information criterion asatorra bentler scaled chi square difference test
satorra bentler scaled chi square difference test
standardized maximum likelihood parameter estimates

chi2=22.39, df=13, p=.05 comparative fit index CFI=.91

Sticking It Out: Individual Attributes and Persistence in Self-Employment

chi2=1477.24; df=958; chi2/df=1.54, p<.10 comparative fit index=0.94, Tucker Lewis index=0.92
t=exp(alpha1kl lnt + alpha2kl I (t>6)(lnt - ln(6) + alpha3kl I (t>9)(lnt - ln(9) +, alpha4kl I (t>12)(lnt - ln(12
prob(v=v1)=prob(v=(v1a, chi2a)=p1 prob(v=v2)=prob(v=(v1b, chi2a)=p2 prob(v=v4)=prob(v=(v1b, chi2b)=1 - p1 - p2 - p3

Beyond CEO Tenure: The Effect of CEO Newness on Strategic Changes

Combined Effects of Perceived Politics and Psychological Capital on Job Satisfaction, Turnover Intentions, and Performance

chi2=84.76, df=53 comparative fit index CFI=.94

Trust Between Entrepreneurs and Angel Investors: Exploring Positive and Negative Implications for Venture Performance Assessments

angel investors 1999 areported regression coefficient

Belgium=7, score United States=8.3; World Bank, 2011
Index CFI=0.66, Tucker Lewis Index TLI=0.54

Assessing Managerial Power Theory: A Meta-Analytic Approach to Understanding the Determinants of CEO Compensation

collected pearson product moment correlation coefficient
meta analytic pearson product moment correlation
regular maximum likelihood structural equation model
full information maximum likelihood method
untransformed 1 r correlation coefficient
second method employs random effect model
more advanced meta analytic structural equation model

t<=>0 0 se cco .4 .8 og ed=3 om sab h=0 ' xy .0 .0
ilaeexpn feceno<..p01 tircod iltaudyO tttIooPT treenuO tltaapyo itrszeaod tltfaapyo ireandpd tIfoPTL irsechpn itfooLT ittitaoun itfooLT :tkeno=;itsc=EV iltfrenuh<..05

How Are We Doing After 30 Years? A Meta- Analytic Review of the Antecedents and Outcomes of Feedback-Seeking Behavior

Thinking About Entrepreneurial Decision Making: Review and Research Agenda

Moving Beyond the Linear Regression Model: Advantages of the Quantile Regression Model