 2019 fielding of the poll.

Phrase 1. Preliminary Scale Development

In collaborative discussions between the National Poll on Healthy Aging Team and the first author, everyday ageism was selected as the topic for in-depth investigation as part of the December 2019 fielding of the National Poll on Healthy Aging. Since we were unable to identify any previously validated scales that comprehensively assessed this phenomenon, a novel scale was developed. The underlying goals were to develop a series of poll questions and, ultimately, a scale that: 1) was reflective of the multidimensional model of ageism proposed in the literature (Iverson et al., 2009; Swift et al., 2017); 2) captured a construct and phenomenon parallel to everyday discrimination (Williams et al., 1997) but with a specific focus on everyday ageism distinct from other sources of everyday discrimination; and 3) met the requirements for the National Poll on Healthy Aging. These requirements were that the everyday ageism module take no more than an average of 2 minutes to complete, use accessible language suitable for a diverse sample of older U.S. adults, and be developed in a compressed timeline from concept and scale development to large-scale data collection and widespread dissemination of preliminary findings.

We used a systematic process informed by the work of Krause (2002) and Wade and Harper (2020) for creating the new scale. We began with the first two authors reviewing the literature on ageism and relationships between ageism and health. This process was used to generate an extensive list of topics and potential closed-ended survey items related to everyday ageism since logistical constraints prevented us from conducting exploratory qualitative research with older adults. Some items derived from existing scales and instruments. Most, however, were developed by the authors due to the dearth of items in the literature with the precision to capture this specific construct. New items were developed with several considerations in mind. First, we focused on creating and revising items to be easily and consistently interpreted and relevant to the lives of contemporary older adults. Next, we selected commonplace, easily identifiable examples of everyday ageism that older adults would be able to accurately report on. Finally, items were designed to ask participants to report on beliefs or concrete behaviors or experiences without necessitating that they attribute them to ageism.

A panel of experts was enlisted to assist in selecting items for inclusion in the scale. The panel included researchers with expertise in relevant content (ageism, discrimination, and their relationships to health), developing novel items and scales, and survey research with older adult populations. The panel comprised the authors, with additional input from representatives of AARP and the Gerontological Society of America’s Reframing Aging initiative (www.reframingaging.org). Panel members assessed items over multiple (approximately five) iterations, including both independent reviews and during organized group meetings. Proposed items were reviewed, revised, and evaluated for face and content validity. Additional items were brainstormed to address identified gaps. We were limited in the number of items we could include in the Everyday Ageism module and so elected to prioritize capturing the breadth of identified dimensions of everyday ageism (i.e., content validity), with the goal of 3-4 items for each subscale.

In phase 1, our expert panel reached consensus in selected 17 items that naturally clustered into four related but conceptually distinct dimensions: exposure to ageist messages, ageism in interpersonal interactions, internalized ageism, and perspectives inconsistent with ageism (later renamed “positive views on aging”). The items and their associated dimensions tapped into the diverse aspects of ageism proposed in the literature (Iverson et al., 2009; Swift et al., 2017) though they did not neatly map onto the individual components of ageism described in these models. Exposure to ageist messages captured older adults’ exposure to environmental and societal cues reaffirming and reinforcing prejudices related to aging and older adults. These cues may generate stereotype embodiment and stereotype threat among older adults. Ageism in interpersonal interactions captured interpersonal discrimination resulting from assumptions about older adults rooted in ageist stereotypes. Internalized ageism captured older adults’ individually held ageist beliefs and stereotypes related to health, which is a key component of stereotype embodiment theory (Levy, 2009). These three dimensions emphasized negative aspects of ageism, both explicit and implicit, and spanning multiple forms, mechanisms, and levels. Perspectives inconsistent with ageism sought to capture older individuals’ more positive perceptions and experiences of aging, specifically ones that were believed to be contrary to negative age-related stereotypes, prejudices, and discrimination.

The first variation of the Everyday Ageism scale was pilot tested in October, 2019. It was administered as an online quantitative survey and accompanied by other module topics, using a format that approximated how the National Poll on Healthy Aging was collected. The goal of this pilot study was to assess the time required to complete the scale, examine basic descriptive statistics and bivariate correlations between items, and identify items with insufficient variance and unusual response distributions and patterns warranting evaluation for revision or deletion. We used the Ipsos KnowledgePanel® sampling methods and data collection procedures, which are described in detail below. Purposeful sampling was used to ensure that the sample was equally divided between the 50-64 and 65-80 age groups.

Phase 2. Scale Refinement and Evaluation

Sample and Data Collection

A nationally representative sample of adults age 50 to 80 was selected to complete the National Poll on Healthy Aging including the Everyday Ageism module in December 2019. This sample did not overlap the pilot test sample. The target sample size for the poll is approximately 2,000 participants. The sample was selected using the Ipsos KnowledgePanel®, which is the largest nationally representative, probability-based online panel in the U.S. Details of the Ipsos sampling and polling methods are provided elsewhere (www.ipsos.com/en-us/solutions/public-affairs/knowledgepanel). Briefly, the KnowledgePanel® is recruited via address-based sampling methods for community-dwelling U.S. adults. The poll sample is stratified by age group such that it is roughly equally divided between ages 50-64 and 65-80. Poststratification survey weights are calculated and applied to reflect the population figures from the U.S. Census Bureau and differential participation rates; weights factor in age, sex, race/ethnicity, education, household income, home ownership, geographic region, metropolitan/non-metropolitan residence, and differential nonresponse. The National Poll on Healthy Aging is completed online, and Ipsos provides participants with web-enabled devices and access to the Internet, as needed.

Measures

Everyday Ageism Scale.

For the second phase of the study, the everyday ageism scale was reconceptualized to have three hypothesized underlying subscale factors: exposure to ageist messages, ageism in interpersonal interactions, and internalized ageism. The final scale consisted of ten items (see Table 2). The first seven scale items (1-7) assessed how often participants experienced seven examples of everyday ageism. The stem was “In your day-to-day life, how often do the following things happen to you?” with the response options of often (3), sometimes (2), rarely (1), and never (0). Scale items 8-10 assessed ageist beliefs. The stem for these items was “How much do you agree with the following statements?” with a Likert response scale coded strongly agree (3), agree (2), disagree (1), and strongly disagree (0). Since some researchers prefer the utility of a single scale that holistically represents a complex construct, made by combining multiple subscale dimensions, while others prefer to examine sets of related subscales dimensions (Edwards, 2001), both options are provided. Subscale scores and the overall scale score were created by summing responses to the relevant items. The overall scale score was calculated such that it reflected the inherent differences in the number of items in each subscale, thereby weighting ageism in interpersonal interactions (5 items), more heavily than the other two subscales. This emphasis on being targeted in discriminatory acts is consistent with the focus of Everyday Discrimination Scale items (Williams et al., 1997) and represents a dimension of ageism not captured in other scales used in ageism research.

Table 2.

Everyday ageism exploratory factor analysis pattern matrix

Item Factor M SD
(1) (2) (3)
1. I hear, see, and/or read jokes about old age, aging, or older people. .539 1.65 .857
2. I hear, see, and/or read things suggesting that older adults and aging are unattractive. 1.025 1.21 .859
3. People insist on helping me with things I can do on my own. .464 .71 .755
4. People assume I have difficulty hearing and/or seeing things. .753 .64 .829
5. People assume I have difficulty remembering and/or understanding things. .836 .67 .804
6. People assume that I have difficulty with cell phones and computers. .608 .72 .861
7. People assume I do not do anything important or valuable. .506 .60 .821
8. Feeling depressed, sad, or worried is part of getting older. .829 1.06 .749
9. Feeling lonely is part of getting older. .888 1.10 .753
10. Having health problems is part of getting older. .443 1.85 .681
Eigenvalue 3.290 1.691 1.285
% Variance 32.90 16.91 12.85
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Participant Characteristics were self-reported in a series of single item measures: age group (50-64 or 65-80 years old), sex (male or female), race/ethnicity (Non-Hispanic Black, Non-Hispanic White, Latino, or other), educational attainment (high school diploma or less, some college, bachelor’s degree or higher), annual household income (less than $30,000; $30,000-$59,999; and $60,000 or more), employment status (working full/part time or retired/not working because unable or unemployed), and relative appearance (self-appraisal of looking younger, older, or the same age compared to other people their age).

Data Analysis

All presented analyses used SPSS 27 (IBM Corp., Armonk, NY). Poststratification survey weights provided by Ipsos were used as appropriate. We conducted exploratory descriptive analyses on the everyday ageism items to examine item distributions and correlations. We assessed whether the study sample was adequate for factor analysis using Bartlett’s test of sphericity and the Kaiser-Meyer-Oklin measure and standard threshold levels (p <.05 and KMO>.5, respectively; Williams et al., 2010). We then used exploratory factor analysis (EFA) to examine whether the ten Everyday Ageism items clustered in the three hypothesized underlying factors: exposure to ageist messages, ageism in interpersonal interactions, and internalized ageism. We used Maximum Likelihood extraction with oblimin rotation and Kaiser Normalization. We elected to use oblimin rotation due to theoretical expectation that each proposed subscale would be correlated with other Everyday Ageism subscales and with the overall scale (Gaskin & Happell, 2014). We retained subscales based on commonly recommended thresholds (Spector, 1992): achieving a minimum factor loading of .35, and an Eigen value greater than 1.00. We assigned items loading on more than one factor to the factor they loaded highest on.

We assessed internal consistency reliability by calculating Cronbach’s alphas for each subscale and the overall Everyday Ageism scale. The assessed convergent construct validity using a correlation matrix composed of each subscale and the overall scale. We also assessed two forms of known-group validity (Spector, 1992) to ascertain whether the scale could differentiate between two groups presumed to differ on the amount of everyday ageism they report. We focused on two grouping characteristics that were theoretical antecedents or determinants of ageism: chronological age and how old people appear. We tested whether the older age group (65-80) reported more everyday ageism than the younger age group (50-64). We also tested whether participants who reported looking older than their age reported more everyday ageism than those looking younger than their age, after accounting for chronological age; participants who indicated that they looked the same as others their age (n=703) and with missing data (n=6) were excluded from this analysis. We tested these assumptions using one-tailed t-tests and general linear modeling (GLM) univariate with estimated marginal means, respectively. We also conducted preliminary analyses assessing whether the scale functioned similarly for the younger and older age groups within the sample. Separate EFAs were calculated for the younger and older age groups. Pearson’s r was used to compare the patterns and relative magnitude of all the factor loadings, including those below the minimum threshold adopted for the current study, for two groups.

We conducted sensitivity analyses to determine if the findings differed using alternate methods. First, we sought to confirm the factor structure using Horn’s parallel analysis for principal component factors using polychoric correlations, mean eigenvalue estimates, and scree plots, which is a method often recommended for use with ordinal variables (Courtney & Gordon, 2013; Yang & Xia, 2015); these analyses were conducted in Stata 16 (College Station, TX). We also repeated all analyses using an alternate Everyday Ageism Score created by summing item response Z-scores, which are sometimes used to account for differing responses options.

Results

Phase 1 Preliminary Scale Development

A total of 100 older adults participated in the pilot testing of the preliminary Everyday Ageism items. Phase 1 pilot study participant characteristics are reported in Table 1. The module took an average of less than two minutes to complete. Based on analyses of pilot test results, we revised one item due to concerns about face validity regarding phrasing thought to prime participants with ageist stereotypes: “I invest time or effort to avoid looking older than I am” was changed to “I invest time or effort to look younger than my age.” We deleted one item with minimal response variability (“There are things you can do to improve your health regardless of your age”) and added one item (“Having health problems is part of getting older”). We also determined that five items designed to capture perspectives that were inconsistent with ageism captured a distinct but related construct, on positive views on aging, rather than a dimension of everyday ageism. This conclusion was based on observed inconsistencies in the ways pilot survey participants responded to these items when compared to the other items that tapped into negative examples of ageism. For example, all the positive views on aging items had comparably limited variance and mean scores (reverse-coded) that diverged from the mean scores of the negative items. In addition, perspectives that were inconsistent with ageism (i.e., positive items) were anticipated to be negatively correlated with the negative ageism item scores, yet this pattern was not consistently documented. We elected to keep these items for use as a separate scale; information on these items is reported elsewhere (Allen, Solway, et al., 2020). In summary, 12 everyday ageism items were retained for the phase 2 scale refinement and evaluation.

Table 1.

Participant characteristics

Phase 1 pilot (N=100) Phase 2, weighted (N=2,048)
Age group
 50-64 50.0% 60.4%
 65-80 50.0% 39.6%
Female 47.0% 52.4%
Race/ethnicity
 Non-Hispanic White 78.0% 71.1%
 Non-Hispanic Black 4.0% 10.8%
 Latino 10.0% 11.5%
 Other 1.0% 6.6%
Educational attainment
 High school diploma or less 41.0% 40.0%
 Some college 28.0% 26.7%
 Bachelor’s degree or higher 31.0% 33.4%
Annual household income
 Less than $30,000 13.0% 18.7%
 $30,000 - $59,999 21.0% 23.2%
 $60,000 or more 66.0% 58.1%
Employment status
 Working 43.0% 46.7%
 Retired/not working 57.0% 53.3%
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Phase 2 Scale Refinement and Evaluation

A total of 2,048 older adults completed the December 2019 National Poll on Healthy Aging with the Everyday Ageism module. The response rate was 77%. The sociodemographic characteristics of the weighted participant sample (Table 1, Phase 2 column) approximated those for this age group nationwide except for average annual household income, which was slightly higher among poll participants than found among adults ages 50 to 80 nationally (Anderson, 2014). For phase 2 scale evaluation, the analytic sample was limited to the 2,012 participants with complete data for the everyday ageism items.

While twelve items were included in the large-scale fielding of the Everyday Ageism module, two (effort to alter appearance, health behaviors and aging) were promptly eliminated for inclusion in the scale due to ongoing validity concerns identified in Phase 1 descriptive analyses and confirmed in Phase 2. We determined that the study sample was adequate for factor analysis, as indicated by the Bartlett’s test of sphericity (χ2=5706.22(45), p<.001) and KMO=.749. The EFA of the 10 remaining everyday ageism items produced a three-factor structure shown in Table 2, with rotations converging in 21 iterations. Three factors with eigen values greater than 1.00 emerged, collectively explaining 62.66% of the variance. Items loaded as anticipated on the three hypothesized factors of exposure to ageist messages (2 items), ageism in interpersonal interactions (5 items), and internalized ageism (3 items). None of the items cross-loaded on more than one factor, using the threshold of >.35.

Table 3 provides descriptive and internal consistency reliability statistics for each individual subscale and the overall Everyday Ageism Scale. Readability statistics were at 8th grade or below, based on the Flesch-Kincaid Grade Level. For the subscales, older adults had the highest average scores for internalized ageism (4.01, SD 1.79), followed by ageism in interpersonal interactions (3.35, SD 2.96) and exposure to ageist messages (2.86, SD 1.52). The Cronbach’s alphas for the subscales were all above .70. For the overall scale, older adults averaged 10.22 (SD 4.55), with possible scores ranging from 0 to 27. Despite its multidimensional nature, the Cronbach’s alpha for the overall scale was .768, indicating that it demonstrated sufficient internal consistency for use as a single scale.

Table 3.

Descriptive and reliability statistics for the Everyday Ageism Scale and its subscales

# items Reading levela M SD Min Max α
Everyday Ageism Scale 10 7.0 10.22 4.55 0 27 .768
(1) exposure to ageist messages 2 6.7 2.86 1.52 0 6 .719
(3) ageism in interpersonal interactions 5 8.2 3.35 2.96 0 15 .777
(2) internalized ageism 3 5.5 4.01 1.79 0 9 .753
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a

Flesch-Kincaid Grade Level

Detailed item analyses (portions not shown) identified issues with individual items requiring further exploration (e.g., items 4 and 7 skew >1.0, item 3 had a low initial communalities, item 10 had a low corrected item-total correlation and initial communality). All ten items were retained since inter-item correlations for each factor were adequate (>.3), Cronbach’s alphas for the overall scale remained comparable (.73-.77) after eliminating individual items, and the data generated a distinct three-factor structure with sound item factor loadings and no cross-loading.

Table 4 presents correlations between the subscales and the overall Everyday Ageism scale. All the subscales were modestly correlated with each other (all p-values <.001). Among the subscales, the dimensions representing external sources of ageism (i.e., ageist messages and interpersonal interactions) were most closely correlated (r=.316), followed by interpersonal and internalized ageism (r=.248), and ageist messages and internalized ageism (r=.171). The overall scale correlated highest with the ageism in interpersonal interactions subscale (r=.855), followed by the internalized ageism and exposure to ageist messages subscales (r=.612 and .607, respectively, all p-values <.001).

Table 4.

Correlation matrix between the Everyday Ageism Scale and its subscales

Everyday Ageism Scale (1) (3)
Everyday Ageism Scale 1
(1) exposure to ageist messages .607*** 1
(3) ageism in interpersonal interactions .855*** .316*** 1
(2) internalized ageism .612*** .171*** .248***
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***

p<.001 (2-tailed tests)

The Everyday Ageism Scale demonstrated known-group validity in two different ways (Table 5). As anticipated, the chronologically older age group and those who reported that they appeared older than their actual age reported more everyday ageism than their younger and younger looking counterparts, respectively. Among the subscales, this pattern was replicated in ageist messages (age group only), interpersonal interactions (both), and internalized ageism (relative appearance only). In general, age group showed evidence of a larger effect size on amount of everyday ageism reported than relative appearance (data not shown).

Table 5.

Confirmation of expected group differences

Age Relative Appearancea
50-64 65-80 Younger Older
M(SD) M(SD) p M(SD) M(SD) p
Everyday Ageism Scale 9.55(4.42) 11.26(4.54) <.001 10.00(4.40) 12.43(4.43) <.001
(1) exposure to ageist messages 2.73(1.52) 3.05(1.49) <.001 3.00(1.43) 2.75(1.50) .066
(3) ageism in interpersonal interactions 2.79(2.79) 4.19(3.02) <.001 3.10(2.86) 4.84(2.88) <.001
(2) internalized ageism 4.02(1.82) 4.01(1.74) .954 3.90(1.79) 4.84(1.85) <.001
N 1215 797 1182 131
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a

Controlling for chronological age; estimates at sample mean age

When stratified by age group, the three-factor structure of the Everyday Ageism Scale was replicated for both the younger (50-64) and older (65-80) age groups (Supplementary Table 1). Both samples were adequate according to the Bartlett’s test of sphericity and KMO. All items loaded in the same pattern as in the EFA for full sample, demonstrating the same direction and approximately the same factor loading magnitude. None of the items cross-loaded on more than one factor, using .35 as the minimum loading threshold. The age groups loadings were highly correlated: Factor 1 r=.985, Factor 2 r =.991, Factor 3 r =.988, and all p-values <.001. The three factors explained 63.48% of the variance for the younger age group and 60.53% for the older age group. These findings provided strong evidence suggestive of measurement invariance across these two age groups of older adults.

Sensitivity analysis using parallel analysis with polychoric correlations (300 iterations) and scree plots confirmed the three-factor structure. Analyses using Z scored item responses to examine differences related to the two different response options were comparable to the presented findings. The only minor differences noted were: 1) standard deviations for the overall scale and its subscales were slightly larger; 2) Cronbach’s alphas changed ±.02; and 3) Table 4 correlation coefficients changed ±.03.

Discussion

This study reports the systematic process used to develop and evaluate the Everyday Ageism Scale. This novel scale was developed to more comprehensively capture the multidimensional nature of ageism that older adults routinely encounter in their daily lives. The concept behind this scale was inspired by the Everyday Discrimination Scale (Williams et al., 1997), with the added advantage for ageism research of identifying and isolating commonplace forms of ageism distinct from other forms of everyday discrimination. The final 10-item scale demonstrated a three-factor structure, with subscales assessing exposure to ageist messages, ageism in interpersonal interactions, and internalized ageist beliefs. Psychometric assessment established the scale as internally reliable and verified convergent and known-group validity. Findings endorse the versatility of the Everyday Ageism Scale. They indicate that the scale can be appropriately employed with a broad age range of older adults, as it captured a consistent construct for adults ages 50-64 and ages 65-80. They also provide support for its use as either a single, overall scale that holistically captures a complex, multidimensional construct or as sets of subscales capturing specific dimensions of ageism.

The creation and evaluation of Everyday Ageism Scale addresses a critical limitation within the field of ageism research by facilitating rigorous, population-based studies seeking to establish the prevalence, distribution, and characteristics of older adults’ exposure to everyday ageism. Ageism is a complex phenomenon comprised of multiple manifestations and interrelated mechanisms that can affect older adults’ lives. Older adults are also a heterogeneous population with diverse lived experiences. Taken together, it is likely that older adults’ experiences with ageism vary widely, both in the overall amount of ageism they experience and in the specific forms or dimensions of ageism they experience. The findings from the current study support this. For example, the significant correlations between the dimension subscales (Table 4) indicate that older adults who reported one form of everyday ageism often reported others. Yet, the modest size of those correlations indicates that people did not report similar amounts of all three dimensions of everyday ageism (e.g., if most of the older adults who reported a great deal of exposure to ageist messages also indicated high levels of internalized and interpersonal ageism, the correlation coefficients between those subscales would have been larger). More research is needed to identify population subgroups that may be particularly vulnerable to everyday ageism, either because they experience above average levels or forms identified as particularly harmful.

Another unique contribution of the current study was the development of subscales assessing two previously unmeasured aspects of external sources of everyday ageism: exposure to ageist messages and ageism in interpersonal interactions. Beliefs, behaviors, and policies related to aging and older adults, including those exemplifying ageism, are shaped by the intersection of biological, historic, sociocultural, political, and economic influences. Accordingly, ageism can affect people through a combination of internal sources (internalized and embodied ageism) and external sources (ageism enacted by other people, in institutional practices and public policies, and within broader society in the form of messages reinforcing ageist prejudices and stereotypes and discrimination). Many existing scales used in ageism research capture internalized aspects of ageism, which is the focus of one of the three Everyday Ageism subscale dimensions. On that note, in-depth studies of everyday ageism may elect to substitute this three-item dimensions of the Everyday Ageism Scale with a more detailed internalized ageism scale, such as 38-item Expectations Regarding Aging Scale (Sarkisian et al., 2002). There is substantial evidence that older adults’ internalization and embodiment of ageist concepts harm their health and wellbeing (Hehman & Bugental, 2015; Hu et al., 2020; Levy, 2003; Levy & Leifheit-Limson, 2009). While older adults are undoubtedly negatively affected by external sources of ageism, much less is known due to the absence of germane scales capturing this dimension of ageism to date. Documenting the adverse outcomes of external sources of ageism will build the case that diminishing ageism as a societal responsibility rather a challenge for older adults to confront on their own. Reducing the consequences of ageism may require multilevel, multisector interventions addressing the multidimensional nature of this phenomenon. The two novel subscales will facilitate research seeking to address these critical gaps in our understanding of how external sources of ageism may affect older adults’ lives and effective strategies for intervention.

This scale represents an important first step in advancing the scientific understanding of relationships between manifestations of everyday ageism and health, and specifically the mechanisms through which these commonplace experiences may influence a range of health and other outcomes. An advantage of the multidimensional nature of the Everyday Ageism Scale is that is can be employed to examine relationships between everyday ageism and various outcomes relevant to older adults in two ways. First, it can be used to examine and compare relationships between individual dimensions of everyday ageism and outcomes and potentially identify more harmful dimensions of everyday ageism. Second, it can also be used to examine the collective impact of everyday ageism, and thereby capture the cumulative or interactive nature of its multiple dimensions. This information can be used to raise public awareness on the effects of everyday ageism on the lives of older adults and inform changes to programs, practices, and policies that perpetuate this form of discrimination. It can also be used to provide evidence in support of initiatives encouraging the adoption of positive views of aging among older adults, themselves, and seeking to alter the negative, stereotypic social narratives surrounding aging and older adults in society more generally.

Parallel research has linked everyday racism to a wide range of poor health outcomes including poor overall self-rated health, hypertension and other cardiovascular disorders, inflammation, psychological distress, depression, anxiety, and premature mortality (Beatty Moody et al., 2014; Farmer et al., 2019; Lewis et al., 2010; Paradies, 2006). While it is plausible that everyday ageism may influence health outcomes via similar mechanisms (e.g., as a source of chronic stress) as everyday racism, several differences between these phenomena would benefit from further exploration. For example, individuals typically experience everyday racism throughout the life course, whereas everyday ageism is most relevant to the latter portion of the life span. Differences in the cumulative effects of everyday discrimination over time or the timing of vulnerable periods in the life course, particularly if they occur during childhood, may generate significantly different relationships to health when comparing everyday ageism and everyday racism, particularly in whether and how strong relationships are between these two forms of discrimination and health.

Everyday ageism is ubiquitous and relatively socially acceptable in the United States when compared to several other common forms of discrimination (Allen, 2016; Angus & Reeve, 2006; Hagestad & Uhlenberg, 2005). Accordingly, it is reasonable to expect that experiences with everyday ageism may be underreported in survey research due to a lack of attention to and awareness of this phenomenon. In addition, self-report measures introduce bias associated with inaccurate recall and social desirability. The Everyday Ageism Scale was designed to mitigate these potential sources of bias in several ways. We engaged in multi-step strategy to identify, develop, and test items that would capture more objectively identified examples of everyday ageism that would be easy for participants to identify and report. Item development also focused on beliefs and concrete behaviors and experiences that did not necessitate that participants perceived them as ageist and/or attribute discrimination to their age. This approach was adopted to reduce risk of social desirability bias. We recognize, however, that stereotypes, norms, and attitudes related to aging, older adults, and ageism evolve over time. This may be particularly true during times when older adults represent a key demographic in debates about major social and political issues, as is the case currently with the ongoing COVID-19 pandemic and its aftermath. The Everyday Ageism scale may need to be periodically reevaluated and updated to reflect cultural shifts in this phenomenon.

While initial work demonstrates promising psychometric properties for the scale, future research is needed to firmly establish the Everyday Ageism Scale as warranting broad-scale adoption and use in ageism research. The current study describes the preliminary development and psychometric evaluation of the Everyday Ageism Scale. Future studies assessing test-retest reliability and criterion concurrent validity with other measures used in ageism research would further enhance the evaluation of the scale. Our team is currently completing a predictive validity study (Allen et al., under review) testing the theorized relationship between everyday ageism and its adverse effects on the mental and physical health of older adults (Allen, 2016). Confirmatory factor analysis is needed for a robust evaluation of the scale and should incorporate rigorous assessment of whether the scale functions similarly (i.e., measurement invariance) for men and women, different racial/ethnic groups, and others. Finally, the relevance of the Everyday Ageism Scale outside the American context is unknown and may necessitate qualitative studies to determine if prevalent examples of everyday ageism, such as those emphasized in the Everyday Ageism Scale, vary by geographic location. Finally, a more expansive study of ageism could pair the Everyday Ageism Scale with items assessing major experiences of ageism (using as an aging-specific variant on the Major Experiences of Discrimination (Williams et al., 2008) or Life Events Checklist (Gray et al., 2004).

Findings should be interpreted considering study limitations. Given National Poll on Healthy Aging logistical requirements, we did not use exhaustive methods in developing the scale, which can be a multi-year process (e.g., Krause, 2002). Accordingly, we were unable to conduct in-depth qualitative research collecting the perspectives and suggestions of older adults. We were also unable to include a comprehensive list of examples of everyday ageism in the piloting or large-scale fielding of the scale. Instead, we focused on more prevalent examples cited in the literature that could be easily included in a relatively brief scale. In addition, Ipsos sampling and data collection procedures may limit the generalizability and appropriateness of the scale and associated findings to adults younger than 50 or over age 80, not residing in community settings (e.g., in long-term care facilities, prison), or adverse to online data collection. Finally, additional research, discussed above, is needed to assess additional psychometric properties of the scale, measurement invariance across demographic groups, and relevance for different geographic contexts.

In summary, the current study describes the development and psychometric evaluation of a novel survey scale on Everyday Ageism. The Everyday Ageism scale is one of the only ageism measures that captures the multidimensional nature of ageism and provides evidence of strong psychometric properties, having already demonstrated internal reliability and several form of validity. Furthermore, it captures a form of ageism that has been difficult to study in population-level survey research previously due to the absence of a measure with the precision to distinguish everyday ageism from other forms of daily discrimination. It is our hope that these strengths will encourage the adoption and use of this measure in ageism research. The Everyday Ageism Scale serves to advance research seeking to refine our understanding of the relationships between ageism and health as well as other outcomes relevant to the lives of older adults.

Supplementary Material

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Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The University of Michigan National Poll on Healthy Aging was sponsored by AARP and Michigan Medicine and directed by the University of Michigan Institute for Healthcare Policy & Innovation. This work was also supported by a grant from the National Institute on Aging at the National Institutes of Health to the Population Studies Center at the University of Michigan [T32-AG000221].

Footnotes

Declaration of Conflicting Interests

The Authors declare that there is no conflict of interest.

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