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I would like to use the NEO-FFI instrument to measure the Big 5 psychological traits. Is a license needed? Are there any open, free, or public domain Big 5 tests that can be used for no (or minimal) cost?
Yes, a license is needed for most uses of the NEO-FFI. Here's a quote from PAR's Permissions & Licensing page:
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If you plan to use a test in its entirety, please purchase the published version of the test…
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- Using a translation. All of our translations have been approved. Back-translations have been conducted by an individual unfamiliar with the English version of the test and the back-translation has been forwarded to the author/PAR for review and approval.
- Using any part of a test;
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Anyway, yes, there are plenty of free tests! One such resource my former advisor (Dan Ozer, personality assessor extraordinaire) has always seemed to favor is the International Personality Item Pool. It might give you a headache to see how many options you actually have just through this source, just for measuring broad personality traits akin to the Big Five. In our work, we've generally used the Big Five Inventory (BFI; John, Donahue, & Kentle, 1991), which is also freely available for non-commercial use, but is somewhat basic (44 items, 5 factors, no facets). If that's not basic enough for you, you may also want to consider Saucier's (1994) Mini-Markers, which has 40 items and one fewer Likert scale option. Even shorter is Rammstedt and John's (2007) 10-item short version of the BFI!
I should add that this question is a partial duplicate: @JeromyAnglim already asked (and answered!) the question about free alternatives here: What free scientific measures of Big 5 personality are available? His list is more comprehensive, and I'm about to go add Rammstedt and John's over there, so this post won't have anything unique from that thread once I do (except the bit about the NEO-FFI licensing).
One might almost be inclined to say there are too many options! As for me, I'm with Martha Stewart on this one: "It's a good thing."
John, O. P., Donahue, E. M., & Kentle, R. L. (1991). The big five inventory-versions 4a and 54. Berkeley: University of California, Berkeley, Institute of Personality and Social Research.
Rammstedt, B., & John, O. P. (2007). Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German. Journal of Research in Personality, 41(1), 203-212. Available online, URL: http://www.westmont.edu/_academics/departments/psychology/documents/Rammstedt_and_John.pdf.
Saucier, G. (1994). Mini-markers: A brief version of Goldberg's unipolar Big-Five markers. Journal of Personality Assessment, 63(3), 506-516. Available online, URL: http://darkwing.uoregon.edu/~prsnlty/SAUCIER/Saucier.Minimarkers.Full.pdf.
ORIGINAL RESEARCH articleRoshin Kunnel John 1* , Boby Xavier 1 , Anja Waldmeier 1 , Andrea Meyer 2 and Jens Gaab 1
- 1 Division of Clinical Psychology and Psychotherapy, Faculty of Psychology, University of Basel, Basel, Switzerland
- 2 Division of Clinical Psychology and Epidemiology, Faculty of Psychology, University of Basel, Basel, Switzerland
The Five-Factor Model (FFM) is one of the most commonly examined constructs of personality across cultures in recent times. However, there is a lacuna of evidence for the suitability of FFM measures for Indian adolescent school students below the age of 17 years. We carried out two independent studies for the psychometric evaluation of the measures BFI-10 and NEO-FFI-3 on Indian adolescent school students. Both studies examined two socio-culturally distinct linguistic groups of secondary and senior secondary school students with a total sample of N = 1117 students. There was very limited support for a five-factor solution in both cases. Model fit was poor when applying FFM measures to our samples, whether applying confirmatory factor analysis or exploratory structural equation models. The results provide evidence against using adult personality measures with adolescents without separate psychometric validation and applying the Western age norms to Indian students without considering that the process of personality consolidation during adolescence may not be identical across cultures.
The present investigation is based on a population representative sample (concerning age, education level, and sex) which is described in detail by Körner et al. . In November 1999 a total of 1908 subjects aged between 18 and 96 years participated in this investigation. Participants were guaranteed that answers would be treated confidentially and anonymously. They received no benefits from their participation. The participants were administered the NEO-Five-Factor-Inventory (NEO-FFI) by Borkenau and Ostendorf  as well as the Giessen-Test (GT) by Beckmann et al. . The NEO-FFI captures the five traits Neuroticism, Extraversion, Openness to Experience, Agreeableness, and Conscientiousness with 12 items each and a five-category answer format per item. The GT captures the six dimensions Social Resonance, Pliancy, Control, Depressiveness, Reservedness, and Social Potency with a total of 40 bipolar statements (e.g. “I got the impression that I am rather patient…impatient”) whose relevance should be rated on a seven-category scale. For a better comparability of both methods the GT was not presented in the classic six-factor version but a five-factor version (without the scale Social Potency) which shows better psychometric characteristics than the six-factor version and is described in detail by Brähler and Beckmann  and Brähler and Brähler . The subsequent analyses only include subjects whose data records were complete in both methods (N=1673 age: 18-96 years, M=47, SD=16 54% female 46% male).
Internalized Stigma of Mental Illness Scale-9 (ISMI-9)
The Internalized Stigma of Mental Illness Scale-9 (ISMI-9) was developed by Dr. Joseph H. Hammer and Dr. Michael D. Toland and published in the peer-reviewed academic journal Stigma and Health in 2017. Here is the APA-style citation for the instrument:
Hammer, J. H., & Toland, M. D. (2017). Internal structure and reliability of the Internalized Stigma of Mental Illness Scale (ISMI-29) and brief versions (ISMI-10, ISMI-9) among Americans with depression. Stigma and Health, 2, 159-174. doi: 10.1037/sah0000049
The ISMI-9 is a nine-item unidimensional short form of the original English-language version of the ISMI-29, which was developed by Dr. Jennifer Boyd and colleagues and published in 2003 (see Ritsher (Boyd), Otilingam, & Grajales, 2003). Dr. Jennifer Boyd and colleagues have published a ten-item short form of the ISMI-29 (see Boyd, Oitlingham, & DeForge, 2014). Researchers are encouraged to consider both the ISMI-9 and ISMI-10 when designing new studies, as each version may offer certain advantages and limitations (see Hammer & Toland, 2017). We thank Dr. Jennifer Boyd and her colleagues for developing the ISMI, which has facilitated groundbreaking research on the nature, impact, and mitigation of internalized stigma of mental illness.
What does the ISMI-9 measure?
The stigma of mental illness is the prejudice and discrimination that results from endorsing negative stereotypes about people with mental illness (Corrigan & Watson, 2002). Internalized stigma of mental illness is the harmful psychological impact that results from internalizing this prejudice and directing it toward oneself.
The ISMI-9 is a self-report instrument designed to measure the overall strength of respondents’ internalized stigma of mental illness (i.e., self-stigma of mental illness) among persons with psychiatric disorders. A higher score indicates more severe internalized stigma of mental illness.
The items assume that respondents self-identify as having a mental illness (e.g., “Because I have a mental illness, I need others to make most decisions for me”) and thus are most appropriately used with clinical populations.
How do I administer the ISMI-9?
The ISMI-9 can be administered via an electronic/internet format or a paper & pencil format.
How do I score the ISMI-9?
The ISMI-9 is a unidimensional instrument that primarily reflects a single common source of variance (Hammer & Toland, 2017). Therefore, only the ISMI-9 total score should be calculated and interpreted.
The ISMI-9 contains 9 items which produce a total score. Reverse-code items 2 and 9 before calculating the total score. Add the item scores together and then divide by the total number of answered items. The resulting score should range from 1 to 4 because the four-point Likert rating scale ranges from 1 (Strongly disagree) to 4 (Strongly agree). For example, if someone answers 8 of the 9 items, the total score is produced by adding together the 8 answered items and dividing by 8.
Per Parent (2014)’s 20% recommendation, I do not recommend calculating a mean score for those cases/participants who responded to less than 8 of the 9 items. In many cases, it should be permissible to calculate a mean score for those cases/participants who answered 8 or all 9 of the ISMI-9 items. See Schlomer et al. (2010) for information on best practices regarding the handling of missing instrument data.
How do I interpret the ISMI-9 total score?
The ISMI-9 total score is a measure of overall internalized stigma of mental illness.
More precisely, the ISMI-9 total score is a numerical quantification of the degree to which a person reports overall agreement with five themes of internalized stigma of mental illness: Alienation, Stereotype Endorsement, Perceived Discrimination, Social Withdrawal, and Stigma Resistance (reverse scored).
These five “topic areas” constitute what is known as the “content domain” of internalized stigma of mental illness, as conceptualized by Dr. Jennifer Boyd and colleagues (2003) and operationalized by the ISMI-29. While the nine items of the ISMI-9 are evenly drawn from these five topic areas, these items were selected to form the ISMI-9 specifically because they primarily measure the general internalized stigma of mental illness factor, which is the factor that any short form of the ISMI is designed to measure. In other words, even though the items of the ISMI-9 are drawn from the different subscales of the ISMI-29, they cannot and should not be used to try to form subscales measuring the five separate topic areas (see Hammer & Toland, 2017 Boyd, Otilingham, & DeForge, 2014). This same logic applies to the ISMI-10.
Boyd, Oitlingham, and DeForge (2014) describe two methods of interpreting the size of ISMI total scores:
4-category method (following the method used by Lysaker et al., 2007):
1.00-2.00: minimal to no internalized stigma
2.01-2.50: mild internalized stigma
2.51-3.00: moderate internalized stigma
3.01-4.00: severe internalized stigma
2-category method (following the method used by Ritsher [Boyd] & Phelan, 2004).
1.00-2.50: does not report high internalized stigma
2.51-4.00: reports high internalized stigma
What is the factor structure of the ISMI-9?
Hammer and Toland (2017) found evidence that the nine items of the ISMI-9 conform most closely to a unidimensional factor structure. Thus, the ISMI-9 is best conceptualized as a unidimensional instrument that primarily reflects a single common source of variance.
As noted above, the ISMI-9 cannot be said to contain subscales and cannot be used to generate factor scores for the five topical areas.
What evidence exists regarding the reliability and validity of the ISMI-9 total score?
Regarding evidence concerning internal structure (Standard 1.13 AERA et al., 2014), the ISMI-9 demonstrated a clear unidimensional factor structure: S-Bχ 2 (27) = 70.35, p < .001, RMSEA = .046 [90% CI of .033, .059], CFI = .976, TLI = .967, SRMR = .027. This supports the use of the ISMI-9 total score as a measure of the overall internalized stigma of mental illness.
Regarding evidence of reliability/precision (Standard 2.3), the ISMI-9 total score demonstrated slightly stronger internal consistency (α = .86, 95% CI [.85, .88]) and cleaner measurement of the general internalized stigma of mental illness construct (ωH = .89, ECV = .87, PUC = .89) than the ISMI-10 total score (see Hammer & Toland, 2017). Consistent with this, 100% of the ISMI-9 items had IECV values above .80.
Regarding content-oriented evidence of the validity of the ISMI-9 total score (Standard 1.1), like the ISMI-10, the ISMI-9 contains items from each of the five purported factors of the ISMI-29.
However, future research is needed to examine convergent evidence for the validity of the ISMI-9 total score (Standard 1.16). Such evidence has already begun accumulating for the ISMI-10 (Boyd, Otilingham, & DeForge, 2014). The total scores of the ISMI-9 and ISMI-10 (r = .88) were found to correlate .95 and .94, respectively, with the total score of the ISMI-29. Given the strong content overlap of the ISMI-9 and ISMI-10, we might anticipate that the ISMI-9 will demonstrate similar convergent evidence of validity as the ISMI-10. This needs to be tested directly, however.
In summary, Hammer and Toland (2017) found that the ISMI-9 total score demonstrated a slightly cleaner unidimensional structure and stronger reliability than the ISMI-10 total score. However, additional research would help to verify whether these findings are idiosyncratic or generalizable.
What are some current limitations of the ISMI-9?
All instruments have limitations. The ISMI-9 is no exception. I believe it is important that potential users of the ISMI-9 know what these limitations are so that they can make informed choices about how to use the ISMI-9. These limitations also present researchers with opportunities to conduct and publish new research studies on the psychometric properties of the ISMI-9. Feel free to reach out to me if you are interested in conducting such a study with my help.
- Further examination of the cross-cultural reliability and validity of the ISMI-9 among diverse groups (e.g., race/ethnicity, inpatient) is necessary, given that the samples used by Hammer and Toland (2017) were composed primarily of community-dwelling adults living in the United States who self-identified as having a mental illness—depression, in this case. The majority of the sample was also female, white, and reported minimal to mild internalized stigma. Therefore, it is possible that the findings of Hammer and Toland (2017) are unique and only apply to this specific intersectional population.
- As noted above, investigation of convergent and predictive evidence of validity for the ISMI-9 total score is warranted.
How do I obtain a copy of the ISMI-9?
You may download a copy of the ISMI-9 instrument in .doc or .pdf format. A full copy of the ISMI-9 instrument is also included in the Appendix of Hammer and Toland (2017). You do not need to request Dr. Hammer’s permission to use the ISMI-9.
Please note that a full copy of Dr. Jennifer Boyd and colleagues’ ISMI-10 is included in the Appendix of Boyd, Oitlingham, and DeForge (2014).
Researchers interested in the ISMI-10 and ISMI-29 are encouraged to correspond with Dr. Jennifer Boyd.
Guide for use: Patient function is assessed using the FIM™ instrument at the start of a rehabilitation episode of care and at the end of a rehabilitation episode of care. Admission assessment is collected within 72 hours of the start of a rehabilitation episode. Discharge assessment is collected within 72 hours prior to the end of a rehabilitation episode.
FIM™ is comprised of 18 items, grouped into 2 subscales - motor and cognition.
The motor subscale includes:
- Dressing, upper body
- Dressing, lower body
- Bladder management
- Bowel management
- Transfers - bed/chair/wheelchair
- Transfers - toilet
- Transfers - bath/shower
The cognition subscale includes:
Each item is scored on a 7 point ordinal scale, ranging from a score of 1 to a score of 7. The higher the score, the more independent the patient is in performing the task associated with that item. 
Helper - Modified Dependence
- 5. Supervision (Subject = 100%)
- 4. Minimal Assistance (Subject = 75% or more)
- 3. Moderate Assistance (Subject = 50% or more)
Helper - Complete Dependence
- 2. Maximal Assistance (Subject = 25% or more)
- 1. Total Assistance or not Testable (Subject less than 25%)
Leave no blanks. Enter 1 if not testable due to risk. 
The total score for the FIM
- motor subscale (the sum of the individual motor subscale items) will be a value between 13 and 91.
- cognition subscale (the sum of the individual cognition subscale items) will be a value between 5 and 35.
The total score for the FIM instrument (the sum of the motor and cognition subscale scores) will be a value between 18 and 126. 
[Personality assessment with the NEO-Five-Factor Inventory: the 30-Item-Short-Version (NEO-FFI-30)]
Over these past years, German researchers have shown much interest for Costa and McCrae's five factor model as well as for their instrument: the NEO-Five-Factor Inventory . Nevertheless, results from a recent survey study using the German version of the NEO-FFI on a representative population sample (n = 1908) have reported problems to replicate the factor structure of the instrument. Insufficient psychometric indices of single items led to partly unsatisfactory scale values. A logical consequence of this was the development of a short version of the instrument with better psychometric properties. This article reports item and scale values of the NEO-FFI-30 for the German population sample. The five scales reach good internal consistency and are highly correlated with the original NEO-FFI scales. Furthermore, the influence of sociodemographic variables and correlations with the Giessentest appear to be very similar for both the original instrument and the short version. Moreover, the factor structure was replicated in an independent sample of 2508 adults. Results confirm the reliability, and factor and construct validity of this economic instrument without any significant loss in information.
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The total sample was composed of 595 Spanish adult volunteers (195 men 400 women), with a mean age of 37.38 years (SD = 12.54 range: 18–76). Most of the participants were married or living with a partner (n = 273 45.89), and most had high-level studies (n = 352 59.2%).
The patients had been diagnosed with depressive disorder (i.e. major depressive disorder, dysthymic disorder, depression not otherwise specified) (n = 237), anxiety disorder (i.e. generalized anxiety disorder, panic disorder/agoraphobia, social anxiety disorder, obsessive-compulsive disorder) (n = 284), and adjustment disorder (n = 74). In addition, 35% of the total sample presented at least one comorbid anxiety or depressive disorder. Table 1 presents a detailed description of the participants’ sociodemographic and clinical data.
Cronbach alpha was .91 for the PANAS-P and .87 for the PANAS-N. Tables 2 and 3 display Cronbach alphas when omitting items, corrected correlations between each item and the total score, and correlations between the items on PANAS-P and PANAS-N, respectively.
Convergent and divergent validity
Table 4 shows the correlations between PANAS-P and PANAS-N and convergent and divergent validity measures. Significant correlations were found between both PANAS-P and PANAS-N and all the measures. As expected, a negative but medium correlation was found between PANAS-P and PANAS-N (r = −.30, p < .01). Negative and large correlations were found between PANAS-P and depression measures (BDI-II: r = −.56, p < .01 PHQ-9: r = −.52, p < .01), whereas the correlations between PANAS-P and anxiety measures were small to medium (BAI: r = −.23, p < .01 OASIS: r = −.39, p < .01). By contrast, positive large correlations were found between PANAS-N and depression (BDI-II: r = .63, p < .01 PHQ-9: r = .54, p < .01) and anxiety measures (BAI: r = .58, p < .01 OASIS: r = .64, p < .01). Finally, a negative but medium correlation was found between PANAS-P and NEO-FFI-N (r = −.39, p < .01), and a positive and medium correlation between PANAS-P and NEO-FFI-E (r = .44, p < .01). In addition, a positive and large correlation was found between PANAS-N and NEO-FFI-N (r = .65, p < .01), and a negative but small correlation between PANAS-N and NEO-FFI-E (r = −.24, p < .01).
Confirmatory factor analysis
The PANAS has a two-correlated factor structure, positive and negative affect. We tested this two-factor structure with a confirmatory factor analysis. The model reasonably fitted the observed data: χ 2 (169) = 1425.31, p < .001, χ 2 (169) = 8.43, RMSEA = .112 CI [.106–.117], CFI = .917, TLI = .907, SRMR = .076. Fit was adequate according to both the CFI and the SRMR. RMSEA was a little higher than expected. However, and taking into account that the parameter estimates were all statistically significant and very large, we can conclude that the model has an adequate fit. Figure 1 shows the CFA model. As mentioned above, all factor loadings were statistically significant (p < .001) and large. With regard to the first factor (positive affect), all standardized loadings were in the range from .68 to .85. Regarding the second factor (negative affect), again standardized loadings were large, with a minimum of .59 and a maximum of .83.
Confirmatory factor analysis (CFA) model. Note. Rectangles are measured variables, the large circles are the latent construct, and ellipses are residual variances. Factor loadings are standardized
PANAS and sociodemographic and clinical variables
In the total sample, the mean PANAS-P score was 20.19 (SD = 6.91), and the mean PANAS-N score was 29.07 (SD = 8.14). Tables 5 and 6 show the means and standard deviations for each item and the total score on both the PANAS-P and the PANAS-N by diagnosis category.
On the PANAS-P, the results of one-way ANOVAs yielded statistically significant differences based on civil status, F(3, 591) = 3.05, p < .05, diagnostic category, F(2, 592) = 12.22, p < .001, principal diagnosis, F(10, 584) = 5.56, p < .001, and number of comorbid diagnoses, F(3, 590) = 2.92, p < .05. Sidak’s post hoc tests showed that patients in the category of depressive disorders had significantly lower scores on PA than those in the categories of anxiety (p < .001) and adjustment disorders (p < .05). Additionally, the results of Sidak’s post hoc tests showed that patients with MDD as the principal diagnosis had significantly lower scores on PA than patients with GAD (p < .01), AG (p < .01) and OCD (p < .001). On the PANAS-N, no significant differences were found on any of the sociodemographic or clinical variables, except for the number of comorbid diagnoses, F(3, 590) = 9.07, p < .001). Sidak’s post hoc tests showed that patients with one (p < .01), two (p < .05) or three (p < .01) comorbid disorders had significantly higher scores on NA, compared to patients with no comorbid diagnoses.
Sensitivity to change
To examine potential floor and ceiling effects for the PANAS-P and PANAS-N scores, the frequencies and percentages of minimum (10) and maximum (50) scores on the pretest were tabulated for each sample, using the participants in the treatment and control groups. As Table 7 shows, floor and ceiling effects can be ruled out because the percentage of minimum and maximum scores was less than 15% in all three studies.
In addition, means and standard deviations for the pretest and posttest were calculated for the treatment groups in each sample. The statistical significance of the pretest-posttest change scores was assessed by applying t-tests. As Table 8 shows, statistically significant pretest-posttest differences were found in the three studies for both PANAS-P and PANAS-N scores. Clinical significance was assessed by means of the effect size index ‘standardized mean change index’ (d). Following Rubio-Aparicio et al.’s (2017) results, with the exception of the PANAS-P scores in Sample 3, all effect sizes were moderate to large (all over 0.74).
There are three types of transfer of training:
- Positive Transfer: Training increases performance in the targeted job or role. Positive transfer is the goal of most training programs. 
- Negative Transfer: Training decreases performance in the targeted job or role. 
- Zero Transfer: Training neither increases nor decreases performance in the targeted job or role. 
Baldwin and Ford (1988)  is the most commonly cited model of transfer, which defines the transfer of training as the generalization and maintenance of material learned in training to the work environment. 
Within this model, the authors conceptualize transfer of training as a three-stage process.   In the first stage, the inputs to training, including the training strategies, the work environment, and trainee characteristics are defined.   Next, through the training process, these inputs generate training outputs in the form of learning and retention.   Ultimately, transfer of training occurs in the final stage when learning and retention are generalized and maintained in the work environment.   Using the training inputs defined in this model, psychological research has identified many factors that contribute to the positive transfer of training.  
Within the current literature, there is a lack of consensus over what factors contribute to the positive transfer of training.   However, across psychological research, the following factors have consistently impacted positive transfer.
Trainee characteristics Edit
- Higher cognitive ability typically leads to higher levels of retention and generalization of learned material.  : Higher self-efficacy contributes to positive transfer through its influence on confidence and persistence.  : Individuals with a higher motivation to learn tend to experience higher levels of positive transfer of training.  : Higher measures of conscientiousness increase the likelihood of positive transfer.  of Utility: Beliefs in the value and usefulness of training increase the likelihood of positive transfer. 
Work environment Edit
- Transfer climate: By definition, a positive transfer climate is a work environment that contains cues and feedback mechanisms that remind employees of learned material.  Positive transfer climates tend to facilitate higher levels of positive transfer. 
- Support: Support from supervisors and peers leads to higher levels of positive transfer. 
- Opportunity to Perform: Work environments that provide opportunities to use learned material promote higher positive transfer of training. 
- Check-Ins: Regular reviews of training material solidify knowledge and contribute to positive transfer. 
Training strategies Edit
- Similarity: Also referred to as identical elements theory, a high degree of similarity between the training environment and work environment increases the positive transfer of training.  : Hands-on practice of material contributes to positive transfer, especially when it incorporates a variety of different contexts.  : A training technique inspired by Albert Bandura's theory of social learning, which involves explanations, demonstrations, and active learning, feedback, and reinforcement .  Behavioral modeling is associated with increased positive transfer, especially when both incorrect and correct behavioral examples are provided during training. 
- Error-based examples: Training that focuses on how to deal with problems and learn from errors facilitates higher positive transfer.  : Collaboration between trainees, trainers, and supervisors during training increases positive transfer. 
- Multiple Strategies: The use of variety of teaching and learning strategies facilitates positive transfer. 
- Goals: Setting goals and expectations for training increases positive transfer.  : Intermittent assessments of participant's knowledge of learned material increases positive transfer. 
Positive transfer is the goal of many organizational training programs.  Therefore, transfer of training plays a vital role in evaluating a training program's effectiveness.  Common training evaluation methods, such as Kirkpatrick's Taxonomy  and the Augmented Framework of Alliger et al.,  utilize transfer as an essential criterion to evaluate training.  Due to its behavioral outcomes, transfer of training allows organizations to quantify the impact of training and measure differences in performance. 
Assessing the Big Five
Abstract. A new measure of the Big Five personality constructs, the Openness Conscientiousness Extraversion Agreeableness Neuroticism Index Condensed (OCEANIC), was developed and validated. In Study 1 (N = 166), the convergent validity with the Big Five as assessed by the NEO-FFI was established. Study 2 (N = 3 808) served to investigate the structure of the instrument with stepwise exploratory factor analysis and confirmatory factor analysis. The incremental predictive validity with respect to objective university grades was examined in Study 3 (N = 145). The results show that a) the scales of the initial item pool converge with those of an established measure of the Big Five, b) the Big Five factor model fits the data both at the item and facet level and both for subsamples of students and workers, and c) consistent with previous research, the Conscientiousness factor of the OCEANIC predicts university grades beyond intelligence measures.
Zusammenfassung. Ein neues Verfahren zur Erfassung der fünf Faktoren der Persönlichkeit (Big Five), der Openness Conscientiousness Extraversion Agreeableness Neuroticism Index Condensed (OCEANIC), wurde entwickelt und validiert. In Studie 1 (N = 166) wurde die konvergente Validität mit den fünf Faktoren untersucht, wie sie mit dem NEO-FFI erfasst werden. Studie 2 (N = 3 808) diente der Untersuchung der Struktur des Instruments unter Verwendung der schrittweisen exploratorischen Faktorenanalyse und konfirmatorischen Faktorenanalyse. Die inkrementelle prädiktive Validität hinsichtlich objektiv erfasster universitärer Prüfungsleistungen wurde in Studie 3 (N = 145) untersucht. Die Resultate zeigen, dass a) die Skalen des Ausgangsitempools mit jenen eines etablierten Erhebungsverfahrens der Big Five konvergieren, b) das Fünf-Faktoren-Modell auf die Daten sowohl auf Itemebene als auch Facettenebene gute Anpassung zeigt - und zwar auch innerhalb von Teilstichproben von Studierenden und Erwerbstätigen - sowie c) konsistent mit vorherigen Untersuchungen der Faktor Gewissenhaftigkeit des OCEANIC Prüfungsleistungen auf universitärem Niveau über Intelligenzmaße hinaus vorhersagt.
Ackerman, P. L. , Heggestad, E. D. ( 1997 ). Intelligence, personality, and interest: Evidence for overlapping traits . Psychological Bulletin , 121, 219– 245 . First citation in articleCrossref, Google Scholar
American Educational Research Association, American Psychological Association, and National Council on Measurement in Education ( 1999 ). Standards for educational and psychological testing . Washington, DC: AERA . First citation in articleGoogle Scholar
Aluja, A. , García, O. , Rossier, J. , García, L. F. ( 2005 ). Comparison of the NEO-FFI, the NEO-FFI-R and an alternative short version of the NEO-PI-R (NEO-60) in Swiss and Spanish samples . Personality and Individual Differences , 38, 591– 604 . First citation in articleCrossref, Google Scholar
Barrick, M. R. , Mount, M. K. ( 1991 ). The Big Five personality dimensions and job performance: A meta-analysis . Personnel Psychology , 44, 1– 26 . First citation in articleCrossref, Google Scholar
Block, J. ( 1995 ). A contrarian view of the five-factor approach to personality description . Psychological Bulletin , 117, 187– 215 . First citation in articleCrossref, Google Scholar
Borkenau, P. , Riemann, R. , Angleitner, A. , Spinath, F. M. ( 2001 ). Genetic and environmental influences on observed personality: Evidence from the German Observational Study of Adult Twins . Journal of Personality and Social Psychology , 80, 655– 668 . First citation in articleCrossref, Google Scholar
Briggs, S. R. ( 1992 ). Assessing the five-factor model of personality description . Journal of Personality , 60, 253– 293 . First citation in articleCrossref, Google Scholar
Byravan, A. , Ramanaiah, N. V. ( 1995 ). Structure of the 16 PF fifth edition from the perspective of the five-factor model . Psychological Reports , 76, 555– 560 . First citation in articleCrossref, Google Scholar
Christal, R. E. ( 1994 ). The Air Force Self Description Inventory (version 1). Final R&D status report (November, 1994) . Armstrong Laboratory, Brooks AFB, TX: USAF Contract # F33615-91-D-0010 . First citation in articleGoogle Scholar
Costa, P. T., Jr. , McCrae, R. R. ( 1992a ). Four ways five factors are basic . Personality and Individual Differences , 13, 653– 665 . First citation in articleCrossref, Google Scholar
Costa, P. T., Jr. , McCrae, R. R. ( 1992b ). Revised NEO personality inventory and NEO five factor inventory professional manual . Odessa, FL: Psychological Assessment Resources . First citation in articleGoogle Scholar
Curran, P. J. , West, S. G. , Finch, J. F. ( 1996 ). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis . Psychological Methods , 1, 16– 29 . First citation in articleCrossref, Google Scholar
Davies, M. , Stankov, L. , Roberts, R. D. ( 1998 ). Emotional Intelligence: In search of an elusive construct . Journal of Personality and Social Psychology , 75, 989– 1015 . First citation in articleCrossref, Google Scholar
De Raad, B. ( 2000 ). The big five personality factors: The psycholexical approach to personality . Kirkland, WA: Hogrefe & Huber . First citation in articleGoogle Scholar
DeVellis, R. F. ( 1991 ). Scale development: Theory and applications . London: Sage . First citation in articleGoogle Scholar
Digman, J. M. ( 1990 ). Personality structure: Emergence of the five-factor model . Annual Review of Psychology , 41, 417– 440 . First citation in articleCrossref, Google Scholar
Digman, J. M. ( 1997 ). Higher-order factors of the Big Five . Journal of Personality and Social Psychology , 73, 1246– 1256 . First citation in articleCrossref, Google Scholar
Digman, J. M. , Inouye, J. ( 1986 ). Further specification of the five robust factors of personality . Journal of Personality and Social Psychology , 50, 116– 123 . First citation in articleCrossref, Google Scholar
Egan, V. , Deary, I. , Austin, E. ( 2000 ). The NEO-FFI: Emerging British norms and item-level analysis suggest N, A, and C are more reliable than O and E . Personality and Individual Differences , 29, 907– 920 . First citation in articleCrossref, Google Scholar
Eysenck, H. J. ( 1992 ). Four ways five factors are not basic . Personality and Individual Differences , 13, 667– 673 . First citation in articleCrossref, Google Scholar
Eysenck, H. J. ( 1993 ). Creativity and personality: Suggestions for a theory . Psychological Inquiry , 4, 147– 178 . First citation in articleCrossref, Google Scholar
Eysenck, H. J. , Eysenck, M. W. ( 1985 ). Personality and Individual Differences: A natural science approach . New York: Plenum . First citation in articleGoogle Scholar
Fabrigar, L. R. , Wegener, D. T. , MacCallum, R. C. , Strahan, E. J. ( 1999 ). Evaluating the use of exploratory factor analysis in psychological research . Psychological Methods , 4, 272– 299 . First citation in articleCrossref, Google Scholar
Funder, D. C. ( 2001 ). Personality . Annual Review of Psychology , 52, 197– 221 . First citation in articleCrossref, Google Scholar
Goldberg, L. R. ( 1990 ). An alternative “description of personality”: The big-five factor structure . Journal of Personality and Social Psychology , 59, 1216– 1229 . First citation in articleCrossref, Google Scholar
Goldberg, L. R. ( 1992 ). The development of markers for the Big-Five factor structure . Psychological Assessment , 4, 26– 42 . First citation in articleCrossref, Google Scholar
Goldberg, L. R. ( 1993 ). The structure of phenotypic personality traits . American Psychologist , 48, 26– 34 . First citation in articleCrossref, Google Scholar
Golden, C. J. , Sawicki, R. F. , Franzen, M. D. ( 1990 ). Test construction . In G. Goldstein & M. Hersen (Eds.), Handbook of psychological assessment (2nd ed., pp 21-40), Elmsford, NY: Pergamon Press . First citation in articleGoogle Scholar
Gorsuch, R. L. ( 1997 ). Exploratory factor analysis: Its role in item analysis . Journal of Personality Assessment , 68, 532– 560 . First citation in articleCrossref, Google Scholar
Gosling, S. D. , Rentfrow, P. J. , Swann, W. B., Jr. ( 2003 ). A very brief measure of the Big Five personality domains . Journal of Research in Personality , 37, 504– 528 . First citation in articleCrossref, Google Scholar
Hendriks, A. A. J. , Perugini, M. , Angleitner, A. , Ostendorf, F. , Johnson, J. A., et al. ( 2003 ). The five-factor personality inventory: Cross-cultural generalizability across 13 countries . European Journal of Personality , 17, 347– 373 . First citation in articleCrossref, Google Scholar
Hogarty, K. Y. , Kromrey, J. D. , Ferron, J. M. , Hines, C. V. ( 2004 ). Selection of variables in exploratory factor analysis: An empirical comparison of a stepwise and traditional approach . Psychometrika , 69, 593– 611 . First citation in articleCrossref, Google Scholar
Holden, R. R. , Fekken, F. G. ( 1994 ). The NEO Five-Factor Inventory in a Canadian context: Psychometric properties for a sample of university women . Personality and Individual Differences , 17, 441– 444 . First citation in articleCrossref, Google Scholar
Horn, J. L. , McArdle, J. J. ( 1992 ). A practical and theoretical guide to measurement invariance . Experimental Aging Research , 18, 117– 144 . First citation in articleCrossref, Google Scholar
Hu, L. , Bentler, P. M. ( 1999 ). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives . Structural Equation Modeling , 6, 1– 55 . First citation in articleCrossref, Google Scholar
International Personality Item Pool ( 2001 ). A Scientific Collaboratory for the Development of Advanced Measures of Personality Traits and Other Individual Differences ( ipip.ori.org/) . Internet Web Site . First citation in articleGoogle Scholar
John, O. P. ( 1990 ). The “big five” factor taxonomy: Dimensions of personality in the natural language and in questionnaires . In L. A. Pervin (Ed.), Handbook of Personality: Theory and Research (pp. 66-100). New York: Guilford . First citation in articleGoogle Scholar
John, O. P. , Goldberg, L. R. , Angleitner, A. ( 1984 ). Better than the alphabet: Taxonomies of personality descriptive terms in English, Dutch, and German . In H. Bonarius, G. van Heck & N. Smid (Eds.), Personality psychology in Europe: Theoretical and empirical developments (pp. 42-71). Berwyn, PA: Swets . First citation in articleGoogle Scholar
John, O. P. , Srivastava, S. ( 1999 ). The Big Five trait taxonomy: History, measurement, and theoretical perspectives . In L. A. Pervin & O. P. John (Eds.), Handbook of personality: Theory and research (2nd ed., pp. 102-138). New York: Guilford Press . First citation in articleGoogle Scholar
Kanning, U. , Holling, H. ( 2001 ). Struktur, Reliabilität und Validität des NEO-FFI in einer Personalauswahlsituation . Zeitschrift für Differentielle und Diagnostische Psychologie , 22, 239– 247 . First citation in articleLink, Google Scholar
Kano, Y. , Harada, A. ( 2000 ). Stepwise variable selection in factor analysis . Psychometrika , 65, 7– 22 . First citation in articleCrossref, Google Scholar
Kline, P. , Barrett, P. ( 1994 ). Studies with the PPQ and the five factor model of personality . European Review of Applied Psychology , 44, 35– 42 . First citation in articleGoogle Scholar
Kuncel, N. R. , Credé, M. , Thomas, L. L. ( 2005 ). The validity of self-reported grade point averages, class ranks, and test scores: A meta-analysis and review of the literature . Review of Educational Research , 75, 63– 82 . First citation in articleCrossref, Google Scholar
Kyllonen, P. C. , Walters, A. M. , Kaufman, J. C. ( 2005 ). Noncognitive constructs and their assessment in graduate education: A review . Educational Assessment , 10, 153– 184 . First citation in articleCrossref, Google Scholar
Little, T. D. , Cunningham, W. A. , Shahar, G. , Widaman, K. F. ( 2002 ). To parcel of not to parcel: Exploring the question, weighing the merits . Structural Equation Modeling , 9, 151– 173 . First citation in articleCrossref, Google Scholar
Loehlin, J. C. ( 1992 ). Genes and environment in personality development . Newbury Park, CA: Sage . First citation in articleGoogle Scholar
Loevinger, J. ( 1994 ). Has psychology lost its conscience? . Journal of Personality Assessment , 62, 2– 8 . First citation in articleCrossref, Google Scholar
Lüdtke, O. , Trautwein, U. , Nagy, G. , Köller, O. ( 2004 ). Eine Validierungsstudie zum NEO-FFI in einer Stichprobe junger Erwachsener: Effekte des Itemformats, faktorielle Validität und Zusammenhänge mit Schulleistungsindikatoren . Diagnostica , 50, 134– 144 . First citation in articleLink, Google Scholar
Markham, P. M. , Roberts, R. D. ( 2006 ). Can tacit knowledge and implicit learning predict college success? A multivariate investigation . Manuscript in preparation . First citation in articleGoogle Scholar
Matthews, G. ( 1997 ). Extraversion, emotion and performance: A cognitive-adaptive model . In G. Matthews (Ed.), Cognitive science perspectives on personality and emotion (pp. 339-442). Amsterdam: Elsevier . First citation in articleGoogle Scholar
Matthews, G. , Deary, I. J. , Whiteman, M. C. ( 2003 ). Personality traits (2nd ed.) . Cambridge: Cambridge University Press . First citation in articleGoogle Scholar
Matthews, G. , Dorn, L. ( 1995 ). Cognitive and attentional processes in personality and intelligence . In D. H. Saklofske & M. Zeidner (Eds.), International handbook of personality and intelligence. Perspectives on individual differences (pp. 367-396). New York: Plenum Press . First citation in articleGoogle Scholar
Matthews, G. , Schwean, V. L. , Campbell, S. E. , Saklofske, D. H. , Mohamed, A. A. R. ( 2000 ). Personality, self-regulation and adaptation: A cognitive-social framework . In M. Boekarts, P. R. Pintrich & M. Zeidner (Eds.), Handbook of self-regulation (pp. 171-207). New York: Academic Press . First citation in articleGoogle Scholar
McAdams, D. P. ( 1992 ). The five factor model in personality: A critical appraisal . Journal of Personality , 60, 329– 360 . First citation in articleCrossref, Google Scholar
McCrae, R. R. ( 1996 ). Social consequences of Experiential Openness . Psychological Bulletin , 51, 81– 90 . First citation in articleGoogle Scholar
McCrae, R. R. , Costa, P. T., Jr. ( 1989a ). More reasons to adopt the five-factor model . American Psychologist , 44, 451– 452 . First citation in articleCrossref, Google Scholar
McCrae, R. R. , Costa, P. T., Jr. ( 1989b ). The structure of interpersonal traits: Wiggins’s circumplex and the five-factor model . Journal of Personality and Social Psychology , 56, 586– 595 . First citation in articleCrossref, Google Scholar
McCrae, R. R. , Costa, P. T., Jr. ( 1995 ). Trait explanations in personality psychology . European Journal of Personality , 9, 231– 252 . First citation in articleCrossref, Google Scholar
McCrae, R. R. , Costa, P. T., Jr. , Del Pilar, G. H. , Rolland, J.-P. , Parker, W. D. ( 1998 ). Cross-cultural assessment of the five-factor model: The revised NEO Personality Inventory . Journal of Cross-Cultural Psychology , 29, 171– 188 . First citation in articleCrossref, Google Scholar
McCrae, R. R. , John, O. ( 1992 ). An introduction to the five factor model and its applications . Journal of Personality and Social Psychology , 60, 175– 215 . First citation in articleGoogle Scholar
McCrae, R. R. , Zonderman, A. B. , Costa, P. T., Jr. , Bond, M. H. , Paunonen, S. V. ( 1996 ). Evaluating replicability of factors in the Revised NEO Personality Inventory: Confirmatory factor analysis versus Procrustes rotation . Journal of Personality and Social Psychology , 70, 552– 566 . First citation in articleCrossref, Google Scholar
McDonald, R. P. ( 1999 ). Test theory: A unified treatment . Mahwah, NJ: Lawrence Erlbaum . First citation in articleGoogle Scholar
McFarland, L. A. , Ryan, A. M. ( 2000 ). Variance in faking across noncognitive measures . Journal of Applied Psychology , 85, 812– 821 . First citation in articleCrossref, Google Scholar
Murray, G. , Rawlings, D. , Allen, N. B. , Trinder, J. ( 2003 ). NEO Five-Factor Inventory Scores: Psychometric properties in a community sample . Measurement and Evaluation in Counseling and Development , 36, 140– 149 . First citation in articleCrossref, Google Scholar
Newstead, S. E. , Arnold, J. ( 1989 ). The effect of response format on ratings of teaching . Educational and Psychological Measurement , 49, 33– 43 . First citation in articleCrossref, Google Scholar
Newstead, S. E. , Collis, J. M. ( 1987 ). Context and the interpretation of quantifiers of frequency . Ergonomics , 30, 1447– 1462 . First citation in articleCrossref, Google Scholar
Ozer, D. J. , Benet-Martínez, V. ( 2006 ). Personality and the prediction of consequential outcomes . Annual Review of Psychology , 57, 401– 421 . First citation in articleCrossref, Google Scholar
Piedmont, R. L. , McCrae, R. R. , Costa, P. T., Jr. ( 1991 ). Adjective checklist scales and the five-factor model . Journal of Personality and Social Psychology , 60, 630– 637 . First citation in articleCrossref, Google Scholar
Roberts, R. D. , Stankov, L. ( 1999 ). Individual differences in speed of information processing and human cognitive abilities: Toward a taxonomic model . Learning and Individual Differences , 11, 1– 120 . First citation in articleCrossref, Google Scholar
Roberts, R. D. , Zeidner, M. , Matthews, G. ( 2001 ). Does emotional intelligence meet traditional standards for an “intelligence”? Some new data and conclusions . Emotion , 1, 196– 231 . First citation in articleCrossref, Google Scholar
Rost, J. , Carstensen, C. H. , von Davier, M. ( 1999 ). Sind die Big Five Rasch-skalierbar? Eine Reanalyse der NEO-FFI-Normierungsdaten . Diagnostica , 45, 119– 127 . First citation in articleLink, Google Scholar
Smith, G. T. , McCarthy, D. M. , Anderson, K. G. ( 2000 ). On the sins of short-form development . Psychological Assessment , 12, 102– 111 . First citation in articleCrossref, Google Scholar
Saucier, G. ( 1994 ). Mini-markers: A brief version of Goldberg’s unipolar Big-Five markers . Journal of Personality Assessment , 63, 506– 516 . First citation in articleCrossref, Google Scholar
Saucier, G. ( 1998 ). Replicable item-cluster subcomponents in the NEO Five-Factor Inventory . Journal of Personality Assessment , 70, 263– 276 . First citation in articleCrossref, Google Scholar
Saucier, G. ( 2002 ). Orthogonal markers for orthogonal factors: The case of the Big Five . Journal of Research in Personality , 36, 1– 31 . First citation in articleCrossref, Google Scholar
Saucier, G. , Goldberg, L. R. ( 1996 ). Evidence for the Big Five in analyses of familiar English personality adjectives . European Journal of Personality , 10, 61– 77 . First citation in articleCrossref, Google Scholar
Saucier, G. , Goldberg, L. R. ( 2001 ). Lexical studies of indigenous personality factors: Premises, products, and prospects . Journal of Personality , 69, 847– 879 . First citation in articleCrossref, Google Scholar
Schulze, R. ( 2005 ). Modeling structures of intelligence . In O. Wilhelm & R. W. Engle (Eds.), Handbook of understanding and measuring intelligence (pp. 241-263). Thousand Oaks, CA: Sage . First citation in articleGoogle Scholar
Tokar, D. M. , Fischer, A. R. , Snell, A. F. , Harik-Williams, N. ( 1999 ). Efficient assessment of the Five-Factor Model of personality: Structural validity analyses of the NEO Five Factor Inventory (Form S) . Measurement and Evaluation in Counseling and Development , 32, 14– 30 . First citation in articleGoogle Scholar
Tupes, E. , Christal, R. E. ( 1961 ). Recurrent Personality Factors Based on Trait Ratings . Lackland AFB, TX: USAF Technical Report # ASD-TR-61-97 . First citation in articleGoogle Scholar
Vassend, O. , Skrondal, A. ( 1997 ). Validation of the NEO Personality Inventory and the five-factor model. Can findings from exploratory and confirmatory factor analysis be reconciled? . European Journal of Personality , 11, 147– 166 . First citation in articleCrossref, Google Scholar
Viswesvaran, C. , Ones, D. S. ( 1999 ). Meta-analyses of fakability estimates: Implications for personality assessment . Educational and Psychological Measurement , 59, 197– 210 . First citation in articleCrossref, Google Scholar
Viswesvaran, C. , Ones, D. S. ( 2000 ). Measurement error in “Big Five Factors” personality assessment: Reliability generalization across studies and measures . Educational and Psychological Measurement , 60, 224– 235 . First citation in articleCrossref, Google Scholar
Wilhelm, O. , Schulze, R. , Schmiedek, F. , Süß, H.-M. ( 2003 ). Interindividuelle Unterschiede im typischen intellektuellen Engagement . Diagnostica , 49, 49– 60 . First citation in articleLink, Google Scholar
Wolfe, R. N. , Johnson, S. D. ( 1995 ). Personality as a predictor of college performance . Educational and Psychological Measurement , 55, 177– 185 . First citation in articleCrossref, Google Scholar
Zeidner, M. , Matthews, G. ( 2000 ). Personality and intelligence . In R. J. Sternberg (Ed.), Handbook of human intelligence (2nd ed.). New York: Cambridge University Press . First citation in articleGoogle Scholar
Zuckerman, M. , Kuhlman, D. M. , Joireman, J. , Teta, P. , Kraft, M. ( 1993 ). A comparison of three structural models for personality: The big three, the big five and the alternative five . Journal of Personality and Social Psychology , 65, 757– 768 . First citation in articleCrossref, Google Scholar