Participants were recruited from the Institute of Noetic Sciences membership through e-newsletters, blogs, and online recruitment postings. Inclusion criteria were: (1) age 18 years or older, (2) fluent in English, (3) having had a prior noetic experience, (4) agreed to the study consent form, and 5) completed all items of the NSI. Volunteers arrived at a HIPAA-compliant SurveyMonkey website and were asked eligibility questions. Eligible volunteers were routed to the informed consent form. All volunteers indicated their informed consent to participate in the study by answering affirmative after being presented details of the study and how the data would be used, and all study activities were approved and overseen by the Institutional Review Board at the Institute of Noetic Sciences (IORG#0003743; WAHH_2019_01). No specific sample size was determined a priori, besides having at least 440 participants (10 participants for each estimated parameter, as recommended; Anthoine et al., 2014; Bentler, 1995).

After consenting, participants answered demographic questions (age, years of education, gender and racial identification, and country) and completed the 44-items of the NSI.

The NSI is a 44-item subjective questionnaire that evaluates 12 factors of intuitive inner knowing ( Wahbeh, Fry, et al., 2022). The 12 factors and their number of items are as follows: General Intuition (10-items); Embodied Sensations (5-items); Visualization/Manifestation (4-items; 1 Reverse Coded); Inner Knowing Through Touch (3-items; 1 Reverse Coded); Healing (3-items; 1 Reverse Coded); Knowing the Future (3-items); Physical Sensations from Other People (3-items); Knowing Yourself (3-items); Knowing Other’s Minds (3-items); Apparent Communication with Non-Physical Beings (3-items; 1 Reverse Coded); Knowing Through Dreams (2-items; 1 Reverse Coded); and Inner Voice (2-items). Items included such statements as, “I have just felt in my body when something is true or not,” and “I have received information about things that will happen in the future.” Participants were asked to, “Please read each statement and move the slider to indicate in general how much you agree or disagree with each statement. There are no right or wrong answers. Just move the slider to the selection you feel is right for you.” The sliding scale was anchored by Strongly Disagree (0), Neither Agree Nor Disagree (50), and Strongly Agree (100). The NSI items are available upon request from the first author.

All data cleaning/organization and statistical analyses were conducted using Microsoft Excel (Redmond, WA). Data analyses were conducted in R version 4.2.3. (RRID:SCR_001905; R Development Core Team, 2021). Reverse coded items were transformed. The updated dataset (with reverse coded items) was assessed for multivariate normality using the Mardia Skewness and Kurtosis multivariate normality tests and Shapiro–Wilk univariate normality tests in the R package MVN ( Korkmaz et al., 2014).

CFA is a statistical technique commonly used to validate the factor structure of a measure and is based on the assumption that a construct is composed of multiple latent factors, each measured by a set of observed variables ( Brown & Moore, 2012). CFA allows researchers to test whether the observed variables are reliable indicators of the latent factors and to identify the underlying factor structure of a measure. Therefore, CFA is a useful tool for assessing the construct validity of a measure. A standard CFA model was fit to the data using the R package lavaan with the latent variable variance constrained to 1. Given that the data did not uphold the assumption of multivariate normality, CFAs were fit using the diagonally weighted least squares (DWLS) estimator and the limited-memory Broyden–Fletcher–Goldfarb–Shanno optimization method ( Byrd et al., 1995). The DWLS estimator was used for this CFA, as it was deemed a more appropriate estimator for these data because 0 and 100 data can be considered ordinal or numerical. That is, it does not quite fit ordinal data because there are so many categories, but they do not quite fit numerical data because they do not live on the real number line but an interval.

We assessed the goodness of fit of the model using several fit indices. While Chi-squared was the original test of model fit, and is still expected to be reported, it has limitations in cases of both large or small sample size, and so various other fit metrics have been developed ( West et al., 2012). Thus, we used the comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR). The CFI and TLI are both indicators of “goodness-of-fit”, ranging from 0 to 1, with larger values indicating better fit. Of note, TLI values can technically extend below 0 or above 1 in cases of extremely bad or good fit. The typical rules for interpreting CFI and TLI values are that values greater than 0.90 and 0.95, respectively, indicate acceptable fit and very good fit ( Hooper et al., 2007; Parry, 2019). RMSEA and SRMR are both indicators of “badness-of-fit”, where 0 corresponds to a perfect fit. For RMSEA, values less than 0.05 correspond to a “close fit”, a value of 0.08 would correspond to “adequate fit”, and values above 0.10 indicate “unacceptable fit” ( West et al., 2012). Interpretation of SRMR is similar to RMSEA, with values less than.05 indicating a “good fit”, and values up to 0.10 considered a “acceptable fit” ( Cangur & Ercan, 2015).

There are several limitations to consider in this study. First, the sample was recruited from the Institute of Noetic Sciences membership, which may introduce selection bias as members of this organization may have a higher likelihood of having had noetic experiences compared to the general population. This may limit the generalizability of the findings to other populations. However, this study was not evaluating prevalence and so recruiting a population that has had noetic experiences is beneficial. While the study population here was somewhat diverse in that around 30% were non-Caucasian, the predominance of Caucasians from Western countries is notable and does not reflect the global population. The NSI being in English limits its use in non-English populations. Another limitation of this study is that we did not include any other questionnaires and thus, can not infer continued content validity in this population, although previous work has already demonstrated this ( Wahbeh et al., 2022). Additionally, the study relied on self-report measures, which are subject to recall bias and social desirability bias. Participants may have provided responses that they believed were more socially acceptable or aligned with their beliefs about noetic experiences, leading to potential measurement error. Furthermore, the study did not include any objective measures to validate the self-report measures, which may further limit the validity of the findings. Finally, the test-retest psychometrics of the NSI over time was not assessed through this current study.

Despite these limitations, this study has important implications for the field of noetic experiences research. The confirmatory factor analysis of the NSI provides evidence for its 12-factor structure, which adds to the growing body of literature on the different aspects of intuitive inner knowing. This finding may facilitate the use of the NSI as a reliable and valid tool for assessing noetic characteristics in future research and clinical practice. The identification of 12 factors of intuitive inner knowing, such as general intuition, embodied sensations, and knowing the future, may also contribute to our understanding of the multidimensionality of noetic experiences and their potential impact on mental health and well-being. Moreover, the study highlights the need for further research to confirm and extend the findings in different populations and settings, which may help to establish the generalizability and robustness of the NSI as a measurement tool. While the NSI likely does not capture every unique iteration of noetic experiences, what it encompasses thus far appears to be generalizable to the populations it has been administered to. We welcome other researchers to use the NSI for their own studies to continue broadening our understanding of the ubiquitous noetic experience. More importantly, this work supports the normalization of these experiences. Long side-lined at best or labeled as mental illness at worst, these experiences warrant further attention from researchers and clinicians alike ( Wahbeh et al., 2022).

Based on the findings of this study, several recommendations for future research can be made. First, future studies should aim to replicate the 12-factor structure of the NSI in diverse populations, such as different age groups, cultural backgrounds, and clinical populations, to further establish the validity and generalizability of the measure. Future steps to translate the NSI into non-English languages would increase recruitment of more diverse populations worldwide. Evaluating these intuitive experiences across cultures will certainly aid in our understanding of them. In addition, targeted recruitment in diverse racial populations will support testing the NSI’s generalizability. Additionally, longitudinal studies could be conducted to investigate the stability and change of noetic experiences over time, and to examine their potential predictive value for mental health outcomes.

Other areas of future research should include the exploration of intra- and inter-variation, normative values within the populations already studied, the prevalence of the 12 factors and their interactions with each other, if any, and potentially beneficial applications of the information the NSI provides. Objective measures, such as physiological or neurobiological markers, could also be incorporated to provide convergent validity evidence for the self-report measures of noetic experiences. The NSI paired with biological markers would evaluate any organic origin to the specific expression of each individual’s noetic experiences. Preliminary studies have alluded to some relationship between genetics and noetic experiences ( Cohn, 1994, 1999; Neppe & Hurst, 1981; Wahbeh, Radin, et al., 2022) but additional work is needed. Ongoing studies are also evaluating the relationship between structural neuroimaging data and self-report noetic experiences ( Pantazatos, 2022). Further research could also explore the underlying mechanisms of noetic experiences, such as the role of cognitive processes, emotional regulation, and neural networks, to gain a deeper understanding of the nature and origins of these phenomena. These areas and more are ripe for further consideration.