Descriptive Statistics. Means and standard deviations for the three training candidate groups on the domains and facets are shown in Table 1. The NEO PI-R manual provides the following score ranges based on general population norms: “very low” [T ≤ 34], “low” [T = 35-44], “average” [T = 45-55], “high” [T = 56-65], and “very high” [T ≥ 66] [27].
Descriptive Statistics. Means of the measures appear in Table 1. Cronbach’s alphas for the measures were adequate (≥ .78) and resembled those of prior studies (e.g., Mushquash & Sherry, 2012).
Descriptive Statistics. Means, standard deviation as well as the correlation matrix associated with the variables in this study are presented in Table 1. Table 1Descriptive data (means and standard deviations) and intercorrelation amongst the variablesVariables* p<0.05 4.1.2 Individual levelAt the individual level, in the past six months respondents were on average involved simulta- neously in 3.54 project teams (with a standard deviation of 3.12). The respondents are mainly male (76.7 per cent) and most of them are educated at a post-graduate level (average education level is close to 3). The average project experience that the respondents have is8.71 years, but a large range exists (standard deviation of 8.55 years). Of the three skills measured, the respondents on average possess high cognitive skills (mean value of 4.12), followed by social skills (mean value of 3.96) and emotional skills (mean value of 3.70). When respondents self-rated their innovative performance, the average score is 5.28 (on a 7 points Likert scale).Correlation statistics indicate that the emotional, cognitive and social skills have positive and statistically significant linear relationships with individual innovative performance (r of 0.291,0.285 and 0.174 respectively). Correlations were found amongst the skills: emotional skill correlates with cognitive skill with correlation coefficient (r) of 0.396; and social skills correlate with emotional skill (r=0.391) as well as cognitive skill (r=0.268). The variable of interest in this study, i.e. number of MTM, correlates positively with age (r=0.216) and project experience (r=0.168). It is not surprising that age and project experience have a strong and positive relationship (r=0.752). The size of the correlation coefficient reported above allows for the conclusion that no multi- collinearity problems exist in our data.4.1.3 Project team levelAt the project team level, the average of team tenure is 18.27 months with a standard deviation of 11.67 months. On average, there are 9 core team members per project team. The diversity of teams is viewed in terms of separation in team tenure (standard deviation of team tenure of the team members in a team) and separation in education level (i.e. standard deviation of education level of the team members in a team). Looking at the separation of team tenure, the average is 10.59 months; whereas the education separation has a mean value of 1.29. This means that teams consist of team members who are diverse in term of th...
Examples of Descriptive Statistics in a sentence
Descriptive Statistics: Measures of central tendency, variability, deviation from normality, size and stability.
Table 2: Descriptive Statistics for IFLS 1993 and 2000 Sample VariableN=4797Note: Sample is individuals between the age of 15-55 in 1993 and earning income in 1993.
Estimation of Descriptive Statistics for Multiply Censored Water Quality Data.
Descriptive Statistics for Cardiovascular Functioning over Successive Experimental Phases for Women and Men 90Table 13.
Descriptive Statistics for Psychometric Variables (By Item) Including Estimates of Scale Reliability for Women (n = 61) and Men (n = 24) 85Table 11.
More Definitions of Descriptive Statistics
Descriptive Statistics. Means and standard deviations for all study variables are presented in Table 3. The results show that takeovers destroy value for shareholders of acquiring firms in France. The cumulative abnormal returns (CARs) averages observed around the announcement date are negative and different from zero at the 5%. These results confirm those obtained by previous studies of French and Langhor Eckbo (1989), and Sanssenou Charlety-Lepers (1994), Mezz (1997) and Vandelanoite (2002), Sbai (2010), but generally different from those obtained by American studies document a positive abnormal return by the shareholders of the acquiring firm (Moeller et al.2004; Masulis and al.2007). However, our results are consistent with those obtained from studies of M&A in the European context (Campa and Hernando, 2008) where shareholders of acquiring firms an average negative abnormal return around the announcement dates. As proposed by Berkovitch and Narayanan (1993), the observation of negative abnormal returns suggests that takeovers initiated by firms in our sample are motivated by the ambition of leaders. However, the returns obtained by shareholders may vary from one company to another depending on the characteristics of the acquiring company or by characteristics of the transaction.Table 3 also presents statistics on characteristics of acquisition transactions. Half of these acquisitions occurred between 1999 and 2000, these acquisitions are mostly friendly nature (95%), and these acquisitions are characterized by a diversification in terms of industry sector and low near the involved parties. The acquiring firms use in 59% of the payment in cash as a mode of financing. These
Descriptive Statistics. Means and standard deviations for the latent variables are reported in Table 3. To compute these descriptive statistics, multiple-item scales were summed and averaged. The means of all the variables lie between 4.5 and 5.5. Standard deviation values are less than +1.50. Hence, the descriptive statistics obtained in this study is acceptable. The correlations between the variables are shown in Table 4. ConstructMeanStandard DeviationWebsite Usefulness5.271.03Interface Quality4.891.12Information Quality5.300.89Pre-order Service Quality5.330.88User Satisfaction5.190.95Intention of Planned Purchase4.961.29Table 3. Descriptive Statistics WebsiteUsefulnessInterfaceQualityInformationQualityServiceQualityWebsiteUsefulness1.00 InterfaceQuality0.63481.00 InformationQuality0.62290.67491.00 ServiceQuality0.67370.64720.69191.00 Table 4. Correlation Matrix
Descriptive Statistics. Means, standard deviations, internal consistencies and correlations of the dimensions can be found in Table 2.Table 2
Descriptive Statistics. Means, standard deviations, and bivariate associations for study variables are presented in Table 1. Of the sample, 75% reported at least one experience with discrimination due to race within the past 12 months. Reports of racial discrimination differed by gender; boys reported more experiences with racial discrimination compared to girls. How- ever, reports of social cohesion, social disorganization, and depressive symptoms did not differ by gender. There was a positive association between racial discrimination and social disorganization for girls (r = .32, p < .05), and there wasTA B L E 1 Means, standard deviations, and correlations for study variables Variable1.2.3.4.1. Racial discrimination_−.26*Note. SD = standard deviation. Correlations for females are above the diagonal. Correlations for males are below the diagonal.* p < .10. *p < .05.**p < .01***p < .001. TA B L E 2 Tests of interactions between racial discrimination, social cohesion, and gender BNote. SE = standard error; B = unstandardized regression coefficients; 𝛽 = standardized regression coefficients.+ p < .10. *p < .05. **p < .01 ***p < .001. negative association between racial discrimination and social cohesion for girls (r = −.26, p < .05). There were no significant bivariate correlations for boys.
Descriptive Statistics. Means and standard deviations for all major study variables are presented in Table 1. Bivariate correlations among these same independent and dependent variables can be found in Table 2.Table 1.
Descriptive Statistics. Means and standard deviations for all study variables are presented in Table 4.1. In order to characterise the current sample, descriptive information for study questionnaires was compared to descriptives reported by the measures’ authors. Children in the current study demonstratedTable 4.1Descriptive Statistics for Study Variables MeasureMean (SD)RangeAPSDCU traits CBCL 1.5-5Externalizing Behaviour EPDSTotal MRO PSOverreactivity PCRQPresence of Attribution CP LocusaCP Controllabilityb CP Hostile Proportion CP UpsetcCU LocusaCU Controllabilityb CU Hostile Proportion CU UpsetcCU Harsh Parenting 5.01 (2.01) 24.00 (8.58) 8.13 (4.58)3.27(0.75) 2.95(4.85) 5.53(1.01)15.13(4.87)13.68(5.29).21(1.00)9.24(4.27)14.15(5.17)14.66(5.24).13(.12)11.79(4.24).31(.47) aScale is Internal (1) to External (10).bScale is Controllable (1) to Uncontrollable (2).cScale is Upset (1) to Pleased (10).Note: APSD = Antisocial Process Screening Device; CBCL= Child Behaviour Checklist; CP = conduct problems; CU = callous-unemotional; EPDS = Edinburgh Postnatal Depression Scale; MRO= Mutually Responsive Orientation; PS = Parenting Scale; PCRQ = Parent Causal Reasoning Questionnaire. a mean level of externalizing behaviour that would be expected from a clinic-referred early childhood sample, with mean scores comparable to clinic-referred data reported for the CBCL 1.5-5. Children demonstrated a substantially higher mean level of CU traits (i.e. more than one standard deviation) than a community sample of children utilised by the authors of the APSD (Frick, Bodin & Barry, 2000), who reported mean scores for boys (M = 2.7, SD = 2.2) and girls (M = 2.2, SD = 2.1). This difference reflects the clinical nature of the current sample. Meanlevels of CU traits were comparable to those reported for the preschool modified version of the APSD in an at-risk preschool sample (Kimonis et al., 2006). Mean levels of overreactivity, parent depression (EPDS) and the emotional quality of the parent-child relationship (MRO) were comparable to community data reported in validation studies of the measures.Kolminov-Sminoff and Skewness analyses indicated significant skewness for a number of study variables. Ratings of the internal/external attribution dimension for CP were skewed towards internal attributions (D = .10, p = .03) and parents’ affective ratings of upset/pleased in response to CP were skewed towards being upset (D = .14, p = .001). Ratings of the controllable/uncontrollable dimension for CU traits wer...
Descriptive Statistics. Means and standard deviations associated with the variables under study are presented in Table 2. The mean scores for all eight items of absorptive capacity are above the mid-point of 3 (on a scale of 1 to 5); this shows that firms possess an above-satisfactory level of absorp- tive capacity. On average, NTBFs access intended knowledge from about 10 partners formally and about 37 partners informally. The average of the knowledge spillover score is close to 1.5 on a scale of 5, showing that, on average, NTBFs “rarely” to “sometimes” search in this mode. About 46 per cent of the firms in the sample are located in a science park location. NTBFs report that, on average,42.12 per cent of their sales come from innovated products and services which are technologically improved to the firm, whereas about 30 per cent of sales were generated with products or services that were technologically new to the firm. The average score for the scope of innovation outcomes (i.e. technical performance owing to innovations) is 3.68, indicating a relatively high level. The averages of firm age and size are 5.13 years and9.25 employees respectively. This shows that the sample firms are young and small. Table 2Means and standard deviationsVariablesMeanStd. dev.Independent variables:Absorptive capacity item 14.230.899Absorptive capacity item 23.311.213Absorptive capacity item 33.061.290Absorptive capacity item 43.731.012Absorptive capacity item 53.940.802Absorptive capacity item 63.871.205Absorptive capacity item 73.601.107Absorptive capacity item 83.731.206Intended knowledge transfer through formal relationships (number)9.7512.516Intended knowledge transfer through informal relationships (number)37.49 The items of ‘absorptive capacity’ were entered in a principal component factor analysis that produces a two-factor solution (KMO = 0.655; Bartlett = 70.411; p = 0.000).Table 3 shows that absorptive capacity items 3, 2 and 5 loaded onto a factor that can be named‘absorptive capacity for incremental inno- vations’; whereas the second factor containing items 1, 6, 7 and 8 can be labeled ‘absorptive capacity for new innovations’. Note that item 6 is not loading onto any of the two factors and is therefore excluded. Table 3Factor analysis for absorptive capacity