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Quantitative analysis of human behavior

Affiliation.

• 1 Dipartimento di Ingegneria dell'Informazione, Elettronica e Telecomunicazioni, Università La Sapienza, Rome, Italy. [email protected]
• PMID: 20949249

Many aspects of individual as well as social behaviours of human beings can be analyzed in a quantitative way using typical scientific methods, based on empirical measurements and mathematical inference. Measurements are made possible today by the large variety of sensing devices, while formal models are synthesized using modern system and information theories.

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• Published: 08 April 2024

A systematic review and multivariate meta-analysis of the physical and mental health benefits of touch interventions

• Julian Packheiser   ORCID: orcid.org/0000-0001-9805-6755 2   na1   nAff1 ,
• Helena Hartmann 2 , 3 , 4   na1 ,
• Kelly Fredriksen 2 ,
• Valeria Gazzola   ORCID: orcid.org/0000-0003-0324-0619 2 ,
• Christian Keysers   ORCID: orcid.org/0000-0002-2845-5467 2 &
• Frédéric Michon   ORCID: orcid.org/0000-0003-1289-2133 2  

Nature Human Behaviour ( 2024 ) Cite this article

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• Human behaviour
• Paediatric research
• Randomized controlled trials

Receiving touch is of critical importance, as many studies have shown that touch promotes mental and physical well-being. We conducted a pre-registered (PROSPERO: CRD42022304281) systematic review and multilevel meta-analysis encompassing 137 studies in the meta-analysis and 75 additional studies in the systematic review ( n  = 12,966 individuals, search via Google Scholar, PubMed and Web of Science until 1 October 2022) to identify critical factors moderating touch intervention efficacy. Included studies always featured a touch versus no touch control intervention with diverse health outcomes as dependent variables. Risk of bias was assessed via small study, randomization, sequencing, performance and attrition bias. Touch interventions were especially effective in regulating cortisol levels (Hedges’ g  = 0.78, 95% confidence interval (CI) 0.24 to 1.31) and increasing weight (0.65, 95% CI 0.37 to 0.94) in newborns as well as in reducing pain (0.69, 95% CI 0.48 to 0.89), feelings of depression (0.59, 95% CI 0.40 to 0.78) and state (0.64, 95% CI 0.44 to 0.84) or trait anxiety (0.59, 95% CI 0.40 to 0.77) for adults. Comparing touch interventions involving objects or robots resulted in similar physical (0.56, 95% CI 0.24 to 0.88 versus 0.51, 95% CI 0.38 to 0.64) but lower mental health benefits (0.34, 95% CI 0.19 to 0.49 versus 0.58, 95% CI 0.43 to 0.73). Adult clinical cohorts profited more strongly in mental health domains compared with healthy individuals (0.63, 95% CI 0.46 to 0.80 versus 0.37, 95% CI 0.20 to 0.55). We found no difference in health benefits in adults when comparing touch applied by a familiar person or a health care professional (0.51, 95% CI 0.29 to 0.73 versus 0.50, 95% CI 0.38 to 0.61), but parental touch was more beneficial in newborns (0.69, 95% CI 0.50 to 0.88 versus 0.39, 95% CI 0.18 to 0.61). Small but significant small study bias and the impossibility to blind experimental conditions need to be considered. Leveraging factors that influence touch intervention efficacy will help maximize the benefits of future interventions and focus research in this field.

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The sense of touch has immense importance for many aspects of our life. It is the first of all the senses to develop in newborns 1 and the most direct experience of contact with our physical and social environment 2 . Complementing our own touch experience, we also regularly receive touch from others around us, for example, through consensual hugs, kisses or massages 3 .

The recent coronavirus pandemic has raised awareness regarding the need to better understand the effects that touch—and its reduction during social distancing—can have on our mental and physical well-being. The most common touch interventions, for example, massage for adults or kangaroo care for newborns, have been shown to have a wide range of both mental and physical health benefits, from facilitating growth and development to buffering against anxiety and stress, over the lifespan of humans and animals alike 4 . Despite the substantial weight this literature gives to support the benefits of touch, it is also characterized by a large variability in, for example, studied cohorts (adults, children, newborns and animals), type and duration of applied touch (for example, one-time hug versus repeated 60-min massages), measured health outcomes (ranging from physical health outcomes such as sleep and blood pressure to mental health outcomes such as depression or mood) and who actually applies the touch (for example, partner versus stranger).

A meaningful tool to make sense of this vast amount of research is through meta-analysis. While previous meta-analyses on this topic exist, they were limited in scope, focusing only on particular types of touch, cohorts or specific health outcomes (for example, refs. 5 , 6 ). Furthermore, despite best efforts, meaningful variables that moderate the efficacy of touch interventions could not yet be identified. However, understanding these variables is critical to tailor touch interventions and guide future research to navigate this diverse field with the ultimate aim of promoting well-being in the population.

In this Article, we describe a pre-registered, large-scale systematic review and multilevel, multivariate meta-analysis to address this need with quantitative evidence for (1) the effect of touch interventions on physical and mental health and (2) which moderators influence the efficacy of the intervention. In particular, we ask whether and how strongly health outcomes depend on the dynamics of the touching dyad (for example, humans or robots/objects, familiarity and touch directionality), demographics (for example, clinical status, age or sex), delivery means (for example, type of touch intervention or touched body part) and procedure (for example, duration or number of sessions). We did so separately for newborns and for children and adults, as the health outcomes in newborns differed substantially from those in the other age groups. Despite the focus of the analysis being on humans, it is widely known that many animal species benefit from touch interactions and that engaging in touch promotes their well-being as well 7 . Since animal models are essential for the investigation of the mechanisms underlying biological processes and for the development of therapeutic approaches, we accordingly included health benefits of touch interventions in non-human animals as part of our systematic review. However, this search yielded only a small number of studies, suggesting a lack of research in this domain, and as such, was insufficient to be included in the meta-analysis. We evaluate the identified animal studies and their findings in the discussion.

Touch interventions have a medium-sized effect

The pre-registration can be found at ref. 8 . The flowchart for data collection and extraction is depicted in Fig. 1 .

Animal outcomes refer to outcomes measured in non-human species that were solely considered as part of a systematic review. Included languages were French, Dutch, German and English, but our search did not identify any articles in French, Dutch or German. MA, meta-analysis.

For adults, a total of n  = 2,841 and n  = 2,556 individuals in the touch and control groups, respectively, across 85 studies and 103 cohorts were included. The effect of touch overall was medium-sized ( t (102) = 9.74, P  < 0.001, Hedges’ g  = 0.52, 95% confidence interval (CI) 0.42 to 0.63; Fig. 2a ). For newborns, we could include 63 cohorts across 52 studies comprising a total of n  = 2,134 and n  = 2,086 newborns in the touch and control groups, respectively, with an overall effect almost identical to the older age group ( t (62) = 7.53, P  < 0.001, Hedges’ g  = 0.56, 95% CI 0.41 to 0.71; Fig. 2b ), suggesting that, despite distinct health outcomes, touch interventions show comparable effects across newborns and adults. Using these overall effect estimates, we conducted a power sensitivity analysis of all the included primary studies to investigate whether such effects could be reliably detected 9 . Sufficient power to detect such effect sizes was rare in individual studies, as investigated by firepower plots 10 (Supplementary Figs. 1 and 2 ). No individual effect size from either meta-analysis was overly influential (Cook’s D  < 0.06). The benefits were similar for mental and physical outcomes (mental versus physical; adults: t (101) = 0.79, P  = 0.432, Hedges’ g difference of −0.05, 95% CI −0.16 to 0.07, Fig. 2c ; newborns: t (61) = 1.08, P  = 0.284, Hedges’ g difference of −0.19, 95% CI −0.53 to 0.16, Fig. 2d ).

a , Orchard plot illustrating the overall benefits across all health outcomes for adults/children across 469 in part dependent effect sizes from 85 studies and 103 cohorts. b , The same as a but for newborns across 174 in part dependent effect sizes from 52 studies and 63 cohorts. c , The same as a but separating the results for physical versus mental health benefits across 469 in part dependent effect sizes from 85 studies and 103 cohorts. d , The same as b but separating the results for physical versus mental health benefits across 172 in part dependent effect sizes from 52 studies and 63 cohorts. Each dot reflects a measured effect, and the number of effects ( k ) included in the analysis is depicted in the bottom left. Mean effects and 95% CIs are presented in the bottom right and are indicated by the central black dot (mean effect) and its error bars (95% CI). The heterogeneity Q statistic is presented in the top left. Overall effects of moderator impact were assessed via an F test, and post hoc comparisons were done using t tests (two-sided test). Note that the P values above the mean effects indicate whether an effect differed significantly from a zero effect. P values were not corrected for multiple comparisons. The dot size reflects the precision of each individual effect (larger indicates higher precision). Small-study bias for the overall effect was significant ( F test, two-sided test) in the adult meta-analysis ( F (1, 101) = 21.24, P  < 0.001; Supplementary Fig. 3 ) as well as in the newborn meta-analysis ( F (1, 61) = 5.25, P  = 0.025; Supplementary Fig. 4 ).

Source data

On the basis of the overall effect of both meta-analyses as well as their median sample sizes, the minimum number of studies necessary for subgroup analyses to achieve 80% power was k  = 9 effects for adults and k  = 8 effects for newborns (Supplementary Figs. 5 and 6 ). Assessing specific health outcomes with sufficient power in more detail in adults (Fig. 3a ) revealed smaller benefits to sleep and heart rate parameters, moderate benefits to positive and negative affect, diastolic blood and systolic blood pressure, mobility and reductions of the stress hormone cortisol and larger benefits to trait and state anxiety, depression, fatigue and pain. Post hoc tests revealed stronger benefits for pain, state anxiety, depression and trait anxiety compared with respiratory, sleep and heart rate parameters (see Fig. 3 for all post hoc comparisons). Reductions in pain and state anxiety were increased compared with reductions in negative affect ( t (83) = 2.54, P  = 0.013, Hedges’ g difference of 0.31, 95% CI 0.07 to 0.55; t (83) = 2.31, P  = 0.024, Hedges’ g difference of 0.27, 95% CI 0.03 to 0.51). Benefits to pain symptoms were higher compared with benefits to positive affect ( t (83) = 2.22, P  = 0.030, Hedges’ g difference of 0.29, 95% CI 0.04 to 0.54). Finally, touch resulted in larger benefits to cortisol release compared with heart rate parameters ( t (83) = 2.30, P  = 0.024, Hedges’ g difference of 0.26, 95% CI 0.04–0.48).

a , b , Health outcomes in adults analysed across 405 in part dependent effect sizes from 79 studies and 97 cohorts ( a ) and in newborns analysed across 105 in part dependent effect sizes from 46 studies and 56 cohorts ( b ). The type of health outcomes measured differed between adults and newborns and were thus analysed separately. Numbers on the right represent the mean effect with its 95% CI in square brackets and the significance level estimating the likelihood that the effect is equal to zero. Overall effects of moderator impact were assessed via an F test, and post hoc comparisons were done using t tests (two-sided test). The F value in the top right represents a test of the hypothesis that all effects within the subpanel are equal. The Q statistic represents the heterogeneity. P values of post hoc tests are depicted whenever significant. P values above the horizontal whiskers indicate whether an effect differed significantly from a zero effect. Vertical lines indicate significant post hoc tests between moderator levels. P values were not corrected for multiple comparisons. Physical outcomes are marked in red. Mental outcomes are marked in blue.

In newborns, only physical health effects offered sufficient data for further analysis. We found no benefits for digestion and heart rate parameters. All other health outcomes (cortisol, liver enzymes, respiration, temperature regulation and weight gain) showed medium to large effects (Fig. 3b ). We found no significant differences among any specific health outcomes.

Non-human touch and skin-to-skin contact

In some situations, a fellow human is not readily available to provide affective touch, raising the question of the efficacy of touch delivered by objects and robots 11 . Overall, we found humans engaging in touch with other humans or objects to have medium-sized health benefits in adults, without significant differences ( t (99) = 1.05, P  = 0.295, Hedges’ g difference of 0.12, 95% CI −0.11 to 0.35; Fig. 4a ). However, differentiating physical versus mental health benefits revealed similar benefits for human and object touch on physical health outcomes, but larger benefits on mental outcomes when humans were touched by humans ( t (97) = 2.32, P  = 0.022, Hedges’ g difference of 0.24, 95% CI 0.04 to 0.44; Fig. 4b ). It must be noted that touching with an object still showed a significant effect (see Supplementary Fig. 7 for the corresponding orchard plot).

a , Forest plot comparing humans versus objects touching a human on health outcomes overall across 467 in part dependent effect sizes from 85 studies and 101 cohorts. b , The same as a but separately for mental versus physical health outcomes across 467 in part dependent effect sizes from 85 studies and 101 cohorts. c , Results with the removal of all object studies, leaving 406 in part dependent effect sizes from 71 studies and 88 cohorts to identify whether missing skin-to-skin contact is the relevant mediator of higher mental health effects in human–human interactions. Numbers on the right represent the mean effect with its 95% CI in square brackets and the significance level estimating the likelihood that the effect is equal to zero. Overall effects of moderator impact were assessed via an F test, and post hoc comparisons were done using t tests (two-sided test). The F value in the top right represents a test of the hypothesis that all effects within the subpanel are equal. The Q statistic represents the heterogeneity. P values of post hoc tests are depicted whenever significant. P values above the horizontal whiskers indicate whether an effect differed significantly from a zero effect. Vertical lines indicate significant post hoc tests between moderator levels. P values were not corrected for multiple comparisons. Physical outcomes are marked in red. Mental outcomes are marked in blue.

We considered the possibility that this effect was due to missing skin-to-skin contact in human–object interactions. Thus, we investigated human–human interactions with and without skin-to-skin contact (Fig. 4c ). In line with the hypothesis that skin-to-skin contact is highly relevant, we again found stronger mental health benefits in the presence of skin-to-skin contact that however did not achieve nominal significance ( t (69) = 1.95, P  = 0.055, Hedges’ g difference of 0.41, 95% CI −0.00 to 0.82), possibly because skin-to-skin contact was rarely absent in human–human interactions, leading to a decrease in power of this analysis. Results for skin-to-skin contact as an overall moderator can be found in Supplementary Fig. 8 .

Influences of type of touch

The large majority of touch interventions comprised massage therapy in adults and kangaroo care in newborns (see Supplementary Table 1 for a complete list of interventions across studies). However, comparing the different types of touch explored across studies did not reveal significant differences in effect sizes based on touch type, be it on overall health benefits (adults: t (101) = 0.11, P  = 0.916, Hedges’ g difference of 0.02, 95% CI −0.32 to 0.29; Fig. 5a ) or comparing different forms of touch separately for physical (massage therapy versus other forms: t (99) = 0.99, P  = 0.325, Hedges’ g difference 0.16, 95% CI −0.15 to 0.47) or for mental health benefits (massage therapy versus other forms: t (99) = 0.75, P  = 0.458, Hedges’ g difference of 0.13, 95% CI −0.22 to 0.48) in adults (Fig. 5c ; see Supplementary Fig. 9 for the corresponding orchard plot). A similar picture emerged for physical health effects in newborns (massage therapy versus kangaroo care: t (58) = 0.94, P  = 0.353, Hedges’ g difference of 0.15, 95% CI −0.17 to 0.47; massage therapy versus other forms: t (58) = 0.56, P  = 0.577, Hedges’ g difference of 0.13, 95% CI −0.34 to 0.60; kangaroo care versus other forms: t (58) = 0.07, P  = 0.947, Hedges’ g difference of 0.02, 95% CI −0.46 to 0.50; Fig. 5d ; see also Supplementary Fig. 10 for the corresponding orchard plot). This suggests that touch types may be flexibly adapted to the setting of every touch intervention.

a , Forest plot of health benefits comparing massage therapy versus other forms of touch in adult cohorts across 469 in part dependent effect sizes from 85 studies and 103 cohorts. b , Forest plot of health benefits comparing massage therapy, kangaroo care and other forms of touch for newborns across 174 in part dependent effect sizes from 52 studies and 63 cohorts. c , The same as a but separating mental and physical health benefits across 469 in part dependent effect sizes from 85 studies and 103 cohorts. d , The same as b but separating mental and physical health outcomes where possible across 164 in part dependent effect sizes from 51 studies and 62 cohorts. Note that an insufficient number of studies assessed mental health benefits of massage therapy or other forms of touch to be included. Numbers on the right represent the mean effect with its 95% CI in square brackets and the significance level estimating the likelihood that the effect is equal to zero. Overall effects of moderator impact were assessed via an F test, and post hoc comparisons were done using t tests (two-sided test). The F value in the top right represents a test of the hypothesis that all effects within the subpanel are equal. The Q statistic represents heterogeneity. P values of post hoc tests are depicted whenever significant. P values above the horizontal whiskers indicate whether an effect differed significantly from a zero effect. Vertical lines indicate significant post hoc tests between moderator levels. P values were not corrected for multiple comparisons. Physical outcomes are marked in red. Mental outcomes are marked in blue.

The role of clinical status

Most research on touch interventions has focused on clinical samples, but are benefits restricted to clinical cohorts? We found health benefits to be significant in clinical and healthy populations (Fig. 6 ), whether all outcomes are considered (Fig. 6a,b ) or physical and mental health outcomes are separated (Fig. 6c,d , see Supplementary Figs. 11 and 12 for the corresponding orchard plots). In adults, however, we found higher mental health benefits for clinical populations compared with healthy ones (Fig. 6c ; t (99) = 2.11, P  = 0.037, Hedges’ g difference of 0.25, 95% CI 0.01 to 0.49).

a , Health benefits for clinical cohorts of adults versus healthy cohorts of adults across 469 in part dependent effect sizes from 85 studies and 103 cohorts. b , The same as a but for newborn cohorts across 174 in part dependent effect sizes from 52 studies and 63 cohorts. c , The same as a but separating mental versus physical health benefits across 469 in part dependent effect sizes from 85 studies and 103 cohorts. d , The same as b but separating mental versus physical health benefits across 172 in part dependent effect sizes from 52 studies and 63 cohorts. Numbers on the right represent the mean effect with its 95% CI in square brackets and the significance level estimating the likelihood that the effect is equal to zero. Overall effects of moderator impact were assessed via an F test, and post hoc comparisons were done using t tests (two-sided test).The F value in the top right represents a test of the hypothesis that all effects within the subpanel are equal. The Q statistic represents the heterogeneity. P values of post hoc tests are depicted whenever significant. P values above the horizontal whiskers indicate whether an effect differed significantly from a zero effect. Vertical lines indicate significant post hoc tests between moderator levels. P values were not corrected for multiple comparisons. Physical outcomes are marked in red. Mental outcomes are marked in blue.

A more detailed analysis of specific clinical conditions in adults revealed positive mental and physical health benefits for almost all assessed clinical disorders. Differences between disorders were not found, with the exception of increased effectiveness of touch interventions in neurological disorders (Supplementary Fig. 13 ).

Familiarity in the touching dyad and intervention location

Touch interventions can be performed either by familiar touchers (partners, family members or friends) or by unfamiliar touchers (health care professionals). In adults, we did not find an impact of familiarity of the toucher ( t (99) = 0.12, P  = 0.905, Hedges’ g difference of 0.02, 95% CI −0.27 to 0.24; Fig. 7a ; see Supplementary Fig. 14 for the corresponding orchard plot). Similarly, investigating the impact on mental and physical health benefits specifically, no significant differences could be detected, suggesting that familiarity is irrelevant in adults. In contrast, touch applied to newborns by their parents (almost all studies only included touch by the mother) was significantly more beneficial compared with unfamiliar touch ( t (60) = 2.09, P  = 0.041, Hedges’ g difference of 0.30, 95% CI 0.01 to 0.59) (Fig. 7b ; see Supplementary Fig. 15 for the corresponding orchard plot). Investigating mental and physical health benefits specifically revealed no significant differences. Familiarity with the location in which the touch was applied (familiar being, for example, the participants’ home) did not influence the efficacy of touch interventions (Supplementary Fig. 16 ).

a , Health benefits for being touched by a familiar (for example, partner, family member or friend) versus unfamiliar toucher (health care professional) across 463 in part dependent effect sizes from 83 studies and 101 cohorts. b , The same as a but for newborn cohorts across 171 in part dependent effect sizes from 51 studies and 62 cohorts. c , The same as a but separating mental versus physical health benefits across 463 in part dependent effect sizes from 83 studies and 101 cohorts. d , The same as b but separating mental versus physical health benefits across 169 in part dependent effect sizes from 51 studies and 62 cohorts. Numbers on the right represent the mean effect with its 95% CI in square brackets and the significance level estimating the likelihood that the effect is equal to zero. Overall effects of moderator impact were assessed via an F test, and post hoc comparisons were done using t tests (two-sided test). The F value in the top right represents a test of the hypothesis that all effects within the subpanel are equal. The Q statistic represents the heterogeneity. P values of post hoc tests are depicted whenever significant. P values above the horizontal whiskers indicate whether an effect differed significantly from a zero effect. Vertical lines indicate significant post hoc tests between moderator levels. P values were not corrected for multiple comparisons. Physical outcomes are marked in red. Mental outcomes are marked in blue.

Frequency and duration of touch interventions

How often and for how long should touch be delivered? For adults, the median touch duration across studies was 20 min and the median number of touch interventions was four sessions with an average time interval of 2.3 days between each session. For newborns, the median touch duration across studies was 17.5 min and the median number of touch interventions was seven sessions with an average time interval of 1.3 days between each session.

Delivering more touch sessions increased benefits in adults, whether overall ( t (101) = 4.90, P  < 0.001, Hedges’ g  = 0.02, 95% CI 0.01 to 0.03), physical ( t (81) = 3.07, P  = 0.003, Hedges’ g  = 0.02, 95% CI 0.01–0.03) or mental benefits ( t (72) = 5.43, P  < 0.001, Hedges’ g  = 0.02, 95% CI 0.01–0.03) were measured (Fig. 8a ). A closer look at specific outcomes for which sufficient data were available revealed that positive associations between the number of sessions and outcomes were found for trait anxiety ( t (12) = 7.90, P  < 0.001, Hedges’ g  = 0.03, 95% CI 0.02–0.04), depression ( t (20) = 10.69, P  < 0.001, Hedges’ g  = 0.03, 95% CI 0.03–0.04) and pain ( t (37) = 3.65, P  < 0.001, Hedges’ g  = 0.03, 95% CI 0.02–0.05), indicating a need for repeated sessions to improve these adverse health outcomes. Neither increasing the number of sessions for newborns nor increasing the duration of touch per session in adults or newborns increased health benefits, be they physical or mental (Fig. 8b–d ). For continuous moderators in adults, we also looked at specific health outcomes as sufficient data were generally available for further analysis. Surprisingly, we found significant negative associations between touch duration and reductions of cortisol ( t (24) = 2.71, P  = 0.012, Hedges’ g  = −0.01, 95% CI −0.01 to −0.00) and heart rate parameters ( t (21) = 2.35, P  = 0.029, Hedges’ g  = −0.01, 95% CI −0.02 to −0.00).

a , Meta-regression analysis examining the association between the number of sessions applied and the effect size in adults, either on overall health benefits (left, 469 in part dependent effect sizes from 85 studies and 103 cohorts) or for physical (middle, 245 in part dependent effect sizes from 69 studies and 83 cohorts) or mental benefits (right, 224 in part dependent effect sizes from 60 studies and 74 cohorts) separately. b , The same as a for newborns (overall: 150 in part dependent effect sizes from 46 studies and 53 cohorts; physical health: 127 in part dependent effect sizes from 44 studies and 51 cohorts; mental health: 21 in part dependent effect sizes from 11 studies and 12 cohorts). c , d the same as a ( c ) and b ( d ) but for the duration of the individual sessions. For adults, 449 in part dependent effect sizes across 80 studies and 96 cohorts were included in the overall analysis. The analysis of physical health benefits included 240 in part dependent effect sizes across 67 studies and 80 cohorts, and the analysis of mental health benefits included 209 in part dependent effect sizes from 56 studies and 69 cohorts. For newborns, 145 in part dependent effect sizes across 45 studies and 52 cohorts were included in the overall analysis. The analysis of physical health benefits included 122 in part dependent effect sizes across 43 studies and 50 cohorts, and the analysis of mental health benefits included 21 in part dependent effect sizes from 11 studies and 12 cohorts. Each dot represents an effect size. Its size indicates the precision of the study (larger indicates better). Overall effects of moderator impact were assessed via an F test (two-sided test). The P values in each panel represent the result of a regression analysis testing the hypothesis that the slope of the relationship is equal to zero. P values are not corrected for multiple testing. The shaded area around the regression line represents the 95% CI.

Demographic influences of sex and age

We used the ratio between women and men in the single-study samples as a proxy for sex-specific effects. Sex ratios were heavily skewed towards larger numbers of women in each cohort (median 83% women), and we could not find significant associations between sex ratio and overall ( t (62) = 0.08, P  = 0.935, Hedges’ g  = 0.00, 95% CI −0.00 to 0.01), mental ( t (43) = 0.55, P  = 0.588, Hedges’ g  = 0.00, 95% CI −0.00 to 0.01) or physical health benefits ( t (51) = 0.15, P  = 0.882, Hedges’ g  = −0.00, 95% CI −0.01 to 0.01). For specific outcomes that could be further analysed, we found a significant positive association of sex ratio with reductions in cortisol secretion ( t (18) = 2.31, P  = 0.033, Hedges’ g  = 0.01, 95% CI 0.00 to 0.01) suggesting stronger benefits in women. In contrast to adults, sex ratios were balanced in samples of newborns (median 53% girls). For newborns, there was no significant association with overall ( t (36) = 0.77, P  = 0.447, Hedges’ g  = −0.01, 95% CI −0.02 to 0.01) and physical health benefits of touch ( t (35) = 0.93, P  = 0.359, Hedges’ g  = −0.01, 95% CI −0.02 to 0.01). Mental health benefits did not provide sufficient data for further analysis.

The median age in the adult meta-analysis was 42.6 years (s.d. 21.16 years, range 4.5–88.4 years). There was no association between age and the overall ( t (73) = 0.35, P  = 0.727, Hedges’ g = 0.00, 95% CI −0.01 to 0.01), mental ( t (53) = 0.94, P  = 0.353, Hedges’ g  = 0.01, 95% CI −0.01 to 0.02) and physical health benefits of touch ( t (60) = 0.16, P  = 0.870, Hedges’ g  = 0.00, 95% CI −0.01 to 0.01). Looking at specific health outcomes, we found significant positive associations between mean age and improved positive affect ( t (10) = 2.54, P  = 0.030, Hedges’ g  = 0.01, 95% CI 0.00 to 0.02) as well as systolic blood pressure ( t (11) = 2.39, P  = 0.036, Hedges’ g  = 0.02, 95% CI 0.00 to 0.04).

A list of touched body parts can be found in Supplementary Table 1 . For the touched body part, we found significantly higher health benefits for head touch compared with arm touch ( t (40) = 2.14, P  = 0.039, Hedges’ g difference of 0.78, 95% CI 0.07 to 1.49) and torso touch ( t (40) = 2.23, P  = 0.031; Hedges’ g difference of 0.84, 95% CI 0.10 to 1.58; Supplementary Fig. 17 ). Touching the arm resulted in lower mental health compared with physical health benefits ( t (37) = 2.29, P  = 0.028, Hedges’ g difference of −0.35, 95% CI −0.65 to −0.05). Furthermore, we found a significantly increased physical health benefit when the head was touched as opposed to the torso ( t (37) = 2.10, P  = 0.043, Hedges’ g difference of 0.96, 95% CI 0.06 to 1.86). Thus, head touch such as a face or scalp massage could be especially beneficial.

Directionality

In adults, we tested whether a uni- or bidirectional application of touch mattered. The large majority of touch was applied unidirectionally ( k  = 442 of 469 effects). Unidirectional touch had higher health benefits ( t (101) = 2.17, P  = 0.032, Hedges’ g difference of 0.30, 95% CI 0.03 to 0.58) than bidirectional touch. Specifically, mental health benefits were higher in unidirectional touch ( t (99) = 2.33, P  = 0.022, Hedges’ g difference of 0.46, 95% CI 0.06 to 0.66).

Study location

For adults, we found significantly stronger health benefits of touch in South American compared with North American cohorts ( t (95) = 2.03, P  = 0.046, Hedges’ g difference of 0.37, 95% CI 0.01 to 0.73) and European cohorts ( t (95) = 2.22, P  = 0.029, Hedges’ g difference of 0.36, 95% CI 0.04 to 0.68). For newborns, we found weaker effects in North American cohorts compared to Asian ( t (55) = 2.28, P  = 0.026, Hedges’ g difference of −0.37, 95% CI −0.69 to −0.05) and European cohorts ( t (55) = 2.36, P  = 0.022, Hedges’ g difference of −0.40, 95% CI −0.74 to −0.06). Investigating the interaction with mental and physical health benefits did not reveal any effects of study location in both meta-analyses (Supplementary Fig. 18 ).

Systematic review of studies without effect sizes

All studies where effect size data could not be obtained or that did not meet the meta-analysis inclusion criteria can be found on the OSF project 12 in the file ‘Study_lists_final_revised.xlsx’ (sheet ‘Studies_without_effect_sizes’). Specific reasons for exclusion are furthermore documented in Supplementary Table 2 . For human health outcomes assessed across 56 studies and n  = 2,438 individuals, interventions mostly comprised massage therapy ( k  = 86 health outcomes) and kangaroo care ( k  = 33 health outcomes). For datasets where no effect size could be computed, 90.0% of mental health and 84.3% of physical health parameters were positively impacted by touch. Positive impact of touch did not differ between types of touch interventions. These results match well with the observations of the meta-analysis of a highly positive benefit of touch overall, irrespective of whether a massage or any other intervention is applied.

We also assessed health outcomes in animals across 19 studies and n  = 911 subjects. Most research was conducted in rodents. Animals that received touch were rats (ten studies, k  = 16 health outcomes), mice (four studies, k  = 7 health outcomes), macaques (two studies, k  = 3 health outcomes), cats (one study, k  = 3 health outcomes), lambs (one study, k  = 2 health outcomes) and coral reef fish (one study, k  = 1 health outcome). Touch interventions mostly comprised stroking ( k  = 13 health outcomes) and tickling ( k  = 10 health outcomes). For animal studies, 71.4% of effects showed benefits to mental health-like parameters and 81.8% showed positive physical health effects. We thus found strong evidence that touch interventions, which were mostly conducted by humans (16 studies with human touch versus 3 studies with object touch), had positive health effects in animal species as well.

The key aim of the present study was twofold: (1) to provide an estimate of the effect size of touch interventions and (2) to disambiguate moderating factors to potentially tailor future interventions more precisely. Overall, touch interventions were beneficial for both physical and mental health, with a medium effect size. Our work illustrates that touch interventions are best suited for reducing pain, depression and anxiety in adults and children as well as for increasing weight gain in newborns. These findings are in line with previous meta-analyses on this topic, supporting their conclusions and their robustness to the addition of more datasets. One limitation of previous meta-analyses is that they focused on specific health outcomes or populations, despite primary studies often reporting effects on multiple health parameters simultaneously (for example, ref. 13 focusing on neck and shoulder pain and ref. 14 focusing on massage therapy in preterms). To our knowledge, only ref. 5 provides a multivariate picture for a large number of dependent variables. However, this study analysed their data in separate random effects models that did not account for multivariate reporting nor for the multilevel structure of the data, as such approaches have only become available recently. Thus, in addition to adding a substantial amount of new data, our statistical approach provides a more accurate depiction of effect size estimates. Additionally, our study investigated a variety of moderating effects that did not reach significance (for example, sex ratio, mean age or intervention duration) or were not considered (for example, the benefits of robot or object touch) in previous meta-analyses in relation to touch intervention efficacy 5 , probably because of the small number of studies with information on these moderators in the past. Owing to our large-scale approach, we reached high statistical power for many moderator analyses. Finally, previous meta-analyses on this topic exclusively focused on massage therapy in adults or kangaroo care in newborns 15 , leaving out a large number of interventions that are being carried out in research as well as in everyday life to improve well-being. Incorporating these studies into our study, we found that, in general, both massages and other types of touch, such as gentle touch, stroking or kangaroo care, showed similar health benefits.

While it seems to be less critical which touch intervention is applied, the frequency of interventions seems to matter. More sessions were positively associated with the improvement of trait outcomes such as depression and anxiety but also pain reductions in adults. In contrast to session number, increasing the duration of individual sessions did not improve health effects. In fact, we found some indications of negative relationships in adults for cortisol and blood pressure. This could be due to habituating effects of touch on the sympathetic nervous system and hypothalamic–pituitary–adrenal axis, ultimately resulting in diminished effects with longer exposure, or decreased pleasantness ratings of affective touch with increasing duration 16 . For newborns, we could not support previous notions that the duration of the touch intervention is linked to benefits in weight gain 17 . Thus, an ideal intervention protocol does not seem to have to be excessively long. It should be noted that very few interventions lasted less than 5 min, and it therefore remains unclear whether very short interventions have the same effect.

A critical issue highlighted in the pandemic was the lack of touch due to social restrictions 18 . To accommodate the need for touch in individuals with small social networks (for example, institutionalized or isolated individuals), touch interventions using objects/robots have been explored in the past (for a review, see ref. 11 ). We show here that touch interactions outside of the human–human domain are beneficial for mental and physical health outcomes. Importantly, object/robot touch was not as effective in improving mental health as human-applied touch. A sub-analysis of missing skin-to-skin contact among humans indicated that mental health effects of touch might be mediated by the presence of skin-to-skin contact. Thus, it seems profitable to include skin-to-skin contact in future touch interventions, in line with previous findings in newborns 19 . In robots, recent advancements in synthetic skin 20 should be investigated further in this regard. It should be noted that, although we did not observe significant differences in physical health benefits between human–human and human–object touch, the variability of effect sizes was higher in human–object touch. The conditions enabling object or robot interactions to improve well-being should therefore be explored in more detail in the future.

Touch was beneficial for both healthy and clinical cohorts. These data are critical as most previous meta-analytic research has focused on individuals diagnosed with clinical disorders (for example, ref. 6 ). For mental health outcomes, we found larger effects in clinical cohorts. A possible reason could relate to increased touch wanting 21 in patients. For example, loneliness often co-occurs with chronic illnesses 22 , which are linked to depressed mood and feelings of anxiety 23 . Touch can be used to counteract this negative development 24 , 25 . In adults and children, knowing the toucher did not influence health benefits. In contrast, familiarity affected overall health benefits in newborns, with parental touch being more beneficial than touch applied by medical staff. Previous studies have suggested that early skin-to-skin contact and exposure to maternal odour is critical for a newborn’s ability to adapt to a new environment 26 , supporting the notion that parental care is difficult to substitute in this time period.

With respect to age-related effects, our data further suggest that increasing age was associated with a higher benefit through touch for systolic blood pressure. These findings could potentially be attributed to higher basal blood pressure 27 with increasing age, allowing for a stronger modulation of this parameter. For sex differences, our study provides some evidence that there are differences between women and men with respect to health benefits of touch. Overall, research on sex differences in touch processing is relatively sparse (but see refs. 28 , 29 ). Our results suggest that buffering effects against physiological stress are stronger in women. This is in line with increased buffering effects of hugs in women compared with men 30 . The female-biased primary research in adults, however, begs for more research in men or non-binary individuals. Unfortunately, our study could not dive deeper into this topic as health benefits broken down by sex or gender were almost never provided. Recent research has demonstrated that sensory pleasantness is affected by sex and that this also interacts with the familiarity of the other person in the touching dyad 29 , 31 . In general, contextual factors such as sex and gender or the relationship of the touching dyad, differences in cultural background or internal states such as stress have been demonstrated to be highly influential in the perception of affective touch and are thus relevant to maximizing the pleasantness and ultimately the health benefits of touch interactions 32 , 33 , 34 . As a positive personal relationship within the touching dyad is paramount to induce positive health effects, future research applying robot touch to promote well-being should therefore not only explore synthetic skin options but also focus on improving robots as social agents that form a close relationship with the person receiving the touch 35 .

As part of the systematic review, we also assessed the effects of touch interventions in non-human animals. Mimicking the results of the meta-analysis in humans, beneficial effects of touch in animals were comparably strong for mental health-like and physical health outcomes. This may inform interventions to promote animal welfare in the context of animal experiments 36 , farming 37 and pets 38 . While most studies investigated effects in rodents, which are mostly used as laboratory animals, these results probably transfer to livestock and common pets as well. Indeed, touch was beneficial in lambs, fish and cats 39 , 40 , 41 . The positive impact of human touch in rodents also allows for future mechanistic studies in animal models to investigate how interventions such as tickling or stroking modulate hormonal and neuronal responses to touch in the brain. Furthermore, the commonly proposed oxytocin hypothesis can be causally investigated in these animal models through, for example, optogenetic or chemogenetic techniques 42 . We believe that such translational approaches will further help in optimizing future interventions in humans by uncovering the underlying mechanisms and brain circuits involved in touch.

Our results offer many promising avenues to improve future touch interventions, but they also need to be discussed in light of their limitations. While the majority of findings showed robust health benefits of touch interventions across moderators when compared with a null effect, post hoc tests of, for example, familiarity effects in newborns or mental health benefit differences between human and object touch only barely reached significance. Since we computed a large number of statistical tests in the present study, there is a risk that these results are false positives. We hope that researchers in this field are stimulated by these intriguing results and target these questions by primary research through controlled experimental designs within a well-powered study. Furthermore, the presence of small-study bias in both meta-analyses is indicative that the effect size estimates presented here might be overestimated as null results are often unpublished. We want to stress however that this bias is probably reduced by the multivariate reporting of primary studies. Most studies that reported on multiple health outcomes only showed significant findings for one or two among many. Thus, the multivariate nature of primary research in this field allowed us to include many non-significant findings in the present study. Another limitation pertains to the fact that we only included articles in languages mostly spoken in Western countries. As a large body of evidence comes from Asian countries, it could be that primary research was published in languages other than specified in the inclusion criteria. Thus, despite the large and inclusive nature of our study, some studies could have been missed regardless. Another factor that could not be accounted for in our meta-analysis was that an important prerequisite for touch to be beneficial is its perceived pleasantness. The level of pleasantness associated with being touched is modulated by several parameters 34 including cultural acceptability 43 , perceived humanness 44 or a need for touch 45 , which could explain the observed differences for certain moderators, such as human–human versus robot–human interaction. Moreover, the fact that secondary categorical moderators could not be investigated with respect to specific health outcomes, owing to the lack of data points, limits the specificity of our conclusions in this regard. It thus remains unclear whether, for example, a decreased mental health benefit in the absence of skin-to-skin contact is linked mostly to decreased anxiolytic effects, changes in positive/negative affect or something else. Since these health outcomes are however highly correlated 46 , it is likely that such effects are driven by multiple health outcomes. Similarly, it is important to note that our conclusions mainly refer to outcomes measured close to the touch intervention as we did not include long-term outcomes. Finally, it needs to be noted that blinding towards the experimental condition is essentially impossible in touch interventions. Although we compared the touch intervention with other interventions, such as relaxation therapy, as control whenever possible, contributions of placebo effects cannot be ruled out.

In conclusion, we show clear evidence that touch interventions are beneficial across a large number of both physical and mental health outcomes, for both healthy and clinical cohorts, and for all ages. These benefits, while influenced in their magnitude by study cohorts and intervention characteristics, were robustly present, promoting the conclusion that touch interventions can be systematically employed across the population to preserve and improve our health.

Open science practices

All data and code are accessible in the corresponding OSF project 12 . The systematic review was registered on PROSPERO (CRD42022304281) before the start of data collection. We deviated from the pre-registered plan as follows:

Deviation 1: During our initial screening for the systematic review, we were confronted with a large number of potential health outcomes to look at. This observation of multivariate outcomes led us to register an amendment during data collection (but before any effect size or moderator screening). In doing so, we aimed to additionally extract meta-analytic effects for a more quantitative assessment of our review question that can account for multivariate data reporting and dependencies of effects within the same study. Furthermore, as we noted a severe lack of studies with respect to health outcomes for animals during the inclusion assessment for the systematic review, we decided that the meta-analysis would only focus on outcomes that could be meaningfully analysed on the meta-analytic level and therefore only included health outcomes of human participants.

Deviation 2: In the pre-registration, we did not explicitly exclude non-randomized trials. Since an explicit use of non-randomization for group allocation significantly increases the risk of bias, we decided to exclude them a posteriori from data analysis.

Deviation 3: In the pre-registration, we outlined a tertiary moderator level, namely benefits of touch application versus touch reception. This level was ignored since no included study specifically investigated the benefits of touch application by itself.

Deviation 4: In the pre-registration, we suggested using the RoBMA function 47 to provide a Bayesian framework that allows for a more accurate assessment of publication bias beyond small-study bias. Unfortunately, neither multilevel nor multivariate data structures are supported by the RoBMA function, to our knowledge. For this reason, we did not further pursue this analysis, as the hierarchical nature of the data would not be accounted for.

Deviation 5: Beyond the pre-registered inclusion and exclusion criteria, we also excluded dissertations owing to their lack of peer review.

Deviation 6: In the pre-registration, we stated to investigate the impact of sex of the person applying the touch. This moderator was not further analysed, as this information was rarely given and the individuals applying the touch were almost exclusively women (7 males, 24 mixed and 85 females in studies on adults/children; 3 males, 17 mixed and 80 females in studied on newborns).

Deviation 7: The time span of the touch intervention as assessed by subtracting the final day of the intervention from the first day was not investigated further owing to its very high correlation with the number of sessions ( r (461) = 0.81 in the adult meta-analysis, r (145) = 0.84 in the newborn meta-analysis).

Inclusion and exclusion criteria

To be included in the systematic review, studies had to investigate the relationship between at least one health outcome (physical and/or mental) in humans or animals and a touch intervention, include explicit physical touch by another human, animal or object as part of an intervention and include an experimental and control condition/group that are differentiated by touch alone. Of note, as a result of this selection process, no animal-to-animal touch intervention study was included, as they never featured a proper no-touch control. Human touch was always explicit touch by a human (that is, no brushes or other tools), either with or without skin-to-skin contact. Regarding the included health outcomes, we aimed to be as broad as possible but excluded parameters such as neurophysiological responses or pleasantness ratings after touch application as they do not reflect health outcomes. All included studies in the meta-analysis and systematic review 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 , 188 , 189 , 190 , 191 , 192 , 193 , 194 , 195 , 196 , 197 , 198 , 199 , 200 , 201 , 202 , 203 , 204 , 205 , 206 , 207 , 208 , 209 , 210 , 211 , 212 , 213 , 214 , 215 , 216 , 217 , 218 , 219 , 220 , 221 , 222 , 223 , 224 , 225 , 226 , 227 , 228 , 229 , 230 , 231 , 232 , 233 , 234 , 235 , 236 , 237 , 238 , 239 , 240 , 241 , 242 , 243 , 244 , 245 , 246 , 247 , 248 , 249 , 250 , 251 , 252 , 253 , 254 , 255 , 256 , 257 , 258 , 259 , 260 , 261 , 262 , 263 are listed in Supplementary Table 2 . All excluded studies are listed in Supplementary Table 3 , together with a reason for exclusion. We then applied a two-step process: First, we identified all potential health outcomes and extracted qualitative information on those outcomes (for example, direction of effect). Second, we extracted quantitative information from all possible outcomes (for example, effect sizes). The meta-analysis additionally required a between-subjects design (to clearly distinguish touch from no-touch effects and owing to missing information about the correlation between repeated measurements 264 ). Studies that explicitly did not apply a randomized protocol were excluded before further analysis to reduce risk of bias. The full study lists for excluded and included studies can be found in the OSF project 12 in the file ‘Study_lists_final_revised.xlsx’. In terms of the time frame, we conducted an open-start search of studies until 2022 and identified studies conducted between 1965 and 2022.

Data collection

We used Google Scholar, PubMed and Web of Science for our literature search, with no limitations regarding the publication date and using pre-specified search queries (see Supplementary Information for the exact keywords used). All procedures were in accordance with the updated Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines 265 . Articles were assessed in French, Dutch, German or English. The above databases were searched from 2 December 2021 until 1 October 2022. Two independent coders evaluated each paper against the inclusion and exclusion criteria. Inconsistencies between coders were checked and resolved by J.P. and H.H. Studies excluded/included for the review and meta-analysis can be found on the OSF project.

Search queries

We used the following keywords to search the chosen databases. Agents (human versus animal versus object versus robot) and touch outcome (physical versus mental) were searched separately together with keywords searching for touch.

TOUCH: Touch OR Social OR Affective OR Contact OR Tactile interaction OR Hug OR Massage OR Embrace OR Kiss OR Cradling OR Stroking OR Haptic interaction OR tickling

AGENT: Object OR Robot OR human OR animal OR rodent OR primate

MENTAL OUTCOME: Health OR mood OR Depression OR Loneliness OR happiness OR life satisfaction OR Mental Disorder OR well-being OR welfare OR dementia OR psychological OR psychiatric OR anxiety OR Distress

PHYSICAL OUTCOME: Health OR Stress OR Pain OR cardiovascular health OR infection risk OR immune response OR blood pressure OR heart rate

Data extraction and preparation

Data extraction began on 10 October 2022 and was concluded on 25 February 2023. J.P. and H.H. oversaw the data collection process, and checked and resolved all inconsistencies between coders.

Health benefits of touch were always coded by positive summary effects, whereas adverse health effects of touch were represented by negative summary effects. If multiple time points were measured for the same outcome on the same day after a single touch intervention, we extracted the peak effect size (in either the positive or negative direction). If the touch intervention occurred multiple times and health outcomes were assessed for each time point, we extracted data points separately. However, we only extracted immediate effects, as long-term effects not controlled through the experimental conditions could be due to influences other than the initial touch intervention. Measurements assessing long-term effects without explicit touch sessions in the breaks were excluded for the same reason. Common control groups for touch interventions comprised active (for example, relaxation therapy) as well as passive control groups (for example, standard medical care). In the case of multiple control groups, we always contrasted the touch group to the group that most closely matched the touch condition (for example, relaxation therapy was preferred over standard medical care). We extracted information from all moderators listed in the pre-registration (Supplementary Table 4 ). A list of included and excluded health outcomes is presented in Supplementary Table 5 . Authors of studies with possible effects but missing information to calculate those effects were contacted via email and asked to provide the missing data (response rate 35.7%).

After finalizing the list of included studies for the systematic review, we added columns for moderators and the coding schema for our meta-analysis per our updated registration. Then, each study was assessed for its eligibility in the meta-analysis by two independent coders (J.P., H.H., K.F. or F.M.). To this end, all coders followed an a priori specified procedure: First, the PDF was skimmed for possible effects to extract, and the study was excluded if no PDF was available or the study was in a language different from the ones specified in ‘ Data collection ’. Effects from studies that met the inclusion criteria were extracted from all studies listing descriptive values or statistical parameters to calculate effect sizes. A website 266 was used to convert descriptive and statistical values available in the included studies (means and standard deviations/standard errors/confidence intervals, sample sizes, F values, t values, t test P values or frequencies) into Cohen’s d , which were then converted in Hedges’ g . If only P value thresholds were reported (for example, P  < 0.01), we used this, most conservative, value as the P value to calculate the effect size (for example, P  = 0.01). If only the total sample size was given but that number was even and the participants were randomly assigned to each group, we assumed equal sample sizes for each group. If delta change scores (for example, pre- to post-touch intervention) were reported, we used those over post-touch only scores. In case frequencies were 0 when frequency tables were used to determine effect sizes, we used a value of 0.5 as a substitute to calculate the effect (the default setting in the ‘metafor’ function 267 ). From these data, Hedges’ g and its variance could be derived. Effect sizes were always computed between the experimental and the control group.

Statistical analysis and risk of bias assessment

Owing to the lack of identified studies, health benefits to animals were not included as part of the statistical analysis. One meta-analysis was performed for adults, adolescents and children, as outcomes were highly comparable. We refer to this meta-analysis as the adult meta-analysis, as children/adolescent cohorts were only targeted in a minority of studies. A separate meta-analysis was performed for newborns, as their health outcomes differed substantially from any other age group.

Data were analysed using R (version 4.2.2) with the ‘rma.mv’ function from the ‘metafor’ package 267 in a multistep, multivariate and multilevel fashion.

We calculated an overall effect of touch interventions across all studies, cohorts and health outcomes. To account for the hierarchical structure of the data, we used a multilevel structure with random effects at the study, cohort and effects level. Furthermore, we calculated the variance–covariance matrix of all data points to account for the dependencies of measured effects within each individual cohort and study. The variance–covariance matrix was calculated by default with an assumed correlation of effect sizes within each cohort of ρ  = 0.6. As ρ needed to be assumed, sensitivity analyses for all computed effect estimates were conducted using correlations between effects of 0, 0.2, 0.4 and 0.8. The results of these sensitivity analyses can be found in ref. 12 . No conclusion drawn in the present manuscript was altered by changing the level of ρ . The sensitivity analyses, however, showed that higher assumed correlations lead to more conservative effect size estimates (see Supplementary Figs. 19 and 20 for the adult and newborn meta-analyses, respectively), reducing the type I error risk in general 268 . In addition to these procedures, we used robust variance estimation with cluster-robust inference at the cohort level. This step is recommended to more accurately determine the confidence intervals in complex multivariate models 269 . The data distribution was assumed to be normal, but this was not formally tested.

To determine whether individual effects had a strong influence on our results, we calculated Cook’s distance D . Here, a threshold of D  > 0.5 was used to qualify a study as influential 270 . Heterogeneity in the present study was assessed using Cochran’s Q , which determines whether the extracted effect sizes estimate a common population effect size. Although the Q statistic in the ‘rma.mv’ function accounts for the hierarchical nature of the data, we also quantified the heterogeneity estimator σ ² for each random-effects level to provide a comprehensive overview of heterogeneity indicators. These indicators for all models can be found on the OSF project 12 in the Table ‘Model estimates’. To assess small study bias, we visually inspected the funnel plot and used the standard error as a moderator in the overarching meta-analyses.

Before any sub-group analysis, the overall effect size was used as input for power calculations. While such post hoc power calculations might be limited, we believe that a minimum number of effects to be included in subgroup analyses was necessary to allow for meaningful conclusions. Such medium effect sizes would also probably be the minimum effect sizes of interest for researchers as well as clinical practitioners. Power calculation for random-effects models further requires a sample size for each individual effect as well as an approximation of the expected heterogeneity between studies. For the sample size input, we used the median sample size in each of our studies. For heterogeneity, we assumed a value between medium and high levels of heterogeneity ( I ² = 62.5% 271 ), as moderator analyses typically aim at reducing heterogeneity overall. Subgroups were only further investigated if the number of observed effects achieved ~80% power under these circumstances, to allow for a more robust interpretation of the observed effects (see Supplementary Figs. 5 and 6 for the adult and newborn meta-analysis, respectively). In a next step, we investigated all pre-registered moderators for which sufficient power was detected. We first looked at our primary moderators (mental versus physical health) and how the effect sizes systematically varied as a function of our secondary moderators (for example, human–human or human–object touch, duration, skin-to-skin presence, etc.). We always included random slopes to allow for our moderators to vary with the random effects at our clustering variable, which is recommended in multilevel models to reduce false positives 272 . All statistical tests were performed two-sided. Significance of moderators was determined using omnibus F tests. Effect size differences between moderator levels and their confidence intervals were assessed via t tests.

Post hoc t tests were performed comparing mental and physical health benefits within each interacting moderator (for example, mental versus physical health benefits in cancer patients) and mental or physical health benefits across levels of the interacting moderator (for example, mental health benefits in cancer versus pain patients). The post hoc tests were not pre-registered. Data were visualized using forest plots and orchard plots 273 for categorical moderators and scatter plots for continuous moderators.

For a broad overview of prior work and their biases, risk of bias was assessed for all studies included in both meta-analyses and the systematic review. We assessed the risk of bias for the following parameters:

Bias from randomization, including whether a randomization procedure was performed, whether it was a between- or within-participant design and whether there were any baseline differences for demographic or dependent variables.

Sequence bias resulting from a lack of counterbalancing in within-subject designs.

Performance bias resulting from the participants or experiments not being blinded to the experimental conditions.

Attrition bias resulting from different dropout rates between experimental groups.

Note that four studies in the adult meta-analysis did not explicitly mention randomization as part of their protocol. However, since these studies never showed any baseline differences in all relevant variables (see ‘Risk of Bias’ table on the OSF project ) , we assumed that randomization was performed but not mentioned. Sequence bias was of no concern for studies for the meta-analysis since cross-over designs were excluded. It was, however, assessed for studies within the scope of the systematic review. Importantly, performance bias was always high in the adult/children meta-analysis, as blinding of the participants and experimenters to the experimental conditions was not possible owing to the nature of the intervention (touch versus no touch). For studies with newborns and animals, we assessed the performance bias as medium since neither newborns or animals are likely to be aware of being part of an experiment or specific group. An overview of the results is presented in Supplementary Fig. 21 , and the precise assessment for each study can be found on the OSF project 12 in the ‘Risk of Bias’ table.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All data are available via Open Science Framework at https://doi.org/10.17605/OSF.IO/C8RVW (ref. 12 ). Source data are provided with this paper.

Code availability

All code is available via Open Science Framework at https://doi.org/10.17605/OSF.IO/C8RVW (ref. 12 ).

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Acknowledgements

We thank A. Frick and E. Chris for supporting the initial literature search and coding. We also thank A. Dreisoerner, T. Field, S. Koole, C. Kuhn, M. Henricson, L. Frey Law, J. Fraser, M. Cumella Reddan, and J. Stringer, who kindly responded to our data requests and provided additional information or data with respect to single studies. J.P. was supported by the German National Academy of Sciences Leopoldina (LPDS 2021-05). H.H. was supported by the Marietta-Blau scholarship of the Austrian Agency for Education and Internationalisation (OeAD) and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, project ID 422744262 – TRR 289). C.K. received funding from OCENW.XL21.XL21.069 and V.G. from the European Research Council (ERC) under European Union’s Horizon 2020 research and innovation programme, grant ‘HelpUS’ (758703) and from the Dutch Research Council (NWO) grant OCENW.XL21.XL21.069. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Julian Packheiser

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These authors contributed equally: Julian Packheiser, Helena Hartmann.

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Julian Packheiser, Helena Hartmann, Kelly Fredriksen, Valeria Gazzola, Christian Keysers & Frédéric Michon

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J.P. contributed to conceptualization, methodology, formal analysis, investigation, data curation, writing the original draft, review and editing, visualization, supervision and project administration. HH contributed to conceptualization, methodology, formal analysis, investigation, data curation, writing the original draft, review and editing, visualization, supervision and project administration. K.F. contributed to investigation, data curation, and review and editing. C.K. and V.G. contributed to conceptualization, and review and editing. F.M. contributed to conceptualization, methodology, formal analysis, investigation, writing the original draft, and review and editing.

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Supplementary Figs. 1–21 and Tables 1–4.

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List of studies included in and excluded from the meta-analyses/review.

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Source Data Fig. 2

Effect size/error (columns ‘Hedges_g’ and ‘variance’) information for each study/cohort/effect included in the analysis. Source Data Fig. 3 Effect size/error (columns ‘Hedges_g’ and ‘variance’) together with moderator data (column ‘Outcome’) for each study/cohort/effect included in the analysis. Source Data Fig. 4 Effect size/error (columns ‘Hedges_g’ and ‘variance’) together with moderator data (columns ‘dyad_type’ and ‘skin_to_skin’) for each study/cohort/effect included in the analysis. Source Data Fig. 5 Effect size/error (columns ‘Hedges_g’ and ‘variance’) together with moderator data (column ‘touch_type’) for each study/cohort/effect included in the analysis. Source Data Fig. 6 Effect size/error (columns ‘Hedges_g’ and ‘variance’) together with moderator data (column ‘clin_sample’) for each study/cohort/effect included in the analysis. Source Data Fig. 7 Effect size/error (columns ‘Hedges_g’ and ‘variance’) together with moderator data (column ‘familiarity’) for each study/cohort/effect included in the analysis. Source Data Fig. 7 Effect size/error (columns ‘Hedges_g’ and ‘variance’) together with moderator data (columns ‘touch_duration’ and ‘sessions’) for each study/cohort/effect included in the analysis.

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Packheiser, J., Hartmann, H., Fredriksen, K. et al. A systematic review and multivariate meta-analysis of the physical and mental health benefits of touch interventions. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01841-8

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DOI : https://doi.org/10.1038/s41562-024-01841-8

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Chapter 2: Psychological Research

Qualitative and Quantitative Approaches to Research

When designing a study, typically, researchers choose a quantitative or qualitative research design. In some cases, a mixed-method approach, including both quantitative and qualitative measures, may be appropriate. Which approach used will develop on the research question and the type of information sought. Quantitative methods may be better for understanding what is happening, while qualitative methods may be better for understanding the hows and why of a phenomenon.

Video 2.2 Types of Research explains the difference between qualitative and quantitative research.

Quantitative Research

Quantitative research typically starts with a focused research question or hypothesis, collects a small amount of data from each of a large number of individuals, describes the resulting data using statistical techniques, and draws general conclusions about some large population. The strength of quantitative research is its ability to provide precise answers to specific research questions and to draw general conclusions about human behavior; however, it is not nearly as good at  generating   novel and interesting research questions. Likewise, while quantitative research is good at drawing general conclusions about human behavior, it is not nearly as good at providing detailed descriptions of the behavior of particular groups in particular situations. And it is not very good at all at communicating what it is actually like to be a member of a particular group in a particular situation. But the relative weaknesses of quantitative research are the relative strengths of qualitative research.

Qualitative Research

Although quantitative research is by far the most common approach to conducting empirical research in psychology, there is a vital alternative called qualitative research . Qualitative research can help researchers to generate new and interesting research questions and hypotheses. Qualitative researchers generally begin with a less focused research question, collect large amounts of relatively “unfiltered” data from a relatively small number of individuals, and describe their data using nonstatistical techniques. They are usually less concerned with drawing general conclusions about human behavior than with understanding in detail the  experience   of their research participants. Qualitative research can also provide rich and detailed descriptions of human behavior in the real-world contexts in which it occurs. Similarly, qualitative research can convey a sense of what it is actually like to be a member of a particular group or in a particular situation—what qualitative researchers often refer to as the ‘lived experience’ of the research participants.

Mixed-Methods

Given their differences, it may come as no surprise that quantitative and qualitative research do not coexist in complete harmony. Some quantitative researchers criticize that qualitative methods lack objectivity, are challenging to evaluate, and do not allow generalization to other people or situations. At the same time, some qualitative researchers criticize that quantitative methods overlook the richness of behavior and experience, and instead answer simple questions about easily quantifiable variables. However, many researchers from both camps now agree that the two approaches can and should be combined into what has come to be called mixed-methods research (Todd, Nerlich, McKeown, & Clarke, 2004). One approach to combining quantitative and qualitative research is to use qualitative research for hypothesis generation and quantitative research for hypothesis testing. A second approach to combining quantitative and qualitative research is referred to as triangulation. The idea is to use both quantitative and qualitative methods simultaneously to study the same general questions and to compare the results. If the results of the quantitative and qualitative methods converge on the same general conclusion, they reinforce and enrich each other. If the results diverge, then they suggest an interesting new question: Why do the results diverge, and how can they be reconciled?

Video 2.3 What are Qualitative and Quantitative Variables explains the difference between quantitative and qualitative variables that may be used in research.

Becoming Familiar with Research

An excellent way to become more familiar with these research approaches, both quantitative and qualitative, is to look at journal articles, which are written in sections that follow these steps in the scientific process. Most psychological articles and many papers in the social sciences follow the writing guidelines and format dictated by the American Psychological Association (APA). In general, the structure follows: abstract (summary of the article), introduction or literature review, methods explaining how the study was conducted, results of the study, discussion and interpretation of findings, and references.

The aftermath of teenage suicide: a qualitative study of the psychosocial consequences for the supervising family

Per Lindqvist and his colleagues (2008), wanted to learn how the families of teenage suicide victims cope with their loss. They did not have a specific research question or hypothesis, such as, what percentage of family members join suicide support groups? Instead, they wanted to understand the variety of reactions that families had, with a focus on what it is like from  their  perspectives. To do this, they interviewed the families of 10 teenage suicide victims in their homes in rural Sweden. The interviews were relatively unstructured, beginning with a general request for the families to talk about the victim and ending with an invitation to talk about anything else that they wanted to tell the interviewer. One of the most important themes that emerged from these interviews was that even as life returned to “normal,” the families continued to struggle with the question of why their loved one committed suicide. This struggle appeared to be especially difficult for families in which the suicide was most unexpected. This relationship can now be explored using quantitative research. But it is unclear whether this question would have arisen at all without the researchers sitting down with the families and listening to what they themselves wanted to say about their experience.

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Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

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Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

• Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
• Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
• Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
• Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
• Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

• Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
• Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
• Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
• Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
• Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
• Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
• Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

• Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
• Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
• Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
• Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
• Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
• Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
• Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

• Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
• Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
• Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
• Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
• Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
• Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

• To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
• To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
• To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
• To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
• To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

• Description : To provide a detailed and accurate description of a particular phenomenon or population.
• Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
• Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
• Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

• Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
• Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
• Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
• Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
• Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
• Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

• Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
• Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
• Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
• Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
• Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
• Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

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• What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

• What is the demographic makeup of Singapore in 2020?
• How has the average temperature changed globally over the last century?
• Does environmental pollution affect the prevalence of honey bees?
• Does working from home increase productivity for people with long commutes?

Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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Rethinking the Place of Qualitative Methods in Behavior Analysis

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• Published: 12 January 2023
• Volume 46 , pages 185–200, ( 2023 )

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Single-case design research is pervasive and dominant in the field of behavior analysis (BA). It allows for effective application of behavior change technologies in a wide variety of real-world settings. However, as the field has grown, behavioral scholars have suggested incorporating other methods into the investigator’s toolbox to supplement single-case design. To date, the call to expand beyond using only variations of single-case design as the standard for behavior analytic research has gone largely unheard. Given the need for behavior analytic work to be more closely aligned with consumer and stakeholder needs and priorities, along with a proliferation of practitioners and researchers in the field, now is the time to consider the benefits of qualitative research methods for behavior analysts. In particular, in areas of social validity and in exploring diverse applied topics, qualitative methods may help the field of behavior analysis to achieve greater success with documenting the outcomes from behavior change interventions. The present article explores areas where behavior analysis may benefit from utilizing qualitative methods, namely social validity and breadth of topics for study, and provides examples of the value of qualitative research from other fields. A brief outline of qualitative research is provided alongside consideration of the seven dimensions of applied behavior analysis. In situations where single-case design does not offer behavior analysts sufficient methodological opportunity, qualitative research methods could form a powerful addition to the field of behavior analysis .

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The Strengths of Single-Case Design

Since the inception of behavior analysis (BA), single-case design research (e.g., single-case experimental design, subject as own control, or small-n) and BA have been nearly ubiquitous (Kazdin, 2011 ). Aligned with the goal of translating applications of behavioral principles from the laboratory to real world settings, single-case designs have provided behavior analysts a bridge between experimental and applied research. Consistent with the view that individual differences are key to understanding why behavior changes, and that measuring the individual against their own baseline avoids contamination by intersubject variability (Johnston & Pennypacker, 1993 ), single-case design offers a tool for demonstrating control over behavior for a single subject (Smith & Little, 2018 ). An in-depth exploration of single-case design methodology sits outside the scope of this article, but it is widely held that this method of scientific inquiry signals quality and rigor to behavior analysts and is a “gold standard” of behavioral research (Kratochwill et al., 2010 ).

Combined with research designs which allow for repeated demonstration of effects on the dependent variable, single-case studies can be agglomerated to show the efficacy of behavioral intervention for participants with similar characteristics. If a clinical intervention works for many individuals across repeated applications, there is stronger evidence of its utility for use with individuals with similar characteristics. Recent attention has been paid to calculating effect sizes and meta-analyses of single-case research, to further quantify the effectiveness of behavioral interventions (Dowdy et al., 2021 ). Indeed, combining single-case demonstrations into a measure of effect-size has reinforced that many treatments based on behavior analytic basic principles and operations represent evidence-based practices for individuals with autism spectrum disorder (Wong et al., 2015 ), and has promoted increased use of behavior interventions in other domains, including: substance abuse treatment, gerontology, brain injury rehabilitation, pediatric feeding, and occupational safety (Carr & Nosik, 2017 ).

Single-case design research is highly valued by behavioral researchers, clinicians, and behavioral journals (Shadish & Sullivan, 2011 ). The method does what it does well, and is here to stay. But, as behavior analysis has developed over the decades, scholars have identified that reliance on single-case design as the only method of scientific investigation may ultimately limit the growth of the field (Friman, 2021 ). Charting the course of this argument through our own behavioral journals, we see that the limitations of solely using single-case design have been highlighted, and alternatives to this course of action have been signaled. In particular, the utility of qualitative approaches has been considered, but rarely adopted (Schwartz & Olswang, 1996 ). The current article aims to identify and describe areas where single-case design may not sufficiently serve behavior analysts (e.g., in areas of social validity and in the breadth of topics for investigation) and provide examples of how qualitative methods have helped other fields improve their impact in these domains. Here, the authors urge behavior analysts to augment single-case design research methods with qualitative methods where they can assist in answering behavior analytic questions.

The Time is Right for Broader Research Methods

More than ever in the history of behavior analysis, the ability to apply flexible, and varied, research methods is paramount. First, because the field is growing, and fast. With the advent and ready adoption of credentialing for behavior analysts, and more recent moves toward licensure in some countries and the majority of states in the United States, the number of practicing (i.e., applied) behavior analysts has grown exponentially, to approximately 55,000 worldwide at the time of writing (compared with almost 20,000 in 2015, and only 6,900 in 2010; Behavior Analyst Certification Board, n.d. ). Alongside this proliferation of practitioners there has been a burgeoning of training programs for behavior analysts (Deochand & Fuqua, 2016 ), an increase in dedicated behavior analytic faculty members at universities, and journals that publish behavior analytic studies. With greater numbers comes increased responsibility to apply our scientific knowledge to the betterment of society. It is important to note that there is a critical need for behavior analytic scholarship to explore topics that contribute to a better world (Kelly et al., 2019 ), and to relate our “big ideas” to an ever-widening array of social issues (LeBlanc, 2020 ). An expansion of our data collection and analysis toolkit to include qualitative methods may aid behavior analysts in both basic and applied applications of our science, to solving problems that are of high social significance to clients and stakeholders.

Second, recent criticism from some consumers of behavior analytic services has described behavior analysis as dehumanizing and possibly abusive (DeVita-Raeburn, 2016 ; Ram, 2020 ). Centered on the use of ABA within the field of autism, commentary suggests that misinformation may play a role in the shift toward viewing some behavior analytic procedures as harmful to clients and stakeholders (Keenan & Dillenburger, 2018 ). This demonstrates that efforts of disseminating behavior analysis outside the field have, to date, been largely ineffective. Other explanations of the negative sentiment from some consumers of behavior analytic services suggest behavior analysts have missed the mark on evaluating the social acceptability of interventions, particularly in work with autistic individuals (Leaf et al., 2021 ). Growing levels of consumer concern highlight an urgent need for the field to respond flexibly in the area of social validity. Increased methodological diversity could provide behavior analysts with effective ways to canvas stakeholder perspectives and better answer questions such as “what is the experience of being involved in behavior analytic intervention?” from the perspective of consumers and the public.

The Argument for Qualitative Methods

Qualitative methods offer strategies to support a shift toward behavior analytic scholarship that is more responsive to consumer needs and preferences. By providing a methodological paradigm that prioritizes gathering consumer and stakeholder experiences, perspectives, and viewpoints, qualitative methods afford behavior analysts a complementary research tool that can help to solve particular research problems for which single-case designs are not best suited (Ferguson, 1993 ).

Described as a paradigm that is focused on interpreting meaning and identifying patterns using “words as data” (Braun & Clarke, 2006 ), qualitative methods allow researchers to examine contextual factors in complex situations, where all the relevant variables cannot easily be teased out (Schwartz & Olswang, 1996 ). Taking a nonpositivist position (i.e., that meaning is constructed and there is no one “correct” version of reality) qualitative methodologies provide data analysis procedures that are: inductive (e.g., generated by participants rather than researcher hypotheses), constructed (e.g., a product of environment and context), and complicated (e.g., to precisely define, replicate, or control experimentally).

Distinct from single-case design in the level of empirical investigation, degree of control over variables, type of research products, and techniques of analysis; qualitative research permits the asking and answering of questions with a focus on vocal or textual (i.e., verbal) reports and descriptive environmental variables (Čolić et al., 2021 ). Although qualitative research has vastly different aims and methods to single-case research, it does have congruence with the seven dimensions of applied behavior analysis (Baer et al., 1968 ), and consideration of the specific ways in which qualitative research aligns with the seven dimensions warrants further exploration.

Qualitative methods prioritize measurement of behavior in the form of verbal report. Although a departure from the quantitative measures routinely used by behavior analysts (primarily: discrete and observable responses), they are no less behavioral in focus. With growing attention paid to nonequivalence relational learning and relational frame theory (e.g., acceptance and commitment therapy; Cihon et al., 2021 ; Tarbox et al., 2020 ) verbal report as data is becoming increasingly recognized as critical information to guide the development of ABA interventions that are appropriate and meaningful. Accepting that verbal report provides insight into understanding humans in context and measuring socially important problems (Baer et al., 1987 ), qualitative methods can both satisfy the behavioral dimension of our applied science and offer techniques for canvasing meaningful areas for application of behavior analytic principles and operations.

Both single-case design and qualitative methods value deep analysis of a single participant as their own control, informing the analytic dimension of ABA. Recognizing the role of context on individual performance, both approaches aim to understand the individual experience, albeit using vastly different data to detect patterns. Both methods apply a cautious approach to generalization: where qualitative research avoids “straying too far from the data” (Sullivan & Forrester, 2018 ) when describing phenomena and processes that maintain those phenomena, single-case design uses replication or “successive study of additional cases” (Schwartz et al., 1995 , p. 96) to draw parallels across participants with similar characteristics, avoiding group or agglomerate data. An understanding that data from an individual subject can sometimes tell us more than aggregated outcome measures closely aligns qualitative and single-case research.

Both methodologies are grounded in applied questions: what does this mean for the person? How does the intervention affect the behavior of the individual? Both aim to discover something about socially important phenomena. Identification of what is valuable, acceptable, and practical is a goal common to single-case and qualitative research with human participants. Taken further, qualitative research is often concerned with understanding problems to be addressed, as conceptualized through the eyes of those who experience the problems (Leko, 2014 ). Qualitative research offers behavior analysts a way to move beyond a focus on the priorities of the behavior analyst, to those valued by and valid for consumers. Both methodologies are interested in processes rather than products (albeit with a different level of focus on replicability), investigating why behavior occurs in the manner in which it is observed or recorded (Schwartz et al., 1995 ). In these ways, qualitative research satisfies the applied dimension of behavior analysis.

Qualitative methodologies have advanced in recent years, giving researchers tools that better fit research aims. Past applications of qualitative research have been described as fraught with difficulty and necessary compromise (Smith, 2015 ). Approaches with strong theoretical or epistemological leanings (such as grounded theory, narrative theory) rarely translated to the aims of psychological or health research. As a result, qualitative methods were applied without comprehensive analysis, or due consideration to theory, leading to what has been described as a “watering down” of research (Thorne, 2016 , p. 39). However, shifts in qualitative research have seen a wealth of new methodological approaches developed and refined, supporting examination of questions relevant to behavior analysts without reliance on theory generation or specific epistemological leanings. It is equally true that the growth of qualitative research has afforded increased attention to issues of quality and rigor (Smith, 2015 ). Tools for assessing the quality of qualitative research have trailed behind other traditions (e.g., quantitative, large-n research; Morse et al., 2002 ). However, current development of protocols and criteria for evaluating the rigor of qualitative research provides confidence to those who use these methods in their scientific inquiry. For behavior analysts, with a focus on interobserver agreement, treatment fidelity, and other measures of validity (Kazdin, 2011 ), a shift toward robust assessment of quality provides reassurance that qualitative methods can meet the technological dimension of behavior analysis.

Qualitative research holds as a basic tenet the generation of new findings, and the sharing of findings with interested communities (Braun & Clarke, 2006 ). This aligns with the behavior analytic goal of generality or generalizability. The ability to share research in ways that will promote, maintain, and appropriately expand the impacts of treatment, has long been at the forefront of behavior analytic research (Baer et al., 1987 ). By using methods preferred and understood by related disciplines (e.g., nursing, social work, occupational therapy) behavior analysts ensure their findings will “appear in a wide variety of possible environments” (Baer et al., 1968 , p. 96) and persist over time. Qualitative methods do not utilize the jargon endemic to ABA, which has been perceived negatively by those outside the field (Critchfield & Doepke, 2018 ), removing the barrier of translating what behavior analysts know into forms that others can understand. Adopting qualitative methods in appropriate situations, behavior analysts can directly show how or why behavioral interventions “fit” within schools, health settings, and communities (Schwartz et al., 1995 ), helping to reach broader audiences and improve the application of effective interventions in natural environments.

Given the growth of the field of behavior analysis over the past decades, and developments in qualitative methodologies during this time, it seems important for contemporary behavior analysts to consider how qualitative methods could be applied to empirical questions that are critical to the success of behavior analysis. In particular, increased adoption of qualitative methods where they are the best “fit” for the question, may aid in progressing a socially valid science that can readily investigate a wide variety of topics of interest and relevance to society today (Heward et al., 2022 ).

Where Qualitative Methods Could Advance BA: Social Validity

From the beginning, behavior analysts have been concerned with establishing, and measuring, social validity (the degree to which goals, procedures, and effects of behavior analytic input are socially meaningful and relevant to consumers; Kazdin, 1977 ). Yet, the area of social validity has been fraught with complications in accurate measurement and empirical evaluation (Carr et al., 1999 ; Ferguson et al., 2018 ).

In the seminal article “Social Validity: The Case for Subjective Measurement or How Applied Behavior Analysis is Finding Its Heart,” Wolf ( 1978 ) posited that social validity created a tension for behavior analysts at the inception of the field because it lent itself to subjective measures rather than tightly defined and controlled experimentation. Wolf identified that variables such as the perceived relevance and usefulness of behavioral intervention by consumers were not amenable to objective definition and measurement, and consequently could not be assessed using single-case design. Rather than discount social validity as subjective and not relevant to ABA, Wolf argued that “if we aspire to social importance, then we must develop systems that allow our consumers to provide us feedback about how our applications relate to their values, to their reinforcers” (p. 213). Even at that early stage, supplementation of traditional behavior analytic measures was proposed, to afford behavior analysts ways “to approach the specific consumer or representatives of the relevant community, and through interviews or ratings, determine much more precisely what the socially significant problems are” (p. 209).

More than a decade later, Schwartz and Baer ( 1991 ) identified that, despite widespread agreement on the importance of social validity in behavior analysis, methods for accurately and effectively measuring social validity through research were still lacking. Revisiting the mismatch between traditional behavior analytic procedures for measuring social validity, they suggested that single-case design does not extend to a comprehensive understanding of the field’s social significance or social acceptability, nor does it allow behavior analysts to research how ABA is perceived in wider society. In the absence of strong research methods, they described behavior analysts’ application of “short, simple, bland, undemanding 7-point scales” (p. 192), as biased towards favorable report and thus “socially invalid.” They argued instead that social validity research should use well-designed indirect measures (including self-report using Likert-type surveys) to assess the viability of an intervention to the consumers and stakeholders. They urged behavior analysts to canvas social validity more widely; by asking communities around consumers (as well as consumers themselves) if an intervention is meaningful, appropriate, acceptable and preferred. Explicit in Schwartz and Baer’s argument is the suggestion that, for questions of social validity, single-case designs do not provide all the answers.

Further to this, Schwartz et al. ( 1995 ) advocated the use of qualitative methodologies in behavior analytic inquiries, particularly to address questions of social acceptability. Presenting a case study of mixed methods research in inclusive education, they demonstrated that “stronger linkages between qualitative and behavior analytic research methods” provided an avenue to making “behavioral research more responsive to the values and goals of consumers” (p. 97). They posited that qualitative research could help behavior analysts identify the priorities of stakeholders and communities (e.g., parents and school boards), to formalize understanding of relevant variables that can then be empirically tested. Through qualitative methods, acceptability variables would be elucidated before designing an intervention, putting social importance at the forefront of behavior analysis. This inductive approach, Schwartz et al. ( 1995 ) argued, could allow recipients of services to set the agenda, rather than relying on what the analyst sees as most important to prioritize for treatment. In this way, qualitative methods provide behavior analysts with tools for avoiding blindness to ideas that are visible to consumers, but not obvious to behavior analysts.

As well as better assessing the social validity of interventions, Schwartz and Olswang ( 1996 ) suggested qualitative methods could result in data that are more meaningful to consumers, and answer questions that interest consumers in ways consumers can understand. They reflected that the audience consuming the research outputs must be paramount for behavior analysts as they decide which research methods will lead to compelling findings. It follows that qualitative methods (using words as data) may be more convincing for stakeholders of behavior analytic interventions, and a better way to explore acceptability.

Building from Wolf ( 1978 ) and Schwartz and Baer ( 1991 ) behavior analysts in the 1990s articulated the call for expanded methodologies, proposing indirect quantitative (questionnaires, surveys; Fawcett, 1991 ; Kennedy, 1992 ) and qualitative approaches, to study what is important to consumers (goals; Schwartz, 1991 ), and how consumers perceive interventions (process and effects; Finney, 1991 ). Use of research methods “other” than single-case designs were described as potential options to allow the field to learn what is meaningful and valuable for society, rather than exclusively surveying direct recipients (Ferguson, 1993 , Schwartz, 1991 ).

The intervening years saw the application of qualitative methods to social validity in a smattering of single-case design studies, primarily at the intersection of behavioral and special education literatures. Leko ( 2014 ) evaluated the acceptability of a phonics-based reading program using teacher interviews coupled with direct observations. Leko noted that qualitative methods in the study offered a different perspective to the complicated picture of treatment acceptability for teachers, arguing qualitative approaches can be “differently informative yet equally valuable” (p. 285) in the pursuit of understanding social validity for clients and stakeholders. In an attempt to measure teacher perspectives of a self-monitoring intervention for students diagnosed with ADHD, Vogelgesang et al. ( 2016 ) used questionnaire, semi-structured interview, and journaling methods, with a teacher before and after implementation. Concluding that qualitative data offered insight into “complications” with the intervention that questionnaires alone missed; the authors emphasized the value of mixed-methods analysis of social validity. Nicolson et al. ( 2020 ) echoed the call for increased flexibility in assessing social validity. They contend that “having more tools that are designed to examine different aspects of social validity is a necessary step in the evolution of paying more attention to this somewhat neglected dimension of ABA” (p. 760). This reiteration of the need to use other methodologies to examine social acceptability, however, sits alongside the relative paucity of published literature using qualitative or other quantitative methods in behavior analytic journals. There have been a few promising applications of qualitative methods in measuring social validity, but the expansion of methodologies for this purpose has lacked wide adoption by behavior analysts (Snodgrass et al., 2021 ), and the repeated calls for BA to broaden research methods in studying social validity (Carr et al., 1999 ; Ferguson et al., 2018 ) have largely gone unanswered.

Where Qualitative Methods Could Advance BA: Diverse Topics

Although ABA is often inaccurately described as synonymous with the study of empirically validated approaches for populations with autism, developmental disabilities, and additional learning needs (Axelrod et al., 2012 ; Normand & Kohn, 2013 ), the field has long been interested in other areas which pertain to the behavior of humans in context—including addiction (Silverman et al., 2019 ), obesity (Wilfley et al., 2018 ), gun safety (Chan & Kirby, 2021 ), and seatbelt use (Berry & Geller, 1991 ). Behavior analysts have become increasingly focused on how the field can generate and share findings about human behavior with those who could directly benefit, across settings and situations (Critchfield & Reed, 2017 ; Freedman, 2015 ; Heward et al., 2022 ). When considering the breadth of socially important topics, and the need for BA to “have a say in resolving the most socially relevant problems of our time, such as war, murder . . . disease, public education, and so on” (Vollmer, 2011 , p. 34), arguments are emerging that single-case designs could be just one of many methodological tools available to behavioral researchers interested in an ever-widening array of social contexts, and that qualitative investigations may allow behavior analysts to broaden the type of research questions they ask of ever-expanding domains of interest.

In exploring the impact of Baer et al.’s ( 1968 ) “seven dimensions” on the current state of behavioral research, Critchfield and Reed ( 2017 ) contend that topics that do not lend themselves to precise definition and measurement (i.e., inappropriate under a single-case framework) have been relatively understudied by behavior analysts. Described as “fuzzy concepts”: issues that are socially important and valuable for advancing the field, but poorly defined or with variables not yet fully understood, these domains have largely been underexplored by behavior analysts. This is in part because research in these murky areas faces barriers to publication for not equally demonstrating all seven dimensions of behavior analysis (thought to represent an “overly strict criterion”; Critchfield & Reed, 2017 ). The reliance on single-case design as the ideal research standard, and the only method which satisfies the “analytic” criteria:

undersells the potential of behavior analysis research to shed light on a wide variety of social problems, discourages interest in problems that do not readily fit into the framework, and supports a too narrow conception of what belongs in applied behavior analysis journals. (Critchfield & Reed, 2017 , p. 151)

In their analysis, Critchfield and Reed remind BA researchers of their responsibility to attend to what Baer et al. ( 1968 ) call “behaviors which are socially important, rather than convenient for study” (p. 92). Far from ignoring socially important topics, analysts should instead apply other research methods to canvas these topics and move toward definition, measurement, and manipulation of key variables in later analysis. In areas where behavior analysis has yet to fully outline the relevant variables (or where variables are multiple and contradictory) other forms of scientific inquiry, such as description of context through qualitative methods, might offer a way forward. Not only do qualitative methods provide novel ways to conceptualize, design, and conduct research in important areas, they may also support behavior analysts in what Kelly et al. ( 2019 ) describe as the “responsibility of every behavior analyst to arrange for contingencies to assure its survival” (p. 449). That is, we need to use a diverse platform of research methods to scope interesting areas of study, and to share newly generated findings in forms consumers are comfortable with, understand, and value (Critchfield & Farmer-Dougan, 2014 ).

Nicolson et al. ( 2020 ) describe the over selectivity of single-case design in ABA literature, which has shaped the field such that other possible methods of inquiry are often not considered by researchers. In addressing the question of what should be done when single-case designs cannot answer all the applied questions behavior analysts, and the wider world, are interested in, Nicolson et al. ( 2020 ) suggest that when questions of interest center around client experiences, consumer perspectives and layperson understanding, qualitative research methods are a more cogent fit. Qualitative methods allow exploration of topics that are important to consumers, but ill-defined from an experimental ABA lens, such as “progress,” “inclusion,” and “relationship.”

The contention that space exists for broadening research methods to investigate “fuzzy concepts” has coincided with a groundswell of public interest in cultural and political movements (e.g., Black Lives Matter, Me Too) and compelling societal issues (e.g., terrorism, pandemic management, and public health initiatives; see Conine et al., 2022 ; Gravina et al., 2020 ; and Matsuda et al., 2020 , for commentary). These are arguably critical areas for behavior analytic investigation but are oftentimes a poor match for research using single-case design, at least in the initial stages of inquiry. Applied behavior analysis, with a unique perspective on socially important human behavior, has a lot to offer in these areas (Dixon et al., 2018 ; Heward et al., 2022 ), particularly if there is a willingness to engage with other research perspectives. It is clear that societally relevant research questions that are important to communities within which behavior analysts live and work, do not always lend themselves to single-case design methodology, at least not until more of the variables involved are clear (Critchfield & Reed, 2017 ; Saini & Vance, 2020 ). Qualitative research could yield the subjective and rich information necessary to identify variables required for later empirical research in these domains, using single-case or group design.

How Qualitative Methods Can Help: Expanding Climate Change Research

To exemplify how qualitative methodology can add to diverse avenues of study, the case of qualitative climate change research provides valuable insights. A growing field (owing to the increasingly evident climate change emergency; World Meteorological Organization, 2022 ) and one historically dominated by quantitative studies, climate change researchers are beginning to incorporate qualitative techniques to answer questions about people’s motivations and reflections on their own behavior, as these relate to actions impacting the environment. In a study on arctic sea-level change in a Norwegian town, Bercht ( 2021 ) used an interpretive paradigm and interview data to understand the perspectives and behavior of locals involved in the fisheries industry as they related to sea level change. Bercht identified an apparent contradiction, when participants talked about concerns of climate change but did not adjust their behavior at either home or work; reflecting how each participant positioned themselves within the climate change crisis facing the town. This research illustrated how the varying ways people see themselves within a problem has implications for how individual, or group-level, action plans could be successfully developed and communicated. Bercht argued that “qualitative approaches urgently matter in climate and ocean change research because climate and ocean solutions are conditional on individual and group behaviors . . . institutional settings and governance structures that are impossible to fully understand in solely quantitative studies” (p. 11). In other applications of qualitative methods to the climate change field, McNamara et al. ( 2020 ) used interview and focus group data to explore the impact and utility of various community-based climate change initiatives in the Pacific Islands, identifying tangible approaches and actions that promote engagement of locals in designing research, collecting data, and developing climate change interventions. In earlier research, Rao et al. ( 2019 ) used a qualitative comparative analysis methodology to explore the role of women’s agency as a variable affecting the effectiveness of climate response programs in Asia and Africa. Without challenging the dominant position of quantitative research in climate change science, current scholars have applied qualitative approaches to better understand the context around the big “problems at hand” (Bercht, 2021 ) and generate data about experiences and perspectives which could better inform the development of effective climate change strategy.

In climate change research, qualitative methods are gaining prominence alongside the traditional and respected quantitative methods. Qualitative approaches give scholars novel tools for exploring the wider context around an issue, event, or situation, such that practical change can be implemented and evaluated. If applied to the field of behavior analysis, qualitative methods could offer similar benefits; allowing the field to study behavior within increasingly novel contexts, and further elucidate the relevant variables in these contexts, to which the science of behavior could be effective in creating desired change. Taken further, qualitative approaches could be woven into mixed methods investigations, again expanding the scope and applicability of behavior analytic research. Broadening research methods could allow behavior analysts to make greater inroads into interesting, socially relevant, and important domains.

For the (Un)Convinced: Where to Next?

The current article highlights the value of applying qualitative methods and may compel behavior analysts to adopt qualitative tools, where these afford a convincing methodology for addressing empirical questions. However, the barriers to routine adoption of qualitative research are many and will require careful problem-solving by the behavior analytic community.

Namely, there exists a bias toward funding provision for quantitative studies (in particular randomized control trials and, more recently, single-case designs), where grant funding agencies are more readily convinced by studies that propose well-planned experimental trials or robust single-case designs than qualitatively derived proposals (Morse, 2003 ). This barrier is not unique to behavior analysis, but it does pose a challenge when calling behavior analysts to use qualitative approaches. Bourgeault ( 2012 ) suggests that both the nature of “traditional” qualitative studies (e.g., small in scale, requiring few resources) and myths around the necessary and sufficient conditions for a high-quality qualitative study have resulted in relatively fewer grant applications using qualitative methods. In turn, funding bodies are less familiar with how to evaluate qualitative studies and less confident about potential outcomes from awarding grants to studies using qualitative methods. Although the contextual factors involved in funding provisions are numerous and complicated (and analysis of these variables is warranted), one suggestion for overcoming this hurdle is to coach both applicants and funders in what constitutes a credible qualitative study and appropriate funding application (see Carey & Swanson, 2003 , for elaboration).

Related to this, the publication trends evident in behavioral journals (Nicolson et al., 2020 ) present a contingency whereby behavior analysts who apply qualitative methods in their scholarship may be thwarted by a reduced likelihood of their research being published in reputable, peer reviewed behavioral journals. Although arguably a key variable affecting the adoption of qualitative research methods, this problem is not insurmountable. Recent special editions within ABA journals and editorial calls for diverse scholarship (LeBlanc, 2020 ) demonstrate that the field is open to, and interested in, a more varied range of perspectives, topics, and research methods in the published literature. It seems that the appropriate motivating operations are present to compel behavior analysts to incorporate qualitative methods into studies published in behavioral journals, when this method is better suited to addressing the research question than single-case design.

Achieving the goal of integrating qualitative methods into behavior science will require that behavior analysts receive rigorous training in qualitative research approaches. Behavior analysts are relatively underinformed about the characteristics of high-quality qualitative methods, what they add to a potential research agenda, and when they should be utilized as the best methodological tool to address the research question or referral concern (Friman, 2021 ). A recent decision from the Behavior Analyst Certification Board (BACB) to move away from regarding Task Lists as “all-encompassing lists of critical behavior-analytic content” (Behavior Analyst Certification Board, 2022 ) toward a suggestion of essential skills for behavior analysts (related to, but distinct from, testing content for accreditation), signals that opportunities to widen the methodological training behavior analysts receive in other research approaches are growing. Enthusiastic behavior analysts could work to develop and publish tutorials in qualitative methods, trainings in using qualitative research tools, and broader conceptualizations of research methodology in verified course sequences; all of which would go some way to developing the skills of behavior analysts in applying qualitative approaches.

As the field of behavior analysis has grown over the past 70 years, attention has been paid to the role of single-case design in defining how, and what, behavior analysts study. Time and again behavior analytic scholars have called for the expansion of research methodologies that behavior analysts can apply to empirical problems. Progress across domains of social validity and diversity of research topics leads the field of behavior analysis to an important conclusion: it is time to expand beyond single-case design research to promote our science. Growth in the field, coupled with rising opposition to ABA in the mainstream, behooves behavior analysts to adopt a range of research methods to best answer a variety of research questions, and to apply the best approach to answer the question, rather than adjusting the topic under study to fit within the more familiar single-case research designs.

In the realm of social validity, where stakeholder perspectives are the key variable of interest, qualitative methods prove a useful addition. Qualitative research provides a methodology to gauge what is important and useful to consumers, and society at large. This could lend credibility to behavior analytic work, and allow behavior analysts to identify when the goals, processes, or products of an intervention could miss the mark for those engaging with the intervention.

Qualitative approaches offer behavior analysts a broader scope to explore interesting and socially relevant topics to which single-case designs do not fit; perhaps because topics require clarification before empirical analyses can be effectively applied, or because key features, phenomenon, and variables are better understood using an inductive, rather than deductive, frame. Inclusion of qualitative research in fields such as climate science demonstrate how qualitative methods can provide behavior analysis another way to increase the reach and scope of our scholarship, to the ultimate benefit of society.

Qualitative methods have evolved rapidly in recent years. They are no longer tied to inflexible philosophical positions or confounded by questionable rigor. Qualitative studies can answer questions that are meaningful to society and hence behavior analysts, with robust and reliable technologies.

This reading of behavioral literature leads us to suggest that the field of behavior analysis could broaden its definition of high quality research to include different methods that answer different, but no less important, questions. Now is the time to expand our horizons and consider a widened scope of research methodologies. In as much as behavior analysts are willing to let the context around behavior dictate selected assessment and intervention approaches, the field should let the context around research questions from the clients’ and the stakeholders’ perspectives drive the selection of research methods, qualitative or quantitative.

This call to consider qualitative methods is not without its challenges. Namely, the apparent lack of funding streams or coherent publication pathways, as well as relative unfamiliarity with qualitative research methods within the behavior analytic field. If now is the time for increased adoption of qualitative methods—and the current authors argue it is—practical tutorials that allow behavior analysts to acquire and become fluent in best practices for using qualitative methods may be an important next step.

Change history

23 february 2023.

Missing Open Access funding information has been added in the Funding Note.

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Burney, V., Arnold-Saritepe, A. & McCann, C.M. Rethinking the Place of Qualitative Methods in Behavior Analysis. Perspect Behav Sci 46 , 185–200 (2023). https://doi.org/10.1007/s40614-022-00362-x

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Rethinking the Place of Qualitative Methods in Behavior Analysis

Victoria burney.

University of Auckland, Private Bag 92019, Auckland, 1142 New Zealand

Angela Arnold-Saritepe

Clare m. mccann.

Single-case design research is pervasive and dominant in the field of behavior analysis (BA). It allows for effective application of behavior change technologies in a wide variety of real-world settings. However, as the field has grown, behavioral scholars have suggested incorporating other methods into the investigator’s toolbox to supplement single-case design. To date, the call to expand beyond using only variations of single-case design as the standard for behavior analytic research has gone largely unheard. Given the need for behavior analytic work to be more closely aligned with consumer and stakeholder needs and priorities, along with a proliferation of practitioners and researchers in the field, now is the time to consider the benefits of qualitative research methods for behavior analysts. In particular, in areas of social validity and in exploring diverse applied topics, qualitative methods may help the field of behavior analysis to achieve greater success with documenting the outcomes from behavior change interventions. The present article explores areas where behavior analysis may benefit from utilizing qualitative methods, namely social validity and breadth of topics for study, and provides examples of the value of qualitative research from other fields. A brief outline of qualitative research is provided alongside consideration of the seven dimensions of applied behavior analysis. In situations where single-case design does not offer behavior analysts sufficient methodological opportunity, qualitative research methods could form a powerful addition to the field of behavior analysis .

The Strengths of Single-Case Design

Since the inception of behavior analysis (BA), single-case design research (e.g., single-case experimental design, subject as own control, or small-n) and BA have been nearly ubiquitous (Kazdin, 2011 ). Aligned with the goal of translating applications of behavioral principles from the laboratory to real world settings, single-case designs have provided behavior analysts a bridge between experimental and applied research. Consistent with the view that individual differences are key to understanding why behavior changes, and that measuring the individual against their own baseline avoids contamination by intersubject variability (Johnston & Pennypacker, 1993 ), single-case design offers a tool for demonstrating control over behavior for a single subject (Smith & Little, 2018 ). An in-depth exploration of single-case design methodology sits outside the scope of this article, but it is widely held that this method of scientific inquiry signals quality and rigor to behavior analysts and is a “gold standard” of behavioral research (Kratochwill et al., 2010 ).

Combined with research designs which allow for repeated demonstration of effects on the dependent variable, single-case studies can be agglomerated to show the efficacy of behavioral intervention for participants with similar characteristics. If a clinical intervention works for many individuals across repeated applications, there is stronger evidence of its utility for use with individuals with similar characteristics. Recent attention has been paid to calculating effect sizes and meta-analyses of single-case research, to further quantify the effectiveness of behavioral interventions (Dowdy et al., 2021 ). Indeed, combining single-case demonstrations into a measure of effect-size has reinforced that many treatments based on behavior analytic basic principles and operations represent evidence-based practices for individuals with autism spectrum disorder (Wong et al., 2015 ), and has promoted increased use of behavior interventions in other domains, including: substance abuse treatment, gerontology, brain injury rehabilitation, pediatric feeding, and occupational safety (Carr & Nosik, 2017 ).

Single-case design research is highly valued by behavioral researchers, clinicians, and behavioral journals (Shadish & Sullivan, 2011 ). The method does what it does well, and is here to stay. But, as behavior analysis has developed over the decades, scholars have identified that reliance on single-case design as the only method of scientific investigation may ultimately limit the growth of the field (Friman, 2021 ). Charting the course of this argument through our own behavioral journals, we see that the limitations of solely using single-case design have been highlighted, and alternatives to this course of action have been signaled. In particular, the utility of qualitative approaches has been considered, but rarely adopted (Schwartz & Olswang, 1996 ). The current article aims to identify and describe areas where single-case design may not sufficiently serve behavior analysts (e.g., in areas of social validity and in the breadth of topics for investigation) and provide examples of how qualitative methods have helped other fields improve their impact in these domains. Here, the authors urge behavior analysts to augment single-case design research methods with qualitative methods where they can assist in answering behavior analytic questions.

The Time is Right for Broader Research Methods

More than ever in the history of behavior analysis, the ability to apply flexible, and varied, research methods is paramount. First, because the field is growing, and fast. With the advent and ready adoption of credentialing for behavior analysts, and more recent moves toward licensure in some countries and the majority of states in the United States, the number of practicing (i.e., applied) behavior analysts has grown exponentially, to approximately 55,000 worldwide at the time of writing (compared with almost 20,000 in 2015, and only 6,900 in 2010; Behavior Analyst Certification Board, n.d. ). Alongside this proliferation of practitioners there has been a burgeoning of training programs for behavior analysts (Deochand & Fuqua, 2016 ), an increase in dedicated behavior analytic faculty members at universities, and journals that publish behavior analytic studies. With greater numbers comes increased responsibility to apply our scientific knowledge to the betterment of society. It is important to note that there is a critical need for behavior analytic scholarship to explore topics that contribute to a better world (Kelly et al., 2019 ), and to relate our “big ideas” to an ever-widening array of social issues (LeBlanc, 2020 ). An expansion of our data collection and analysis toolkit to include qualitative methods may aid behavior analysts in both basic and applied applications of our science, to solving problems that are of high social significance to clients and stakeholders.

Second, recent criticism from some consumers of behavior analytic services has described behavior analysis as dehumanizing and possibly abusive (DeVita-Raeburn, 2016 ; Ram, 2020 ). Centered on the use of ABA within the field of autism, commentary suggests that misinformation may play a role in the shift toward viewing some behavior analytic procedures as harmful to clients and stakeholders (Keenan & Dillenburger, 2018 ). This demonstrates that efforts of disseminating behavior analysis outside the field have, to date, been largely ineffective. Other explanations of the negative sentiment from some consumers of behavior analytic services suggest behavior analysts have missed the mark on evaluating the social acceptability of interventions, particularly in work with autistic individuals (Leaf et al., 2021 ). Growing levels of consumer concern highlight an urgent need for the field to respond flexibly in the area of social validity. Increased methodological diversity could provide behavior analysts with effective ways to canvas stakeholder perspectives and better answer questions such as “what is the experience of being involved in behavior analytic intervention?” from the perspective of consumers and the public.

The Argument for Qualitative Methods

Qualitative methods offer strategies to support a shift toward behavior analytic scholarship that is more responsive to consumer needs and preferences. By providing a methodological paradigm that prioritizes gathering consumer and stakeholder experiences, perspectives, and viewpoints, qualitative methods afford behavior analysts a complementary research tool that can help to solve particular research problems for which single-case designs are not best suited (Ferguson, 1993 ).

Described as a paradigm that is focused on interpreting meaning and identifying patterns using “words as data” (Braun & Clarke, 2006 ), qualitative methods allow researchers to examine contextual factors in complex situations, where all the relevant variables cannot easily be teased out (Schwartz & Olswang, 1996 ). Taking a nonpositivist position (i.e., that meaning is constructed and there is no one “correct” version of reality) qualitative methodologies provide data analysis procedures that are: inductive (e.g., generated by participants rather than researcher hypotheses), constructed (e.g., a product of environment and context), and complicated (e.g., to precisely define, replicate, or control experimentally).

Distinct from single-case design in the level of empirical investigation, degree of control over variables, type of research products, and techniques of analysis; qualitative research permits the asking and answering of questions with a focus on vocal or textual (i.e., verbal) reports and descriptive environmental variables (Čolić et al., 2021 ). Although qualitative research has vastly different aims and methods to single-case research, it does have congruence with the seven dimensions of applied behavior analysis (Baer et al., 1968 ), and consideration of the specific ways in which qualitative research aligns with the seven dimensions warrants further exploration.

Qualitative methods prioritize measurement of behavior in the form of verbal report. Although a departure from the quantitative measures routinely used by behavior analysts (primarily: discrete and observable responses), they are no less behavioral in focus. With growing attention paid to nonequivalence relational learning and relational frame theory (e.g., acceptance and commitment therapy; Cihon et al., 2021 ; Tarbox et al., 2020 ) verbal report as data is becoming increasingly recognized as critical information to guide the development of ABA interventions that are appropriate and meaningful. Accepting that verbal report provides insight into understanding humans in context and measuring socially important problems (Baer et al., 1987 ), qualitative methods can both satisfy the behavioral dimension of our applied science and offer techniques for canvasing meaningful areas for application of behavior analytic principles and operations.

Both single-case design and qualitative methods value deep analysis of a single participant as their own control, informing the analytic dimension of ABA. Recognizing the role of context on individual performance, both approaches aim to understand the individual experience, albeit using vastly different data to detect patterns. Both methods apply a cautious approach to generalization: where qualitative research avoids “straying too far from the data” (Sullivan & Forrester, 2018 ) when describing phenomena and processes that maintain those phenomena, single-case design uses replication or “successive study of additional cases” (Schwartz et al., 1995 , p. 96) to draw parallels across participants with similar characteristics, avoiding group or agglomerate data. An understanding that data from an individual subject can sometimes tell us more than aggregated outcome measures closely aligns qualitative and single-case research.

Both methodologies are grounded in applied questions: what does this mean for the person? How does the intervention affect the behavior of the individual? Both aim to discover something about socially important phenomena. Identification of what is valuable, acceptable, and practical is a goal common to single-case and qualitative research with human participants. Taken further, qualitative research is often concerned with understanding problems to be addressed, as conceptualized through the eyes of those who experience the problems (Leko, 2014 ). Qualitative research offers behavior analysts a way to move beyond a focus on the priorities of the behavior analyst, to those valued by and valid for consumers. Both methodologies are interested in processes rather than products (albeit with a different level of focus on replicability), investigating why behavior occurs in the manner in which it is observed or recorded (Schwartz et al., 1995 ). In these ways, qualitative research satisfies the applied dimension of behavior analysis.

Qualitative methodologies have advanced in recent years, giving researchers tools that better fit research aims. Past applications of qualitative research have been described as fraught with difficulty and necessary compromise (Smith, 2015 ). Approaches with strong theoretical or epistemological leanings (such as grounded theory, narrative theory) rarely translated to the aims of psychological or health research. As a result, qualitative methods were applied without comprehensive analysis, or due consideration to theory, leading to what has been described as a “watering down” of research (Thorne, 2016 , p. 39). However, shifts in qualitative research have seen a wealth of new methodological approaches developed and refined, supporting examination of questions relevant to behavior analysts without reliance on theory generation or specific epistemological leanings. It is equally true that the growth of qualitative research has afforded increased attention to issues of quality and rigor (Smith, 2015 ). Tools for assessing the quality of qualitative research have trailed behind other traditions (e.g., quantitative, large-n research; Morse et al., 2002 ). However, current development of protocols and criteria for evaluating the rigor of qualitative research provides confidence to those who use these methods in their scientific inquiry. For behavior analysts, with a focus on interobserver agreement, treatment fidelity, and other measures of validity (Kazdin, 2011 ), a shift toward robust assessment of quality provides reassurance that qualitative methods can meet the technological dimension of behavior analysis.

Qualitative research holds as a basic tenet the generation of new findings, and the sharing of findings with interested communities (Braun & Clarke, 2006 ). This aligns with the behavior analytic goal of generality or generalizability. The ability to share research in ways that will promote, maintain, and appropriately expand the impacts of treatment, has long been at the forefront of behavior analytic research (Baer et al., 1987 ). By using methods preferred and understood by related disciplines (e.g., nursing, social work, occupational therapy) behavior analysts ensure their findings will “appear in a wide variety of possible environments” (Baer et al., 1968 , p. 96) and persist over time. Qualitative methods do not utilize the jargon endemic to ABA, which has been perceived negatively by those outside the field (Critchfield & Doepke, 2018 ), removing the barrier of translating what behavior analysts know into forms that others can understand. Adopting qualitative methods in appropriate situations, behavior analysts can directly show how or why behavioral interventions “fit” within schools, health settings, and communities (Schwartz et al., 1995 ), helping to reach broader audiences and improve the application of effective interventions in natural environments.

Given the growth of the field of behavior analysis over the past decades, and developments in qualitative methodologies during this time, it seems important for contemporary behavior analysts to consider how qualitative methods could be applied to empirical questions that are critical to the success of behavior analysis. In particular, increased adoption of qualitative methods where they are the best “fit” for the question, may aid in progressing a socially valid science that can readily investigate a wide variety of topics of interest and relevance to society today (Heward et al., 2022 ).

Where Qualitative Methods Could Advance BA: Social Validity

From the beginning, behavior analysts have been concerned with establishing, and measuring, social validity (the degree to which goals, procedures, and effects of behavior analytic input are socially meaningful and relevant to consumers; Kazdin, 1977 ). Yet, the area of social validity has been fraught with complications in accurate measurement and empirical evaluation (Carr et al., 1999 ; Ferguson et al., 2018 ).

In the seminal article “Social Validity: The Case for Subjective Measurement or How Applied Behavior Analysis is Finding Its Heart,” Wolf ( 1978 ) posited that social validity created a tension for behavior analysts at the inception of the field because it lent itself to subjective measures rather than tightly defined and controlled experimentation. Wolf identified that variables such as the perceived relevance and usefulness of behavioral intervention by consumers were not amenable to objective definition and measurement, and consequently could not be assessed using single-case design. Rather than discount social validity as subjective and not relevant to ABA, Wolf argued that “if we aspire to social importance, then we must develop systems that allow our consumers to provide us feedback about how our applications relate to their values, to their reinforcers” (p. 213). Even at that early stage, supplementation of traditional behavior analytic measures was proposed, to afford behavior analysts ways “to approach the specific consumer or representatives of the relevant community, and through interviews or ratings, determine much more precisely what the socially significant problems are” (p. 209).

More than a decade later, Schwartz and Baer ( 1991 ) identified that, despite widespread agreement on the importance of social validity in behavior analysis, methods for accurately and effectively measuring social validity through research were still lacking. Revisiting the mismatch between traditional behavior analytic procedures for measuring social validity, they suggested that single-case design does not extend to a comprehensive understanding of the field’s social significance or social acceptability, nor does it allow behavior analysts to research how ABA is perceived in wider society. In the absence of strong research methods, they described behavior analysts’ application of “short, simple, bland, undemanding 7-point scales” (p. 192), as biased towards favorable report and thus “socially invalid.” They argued instead that social validity research should use well-designed indirect measures (including self-report using Likert-type surveys) to assess the viability of an intervention to the consumers and stakeholders. They urged behavior analysts to canvas social validity more widely; by asking communities around consumers (as well as consumers themselves) if an intervention is meaningful, appropriate, acceptable and preferred. Explicit in Schwartz and Baer’s argument is the suggestion that, for questions of social validity, single-case designs do not provide all the answers.

Further to this, Schwartz et al. ( 1995 ) advocated the use of qualitative methodologies in behavior analytic inquiries, particularly to address questions of social acceptability. Presenting a case study of mixed methods research in inclusive education, they demonstrated that “stronger linkages between qualitative and behavior analytic research methods” provided an avenue to making “behavioral research more responsive to the values and goals of consumers” (p. 97). They posited that qualitative research could help behavior analysts identify the priorities of stakeholders and communities (e.g., parents and school boards), to formalize understanding of relevant variables that can then be empirically tested. Through qualitative methods, acceptability variables would be elucidated before designing an intervention, putting social importance at the forefront of behavior analysis. This inductive approach, Schwartz et al. ( 1995 ) argued, could allow recipients of services to set the agenda, rather than relying on what the analyst sees as most important to prioritize for treatment. In this way, qualitative methods provide behavior analysts with tools for avoiding blindness to ideas that are visible to consumers, but not obvious to behavior analysts.

As well as better assessing the social validity of interventions, Schwartz and Olswang ( 1996 ) suggested qualitative methods could result in data that are more meaningful to consumers, and answer questions that interest consumers in ways consumers can understand. They reflected that the audience consuming the research outputs must be paramount for behavior analysts as they decide which research methods will lead to compelling findings. It follows that qualitative methods (using words as data) may be more convincing for stakeholders of behavior analytic interventions, and a better way to explore acceptability.

Building from Wolf ( 1978 ) and Schwartz and Baer ( 1991 ) behavior analysts in the 1990s articulated the call for expanded methodologies, proposing indirect quantitative (questionnaires, surveys; Fawcett, 1991 ; Kennedy, 1992 ) and qualitative approaches, to study what is important to consumers (goals; Schwartz, 1991 ), and how consumers perceive interventions (process and effects; Finney, 1991 ). Use of research methods “other” than single-case designs were described as potential options to allow the field to learn what is meaningful and valuable for society, rather than exclusively surveying direct recipients (Ferguson, 1993 , Schwartz, 1991 ).

The intervening years saw the application of qualitative methods to social validity in a smattering of single-case design studies, primarily at the intersection of behavioral and special education literatures. Leko ( 2014 ) evaluated the acceptability of a phonics-based reading program using teacher interviews coupled with direct observations. Leko noted that qualitative methods in the study offered a different perspective to the complicated picture of treatment acceptability for teachers, arguing qualitative approaches can be “differently informative yet equally valuable” (p. 285) in the pursuit of understanding social validity for clients and stakeholders. In an attempt to measure teacher perspectives of a self-monitoring intervention for students diagnosed with ADHD, Vogelgesang et al. ( 2016 ) used questionnaire, semi-structured interview, and journaling methods, with a teacher before and after implementation. Concluding that qualitative data offered insight into “complications” with the intervention that questionnaires alone missed; the authors emphasized the value of mixed-methods analysis of social validity. Nicolson et al. ( 2020 ) echoed the call for increased flexibility in assessing social validity. They contend that “having more tools that are designed to examine different aspects of social validity is a necessary step in the evolution of paying more attention to this somewhat neglected dimension of ABA” (p. 760). This reiteration of the need to use other methodologies to examine social acceptability, however, sits alongside the relative paucity of published literature using qualitative or other quantitative methods in behavior analytic journals. There have been a few promising applications of qualitative methods in measuring social validity, but the expansion of methodologies for this purpose has lacked wide adoption by behavior analysts (Snodgrass et al., 2021 ), and the repeated calls for BA to broaden research methods in studying social validity (Carr et al., 1999 ; Ferguson et al., 2018 ) have largely gone unanswered.

Where Qualitative Methods Could Advance BA: Diverse Topics

Although ABA is often inaccurately described as synonymous with the study of empirically validated approaches for populations with autism, developmental disabilities, and additional learning needs (Axelrod et al., 2012 ; Normand & Kohn, 2013 ), the field has long been interested in other areas which pertain to the behavior of humans in context—including addiction (Silverman et al., 2019 ), obesity (Wilfley et al., 2018 ), gun safety (Chan & Kirby, 2021 ), and seatbelt use (Berry & Geller, 1991 ). Behavior analysts have become increasingly focused on how the field can generate and share findings about human behavior with those who could directly benefit, across settings and situations (Critchfield & Reed, 2017 ; Freedman, 2015 ; Heward et al., 2022 ). When considering the breadth of socially important topics, and the need for BA to “have a say in resolving the most socially relevant problems of our time, such as war, murder . . . disease, public education, and so on” (Vollmer, 2011 , p. 34), arguments are emerging that single-case designs could be just one of many methodological tools available to behavioral researchers interested in an ever-widening array of social contexts, and that qualitative investigations may allow behavior analysts to broaden the type of research questions they ask of ever-expanding domains of interest.

In exploring the impact of Baer et al.’s ( 1968 ) “seven dimensions” on the current state of behavioral research, Critchfield and Reed ( 2017 ) contend that topics that do not lend themselves to precise definition and measurement (i.e., inappropriate under a single-case framework) have been relatively understudied by behavior analysts. Described as “fuzzy concepts”: issues that are socially important and valuable for advancing the field, but poorly defined or with variables not yet fully understood, these domains have largely been underexplored by behavior analysts. This is in part because research in these murky areas faces barriers to publication for not equally demonstrating all seven dimensions of behavior analysis (thought to represent an “overly strict criterion”; Critchfield & Reed, 2017 ). The reliance on single-case design as the ideal research standard, and the only method which satisfies the “analytic” criteria:

undersells the potential of behavior analysis research to shed light on a wide variety of social problems, discourages interest in problems that do not readily fit into the framework, and supports a too narrow conception of what belongs in applied behavior analysis journals. (Critchfield & Reed, 2017 , p. 151)

In their analysis, Critchfield and Reed remind BA researchers of their responsibility to attend to what Baer et al. ( 1968 ) call “behaviors which are socially important, rather than convenient for study” (p. 92). Far from ignoring socially important topics, analysts should instead apply other research methods to canvas these topics and move toward definition, measurement, and manipulation of key variables in later analysis. In areas where behavior analysis has yet to fully outline the relevant variables (or where variables are multiple and contradictory) other forms of scientific inquiry, such as description of context through qualitative methods, might offer a way forward. Not only do qualitative methods provide novel ways to conceptualize, design, and conduct research in important areas, they may also support behavior analysts in what Kelly et al. ( 2019 ) describe as the “responsibility of every behavior analyst to arrange for contingencies to assure its survival” (p. 449). That is, we need to use a diverse platform of research methods to scope interesting areas of study, and to share newly generated findings in forms consumers are comfortable with, understand, and value (Critchfield & Farmer-Dougan, 2014 ).

Nicolson et al. ( 2020 ) describe the over selectivity of single-case design in ABA literature, which has shaped the field such that other possible methods of inquiry are often not considered by researchers. In addressing the question of what should be done when single-case designs cannot answer all the applied questions behavior analysts, and the wider world, are interested in, Nicolson et al. ( 2020 ) suggest that when questions of interest center around client experiences, consumer perspectives and layperson understanding, qualitative research methods are a more cogent fit. Qualitative methods allow exploration of topics that are important to consumers, but ill-defined from an experimental ABA lens, such as “progress,” “inclusion,” and “relationship.”

The contention that space exists for broadening research methods to investigate “fuzzy concepts” has coincided with a groundswell of public interest in cultural and political movements (e.g., Black Lives Matter, Me Too) and compelling societal issues (e.g., terrorism, pandemic management, and public health initiatives; see Conine et al., 2022 ; Gravina et al., 2020 ; and Matsuda et al., 2020 , for commentary). These are arguably critical areas for behavior analytic investigation but are oftentimes a poor match for research using single-case design, at least in the initial stages of inquiry. Applied behavior analysis, with a unique perspective on socially important human behavior, has a lot to offer in these areas (Dixon et al., 2018 ; Heward et al., 2022 ), particularly if there is a willingness to engage with other research perspectives. It is clear that societally relevant research questions that are important to communities within which behavior analysts live and work, do not always lend themselves to single-case design methodology, at least not until more of the variables involved are clear (Critchfield & Reed, 2017 ; Saini & Vance, 2020 ). Qualitative research could yield the subjective and rich information necessary to identify variables required for later empirical research in these domains, using single-case or group design.

How Qualitative Methods Can Help: Expanding Climate Change Research

To exemplify how qualitative methodology can add to diverse avenues of study, the case of qualitative climate change research provides valuable insights. A growing field (owing to the increasingly evident climate change emergency; World Meteorological Organization, 2022 ) and one historically dominated by quantitative studies, climate change researchers are beginning to incorporate qualitative techniques to answer questions about people’s motivations and reflections on their own behavior, as these relate to actions impacting the environment. In a study on arctic sea-level change in a Norwegian town, Bercht ( 2021 ) used an interpretive paradigm and interview data to understand the perspectives and behavior of locals involved in the fisheries industry as they related to sea level change. Bercht identified an apparent contradiction, when participants talked about concerns of climate change but did not adjust their behavior at either home or work; reflecting how each participant positioned themselves within the climate change crisis facing the town. This research illustrated how the varying ways people see themselves within a problem has implications for how individual, or group-level, action plans could be successfully developed and communicated. Bercht argued that “qualitative approaches urgently matter in climate and ocean change research because climate and ocean solutions are conditional on individual and group behaviors . . . institutional settings and governance structures that are impossible to fully understand in solely quantitative studies” (p. 11). In other applications of qualitative methods to the climate change field, McNamara et al. ( 2020 ) used interview and focus group data to explore the impact and utility of various community-based climate change initiatives in the Pacific Islands, identifying tangible approaches and actions that promote engagement of locals in designing research, collecting data, and developing climate change interventions. In earlier research, Rao et al. ( 2019 ) used a qualitative comparative analysis methodology to explore the role of women’s agency as a variable affecting the effectiveness of climate response programs in Asia and Africa. Without challenging the dominant position of quantitative research in climate change science, current scholars have applied qualitative approaches to better understand the context around the big “problems at hand” (Bercht, 2021 ) and generate data about experiences and perspectives which could better inform the development of effective climate change strategy.

In climate change research, qualitative methods are gaining prominence alongside the traditional and respected quantitative methods. Qualitative approaches give scholars novel tools for exploring the wider context around an issue, event, or situation, such that practical change can be implemented and evaluated. If applied to the field of behavior analysis, qualitative methods could offer similar benefits; allowing the field to study behavior within increasingly novel contexts, and further elucidate the relevant variables in these contexts, to which the science of behavior could be effective in creating desired change. Taken further, qualitative approaches could be woven into mixed methods investigations, again expanding the scope and applicability of behavior analytic research. Broadening research methods could allow behavior analysts to make greater inroads into interesting, socially relevant, and important domains.

For the (Un)Convinced: Where to Next?

The current article highlights the value of applying qualitative methods and may compel behavior analysts to adopt qualitative tools, where these afford a convincing methodology for addressing empirical questions. However, the barriers to routine adoption of qualitative research are many and will require careful problem-solving by the behavior analytic community.

Namely, there exists a bias toward funding provision for quantitative studies (in particular randomized control trials and, more recently, single-case designs), where grant funding agencies are more readily convinced by studies that propose well-planned experimental trials or robust single-case designs than qualitatively derived proposals (Morse, 2003 ). This barrier is not unique to behavior analysis, but it does pose a challenge when calling behavior analysts to use qualitative approaches. Bourgeault ( 2012 ) suggests that both the nature of “traditional” qualitative studies (e.g., small in scale, requiring few resources) and myths around the necessary and sufficient conditions for a high-quality qualitative study have resulted in relatively fewer grant applications using qualitative methods. In turn, funding bodies are less familiar with how to evaluate qualitative studies and less confident about potential outcomes from awarding grants to studies using qualitative methods. Although the contextual factors involved in funding provisions are numerous and complicated (and analysis of these variables is warranted), one suggestion for overcoming this hurdle is to coach both applicants and funders in what constitutes a credible qualitative study and appropriate funding application (see Carey & Swanson, 2003 , for elaboration).

Related to this, the publication trends evident in behavioral journals (Nicolson et al., 2020 ) present a contingency whereby behavior analysts who apply qualitative methods in their scholarship may be thwarted by a reduced likelihood of their research being published in reputable, peer reviewed behavioral journals. Although arguably a key variable affecting the adoption of qualitative research methods, this problem is not insurmountable. Recent special editions within ABA journals and editorial calls for diverse scholarship (LeBlanc, 2020 ) demonstrate that the field is open to, and interested in, a more varied range of perspectives, topics, and research methods in the published literature. It seems that the appropriate motivating operations are present to compel behavior analysts to incorporate qualitative methods into studies published in behavioral journals, when this method is better suited to addressing the research question than single-case design.

Achieving the goal of integrating qualitative methods into behavior science will require that behavior analysts receive rigorous training in qualitative research approaches. Behavior analysts are relatively underinformed about the characteristics of high-quality qualitative methods, what they add to a potential research agenda, and when they should be utilized as the best methodological tool to address the research question or referral concern (Friman, 2021 ). A recent decision from the Behavior Analyst Certification Board (BACB) to move away from regarding Task Lists as “all-encompassing lists of critical behavior-analytic content” (Behavior Analyst Certification Board, 2022 ) toward a suggestion of essential skills for behavior analysts (related to, but distinct from, testing content for accreditation), signals that opportunities to widen the methodological training behavior analysts receive in other research approaches are growing. Enthusiastic behavior analysts could work to develop and publish tutorials in qualitative methods, trainings in using qualitative research tools, and broader conceptualizations of research methodology in verified course sequences; all of which would go some way to developing the skills of behavior analysts in applying qualitative approaches.

As the field of behavior analysis has grown over the past 70 years, attention has been paid to the role of single-case design in defining how, and what, behavior analysts study. Time and again behavior analytic scholars have called for the expansion of research methodologies that behavior analysts can apply to empirical problems. Progress across domains of social validity and diversity of research topics leads the field of behavior analysis to an important conclusion: it is time to expand beyond single-case design research to promote our science. Growth in the field, coupled with rising opposition to ABA in the mainstream, behooves behavior analysts to adopt a range of research methods to best answer a variety of research questions, and to apply the best approach to answer the question, rather than adjusting the topic under study to fit within the more familiar single-case research designs.

In the realm of social validity, where stakeholder perspectives are the key variable of interest, qualitative methods prove a useful addition. Qualitative research provides a methodology to gauge what is important and useful to consumers, and society at large. This could lend credibility to behavior analytic work, and allow behavior analysts to identify when the goals, processes, or products of an intervention could miss the mark for those engaging with the intervention.

Qualitative approaches offer behavior analysts a broader scope to explore interesting and socially relevant topics to which single-case designs do not fit; perhaps because topics require clarification before empirical analyses can be effectively applied, or because key features, phenomenon, and variables are better understood using an inductive, rather than deductive, frame. Inclusion of qualitative research in fields such as climate science demonstrate how qualitative methods can provide behavior analysis another way to increase the reach and scope of our scholarship, to the ultimate benefit of society.

Qualitative methods have evolved rapidly in recent years. They are no longer tied to inflexible philosophical positions or confounded by questionable rigor. Qualitative studies can answer questions that are meaningful to society and hence behavior analysts, with robust and reliable technologies.

This reading of behavioral literature leads us to suggest that the field of behavior analysis could broaden its definition of high quality research to include different methods that answer different, but no less important, questions. Now is the time to expand our horizons and consider a widened scope of research methodologies. In as much as behavior analysts are willing to let the context around behavior dictate selected assessment and intervention approaches, the field should let the context around research questions from the clients’ and the stakeholders’ perspectives drive the selection of research methods, qualitative or quantitative.

This call to consider qualitative methods is not without its challenges. Namely, the apparent lack of funding streams or coherent publication pathways, as well as relative unfamiliarity with qualitative research methods within the behavior analytic field. If now is the time for increased adoption of qualitative methods—and the current authors argue it is—practical tutorials that allow behavior analysts to acquire and become fluent in best practices for using qualitative methods may be an important next step.

Open Access funding enabled and organized by CAUL and its Member Institutions. Preparation of this manuscript was not supported by external funding.

Compliance with Ethical Standards

No research findings with human participants or animal subjects are reported, as such institutional review board approval and informed consent are not relevant to the project.

The authors have no competing interests to declare that are relevant to the content of this article.

The first draft of the manuscript was written by Victoria Burney and all authors commented on previous versions of the manuscript. All authors reviewed, edited, and approved the final manuscript. Authors confirm that this manuscript is comprised solely of original work and is the contribution of all three listed authors.

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