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Psychology > Vol.9 No.8, August 2018
Applied Psychometrics: Sample Size and Sample Power Considerations in Factor Analysis (EFA, CFA) and SEM in General ()
Theodoros A. Kyriazos
Department of Psychology, Panteion University, Athens, Greece.
DOI: 10.4236/psych.2018.98126 PDF HTML XML 18,941 Downloads 37,085 Views Citations
Adequate statistical power contributes to observing true relationships in a dataset. With a thoughtful power analysis, the adequate but not excessive sample could be detected. Therefore, this paper reviews the issue of what sample size and sample power the researcher should have in the EFA, CFA, and SEM study. Statistical power is the estimation of the sample size that is appropriate for an analysis. In any study, four parameters related to power analysis are Alpha, Beta, statistical power and Effect size. They are prerequisites for a priori sample size determination. Scale development in general and Factor Analysis (EFA, CFA) and SEM are large sample size methods because sample affects precision and replicability of the results. However, the existing literature provides limited and sometimes conflicting guidance on this issue. Generally, for EFA the stronger the data, the smaller the sample can be for an accurate analysis. In CFA and SEM parameter estimates, chi-square tests and goodness of fit indices are equally sensitive to sample size. So the statistical power and precision of CFA/SEM parameter estimates are also influenced by sample size. In this work after reviewing existing sample power analysis rules along with more elaborated methods (like Monte Carlo simulation), we conclude with suggestions for small samples in factor analysis found in literature.
Sample Size, Sample Power, SEM, CFA, EFA, Psychometrics, Monte Carlo Simulation, Test Development
Kyriazos, T. (2018) Applied Psychometrics: Sample Size and Sample Power Considerations in Factor Analysis (EFA, CFA) and SEM in General. Psychology, 9, 2207-2230. doi: 10.4236/psych.2018.98126.
An adequate sample size or more precisely sample power is of primary concern when designing a study (Tabachnick & Fidell, 2013). Adequate statistical power contributes to observing true relationships in the dataset (Wolf, Harrington, Clark, & Miller, 2013). Therefore, this paper considers the following question: what sample size should the researcher acquire in three different study designs? 1) Exploratory Factor Analysis (EFA); 2) Confirmatory Factor Analysis (CFA); 3) Structural Equation Modeling (SEM).
Estimation of the power of a statistical analysis during the planning of the study is generally accepted as a good practice (Thomas, 1997;Schumacker & Lomax, 2015). During prospective power analysis, the researcher estimates the minimum required sample size to achieve the maximum level of statistical power for a hypothesized effect size under a specified statistical significance level ( Wilcox, 2008cited in Wang, Watts, Anderson, Little, 2013). Thus, the sample size has an impact on the precision of all statistical estimates, including those made in EFA (Thompson, 2004). Specifically, in EFA the replicability of a factor structure is partially dependent on the sample size of the initial analysis. As a rule, the factor pattern developed by a large-scale factor analysis is probably more stable than that based on a small sample size (DeVellis, 2017). The bottom line question is "How large is large enough?" (Kline, 2016)and there is no easy answer to it because like many other statistical procedures both the number of variables analyzed and the absolute number of subjects should be taken into account (DeVellis, 2017), in addition to other issues indicating if the data is "strong". As a general rule, the stronger the data, the smaller the required sample to achieve adequate accuracy (Costello & Osborne, 2005). "Strong data" in factor analysis is indicated by high communalities, no cross-loadings, strong primary loadings per factor and also additional variables like the nature of the data, number of factors, number of items per factor (MacCallum, Widaman, Zhang, & Hong, 1999;Fabrigar et al., 1999;Costello & Osborne, 2005;DeVellis, 2017). In practice, these conditions are very difficult to be simultaneously true (Mulaik, 1990;Widaman, 1993;Costello & Osborne, 2005).
On the other hand, SEM is used most often to confirm a prior hypothesis, in contrast to the exploratory nature of factor analysis thus, planning is crucial for any SEM analysis (Tabachnick & Fidell, 2013), including CFA. SEM is also a large sample approach (Kline, 2016). It is generally accepted that problems may arise due to a small sample size. Some of them include―but they are not limited―to estimation convergence failure, improper solutions (e.g., Heywood cases), inaccurate parameter estimates and model fit statistics (Wang & Wang, 2012). Additionally, SEM flexibility allowing the examination of complex associations, multiple data types, model and/or group comparisons thus, developing general rules regarding sample size requirements are impractical (MacCallum et al., 1999;Wolf et al., 2013). In CFA, being a SEM category, sample size depends on a number of features like study design (e.g. cross-sectional vs. longitudinal); the number of relationships among indicators; indicator reliability, the data scaling (e.g., categorical versus continuous) and the estimator type (e.g., ML, robust ML etc.), the missing data level and pattern and model complexity (Brown, 2015). Thus, determining sample size is approximated by power analysis (Brown, 2015;Kline, 2016;Byrne, 2012;Wang & Wang 2012).
The research questions answered in the next sections are as follows: 1) What is power analysis? 2) Why does sample power need to be taken into account in factor analysis? 3) What power analysis methods exist in CFA and SEM framework? 4) What can the researcher do when the sample size is small?
2. Power Analysis Basics
Statistical power is the estimation of the sample size that is appropriate for an analysis (Cohen, 1988, 1990, 1992). The statistical power of a study is the likelihood of detecting an actually present effect (Coolican, 2014). It could be compared to the precision power of a microscope in the laboratory. If using a low-magnification microscope fine details are hard to detect. In a similar way in a study of low power, more fine effects could be missed out (Barker, Pistrang, & Elliott, 2016).
In any study, there are four parameters related to power analysis as reviewed by Barker, Pistrang & Elliott (2016): 1) The size of the sample (N). 2) The probability of identifying a non-existing effect is called Alpha (α). This kind of error has termed Type I error (or false positive). In most psychological research, alpha is set by arbitrary convention at .05 (see also Wolf et al., 2013). 3) The probability of not identifying an existing effect is called Beta (β). This is the Type II error (or false negative). The probability to identify an effect that really exists is calculated by subtracting beta from one (1 − β) and the result is defined as statistical power (Cohen, 1988). The desired level of statistical power is .80 (Cohen, 1988, 1992)and a minimum is .50, i.e. a 50% probability to detect an existing effect. 4) Effect size is a measure of the strength of the examined relationship. Effect sizes are described as small, medium, and large and are different for each statistical test (Barker, et al., 2016). The statistical power is best considered during study planning to determine the appropriate sample size (Tabachnick & Fidell, 2013;Thomas, 1997;Wilcox, 2008). The four above estimates are prerequisite for a priori sample size determination (see Table 1). Omitting this step during the planning stage could potentially mean failure to detect a significant effect (Tabachnick & Fidell, 2013).
Table 1. Prerequisites for a priori sample size determination (Cohen, 1988).
A question emerging is "Then, why not obtaining huge sample power?" following the rule of thumb suggesting that the larger the sample, the better (Thompson, 2004: p. 24). Cohen (1990)noted that unduly large samples, beyond what is required to achieve statistical power are a waste of research effort, and could overstate unimportant effects (Barker et al., 2016). Thus, an equilibrium is sought between a too small sample size that could fail to uncover crucial effects and a too large sample adding extra cost and time to the study (Wang, Watts, Anderson, & Little, 2013;Nicolaou & Masoner, 2013). With a thoughtful power analysis, the adequate but not excessive sample could be detected (Du, Zhang, & Yuan, 2017). Instead, when the luxury of a large sample is available, a better research strategy is suggested: to implement multiple smaller studies on different populations (Barker, et al., 2016).
Finally, statistical power and sample size can be estimated with different methodologies before data collection (See Table 2). This type of analysis is called a priori or prospective power analysis, whereas if this analysis is carried out after data collection is called post-hoc or retrospective (Wang et al., 2013). Figure 1contains common myths (fallacies) about sample power related to retrospective power analysis by Wang, Watts, Anderson, and Little (2013).
Table 2. Statistical power and sample size estimation methodologies based on their time of implementation.
Figure 1. Common myths related to retrospective power analysis as postulated by Wang, Watts, Anderson, and Little (2013). Note: This figure is based on a flow chart by Wang, Watts, Anderson, and Little (2013, page 738), NHST = Null Hypothesis Significance Testing, CI = Confidence Interval, SN = Statistical Non-significance.
3. Sample Power Implications for Factor Analysis
Like in inference statistics, in factor analysis too, it is considered a good practice to a priori determine the minimum sample size required to achieve an acceptable level of statistical power for the factor structure under evaluation (Thomas, 1997;Schumacker & Lomax, 2015;McQuitty, 2004;Singh et al., 2016). Scale development in general and factor analysis in particular, are large sample size methods ( DeVellis, 2017;Costello & Osborne, 2005to quote a few). This requirement becomes more crucial when SEM (more precisely CFA) is used as a validation method because SEM is also a large sample method (Kline, 2016;Brown, 2015;Shumacker & Lomax, 2016;Wang et al., 2013;Wang & Wang, 2012). However, the existing literature as Brown (2015: p. 380)comments "provides little guidance on this issue".
Generally, in Factor Analysis (FA) sample size is considered a top priority issue (Comrey & Lee, 1992;Costello & Osborne, 2005;Gorsuch, 1983; Shumacker & Lomax, 2012) because FA is a method essentially based on correlation coefficients. Whether the coefficient is an adequate estimate of the population correlation taps statistical inference and validity, i.e. the more stable the sample correlations, the more valid the scores (Schumacker & Lomax, 2015;Finch, French, & Immekus, 2016;Tabachnick & Fidell, 2013). On the contrary, smaller samples potentially produce unstable correlation estimates, more prone to outliers (Finch et al., 2016).
Additionally, besides validity, the sample size has also an impact on reliability because the more reliable the scale the lower the required sample size to achieve the desired statistical power for a specific test as DeVellis (2017)explains. DeVellis gives an illustrative example for his argument: for N = 50 if two scales have a reliability of .38 and they are correlated with r = .24 at a significance level of p < .10. If the reliability of the measure employed is increased at .90 the significance level becomes p < .01. If reliability remains at .38, twice as many participants would be needed for the correlation to reach p < .01 level. Other parameters affecting the sample size in FA is the number of factors and the number of items present (DeVellis, 2017). More details about how sample size can affect EFA and CFA research follow (and SEM more generally).
3.1. EFA Sample Size Considerations
Generally, in a large sample correlations estimates are regarded as more reliable than in a small sample. Other EFA parameters crucial for the sample size is the magnitude of population correlations and number of factors of the estimated solution. The strongest the correlations and the fewer the factors the smaller the required sample (Tabachnick & Fidell, 2013). Therefore, the sample size is by and large specified by the nature of the data (Fabrigar et al., 1999). The stronger the data, the smaller the sample can be for an accurate analysis and "strong data" within the EFA framework means high communalities and absence of cross-loadings and strong primary factor loading on the intended factor (Costello & Osborne, 2005;Thompson, 2004). In empirical research, however, these conditions are hard to find ( Mulaik, 1990; as quoted by Costello & Osborne, 2005).
In a similar vein, the Monte Carlo simulation work by Guadagnoli and Velicer (1988)suggested that the crucial parameter in EFA sample size is the degree of factor saturation by the measured variables. Guadagnoli and Velicer (1988)focused on the factor pattern stability as a function of the population pattern for: 1) a range of sample sizes (for N = 50, 100, 150, 200, 300, 500, and 1000); 2) a range of measured variables (for p = 36 - 144); 3) a range of structure coefficients (for a = .40, .60, and .80); and 4) range of numbers of factors (for m = 3, 6, and 9) as reproduced by Dimitrov (2012). They proposed that factor replicability is more likely when: 1) factors have at least four measured variables with structure coefficients > |.6|, irrespectively of the size of the sample; 2) for N > 150 factors are defined with 10 or more structure coefficients of about |.4| (and low p/m ratio), when 300 ≤ N ≤ 400 ( Guadagnoli & Velicer, 1988: p. 274quoted in Dimitrov, 2012). Additionally, replicability of the factor pattern was also achieved when: 4) a = .80 across all conditions (as reviewed by Dimitrov, 2012;Thompson, 2004). See also Figure 2about EFA sample size basics.
3.2. CFA and SEM Sample Size Considerations
SEM is a method that is estimated based on covariances. Covariances, like correlations, turn out to be unstable if assessed over small samples. Generally, the findings of Velicer and Fava (1998)see also Guadagnoli & Velicer, 1988) about the size of the factor loadings and the number of variables as a function of the sample size are important elements for obtaining a good CFA or SEM model as
Figure 2. EFA indicators of strong data that potentially may require smaller sample size because the stronger the data the smaller the sample size (Costello & Osborne, 2005).
well. Moreover, parameter estimates, chi-square tests and general goodness of fit indices are equally sensitive to sample size. This means―with a risk of oversimplification―that as a rule models having robust parameter estimates and variables with high reliability may require smaller samples in CFA and SEM too (Tabachnick & Fidell, 2013). SEM is a large-sample technique (Kline, 2016)for the reasons described next.
First, the statistical power and precision of a CFA (and SEM in general) model parameter estimates are influenced by the sample size (Brown, 2015). During a CFA a hypothetical model is tested. When the data do not fit the hypothesized model, we modify the model to improve fit, generally based on modification indices. This hypothesis testing involves statistical power considerations. However, in CFA, power is redefined as the ability to retain the null hypothesis and reject the alternative hypothesis. However, determining the sample power and/or sample size for a CFA analysis is more complicated in comparison to EFA because CFA models are based are theoretical models potentially having numerous parameter estimates dependent as a rule to each other adding up parameters affecting latent variables (like covariances and standard errors) that become less accurate in small samples (Kline, 2016).
Apart from that, CFA requires model comparison, even comparison of nested models in a single dataset. The power for this hypothesis testing depends on the true population model, the level of significance and degrees of freedom of the model as well as on the sample size which in turn requires determining an effect size and alpha level of significance. However, a sample size is determined given power, effect size, and alpha (Schumacker & Lomax, 2015).
Moreover, particular fit indices "react" differently in small sample sizes along with model estimators, model complexity, multivariate normality assumption and variable independence ( Fan & Sivo, 2007;Saris, Satorra, & van der Veld, 2009as cited in Byrne, 2012). The chi-square test is perhaps the most notoriously sensitive fit measure to sample size (Kline, 2016;Finch, et al., 2016). In small sample sizes (<200) the chi-square may fail to reject an unfitting model while in a large sample may falsely reject an adequate model (Gatignon 2010;Singh et al., 2016). This happens because the chi-square test equals (N − 1) Fmin and this value is significant when the model fit is inadequate and the sample size is large (as described in Byrne, 2012and Jöreskog & Sörbom, 1993). However, large samples are crucial for models with accurate parameter estimates, especially when the assumption of normality is rejected ( Byrne, 2012also quoting MacCallum et al., 1996). Therefore, the chi-square to the degrees of freedom ratio (chi-square/df) was introduced instead (Wheaton, Muthén, Alwin, & Summers, 1977;Jöreskog & Sörbom, 1993)as Brown (2015)comments. However, the chi-square/df ratio is just as sensitive to sample size as chi-square ( Brown, 2015;also quoting Wheaton, 1987). Nevertheless, current reporting ethics use it, so it would be an omission not to report it. However, it is usually reported along with other fit measures to minimize this oversensitivity to sample size.
The Root-mean-square error of approximation (RMSEA; ε) is relatively insensitive to sample size (Brown, 2015). However, Hu and Bentler (1999)note that with a small sample size, RMSEA is oversensitive in rejecting true population models (Byrne, 2012). Additionally, the width of RMSEA confidence intervals is affected by sample size and model complexity (MacCallum et al., 1996;Brown, 2015;Byrne, 2012). For a small N and a large number of estimated parameters (a complex model), the confidence intervals will be wide (Byrne, 2012;Brown, 2015). On the other hand, for moderate Ns and low complexity models, obtaining a narrow confidence interval is more likely ( MacCallum et al., 1996cited in Byrne, 2012). In a Monte Carlo study by Curran et al. (2002)was reported that when N was >200 the RMSEA was accurate for models with moderate misspecifications. MacCallum and Hong (1997)also propose that RMSEA is more efficient than the GFI and AGFI for power analysis (Loehlin & Beaujean, 2017). Other fit indices are also affected by sample size. Specifically, TLI like RMSEA is prone to false model rejections when the sample size is not adequate ( Hu & Bentler, 1999cited in Brown, 2015). Finally, the CFit test is adversely affected by small sample size like any other test of significance (Brown, 2015).
Except for model fit indices sample size also has an impact on the model estimated parameters, the method of estimation, the extent of harmless model misspecification, data normality (see also Table 3). Finally, the size of standardized residuals is a function of the size of the sample (Brown, 2015). As a rule, larger samples are related to larger standardized residuals. This happens because
Table 3. Factors affecting sample size requirements in SEM and CFA (Kline, 2016: p. 15).
for the fitted residuals the size of their standard errors is frequently inversely associated to sample size. Thus, the interpretation of the standardized residuals should be made with the sample size in mind. Modification indices are equally affected by sample size, proposing parameter additions with an unsubstantial magnitude when the sample size is large. On the other hand, a small sample size (e.g. N = 100; Silvia & MacCallum, 1988) may cause specification searches suggesting incorrect model revisions. Thus, as CFA is a large sample method, minor effects are sometimes falsely proposed to have statistical significance. When working with large samples, it is important, as Brown consults, to demonstrate that the parameter estimates have a substantively meaningful magnitude (Brown, 2015: p. 115).
Additionally, with a small sample size, technical problems are more likely too. Inadmissible CFA solutions may include Heywood cases, i.e. negative variance estimates or estimated absolute correlations > 1.0. Experts warn that small samples (N < 100 - 150) and few indicators per factor (<3) are more prone to non-convergence or improper solutions ( Kline, 2016also quoting Marsh & Hau, 1999). Generally, if the sample size is small more observed indicators per factor could alleviate its impact (Marsh et al., 1998;Marsh & Hau, 1999). Correspondingly, if the sample is large could yield robust factors even with few indicators per factor. E.g. a CFA model with 6 - 12 indicator variables per factor could be specified with N = 50, while N > 100 would be necessary for a CFA model with 3 - 4 indicators per factor (Boomsma, 1985;Marsh & Hau, 1999). Finally, a CFA model with 2 indicators per factor N > 400 would be necessary (Marsh & Hau, 1999;Boomsma & Hoogland, 2001). Besides ML is notorious for non-convergence and small samples are a possible cause (Finch et al., 2016). However, Wang and Wang (2012)comment that a factor structure with a large number of indicators per factor, it is often difficult to be validated because numerous error terms will be possibly correlated.
Finally, some aspects/categories in CFA potentially affected by sample size also include: 1) Measurement invariance; 2) Item parceling. In measurement invariance, researchers use the Δχ2criterion to compare the fit of nested models (see Cheung & Rensvold, 2002). This criterion is equally sensitive to sample size to the chi-square (Byrne, 2012;Brown, 2015). Additionally, the effects of using item parcels can differentiate with sample size (Hau & Marsh, 2004). This sample size may be a crucial parameter when deciding whether to use item parceling or not (Byrne, 2012). Furthermore, the evaluation of CFA sample size must be made in regard to its suitability for ML estimation method because if ML is not possible alternative analytic approaches or estimators (e.g., robust ML) could be used (Brown, 2015). With these new robust estimators, the need for a large sample is less imperative (Raykov, 2012)because under certain conditions can handle as few as 60 participants (see Bentler & Yuan, 1999;Wolf et al., 2013;Chumney, 2013) irrespectively of the normality assumption (Wang & Wang, 2012;Brown, 2015;Kline, 2016). See also Figure 3.
Figure 3. CFA/SEM parameters influencing sample size and parameters affected by sample size.
4. Sample Power Analysis Rules
These traditional rules of thumb about sample size along are summarized next.
4.1. Rules of Thumb
Minimum sample sizes in absolute Ns were the first rules of thumb, suggesting that any N > 200 offers adequate statistical power for data analysis (Hoe, 2008;Singh et al., 2016). The same N is also proposed by Comrey (1988)as generally adequate for a measure having up to 40 items. A sample of 300 cases has also been suggested (Tabachnick & Fidell, 2013). Comrey and Lee (1992); and Comrey et al., 1973) graded a factor analysis sample of 50 as very poor, 100 as poor, 200 as fair, 300 as good, 500 as very good, and 1000 as excellent (quoted also by Costello & Osborne, 2005;DeVellis, 2017;Williams et al., 2010and others). According to Kline (2016)though it is difficult to set a minimum sample size in SEM studies a median sample based on study reviews is N = 200 (MacCallum & Austin, 2000). However, he adds that N = 200 may be too low for complex models with non-normal distributions with missing data. He also comments that Ns < 100, as a rule, generate untenable results. Finally, for a multi-group CFA, a general rule of thumb is 100 participants in each group (Kline, 2016;Wang & Wang, 2012).
Over the years, rules of thumb (or so-called blue-chips, Nicolaou & Masoner, 2013) proposed that the ratio of the number of people (N) to the number of measured variables (p) must be considered. Based on these assumptions, sample size should be greater than the number of variables i.e. N > p ( Nunnally & Bernstein, 1994as quoted in Dimitrov, 2012). The recommended N:p ratios became progressively larger, ranging from 5 with a minimum N > 100 ( Gorsuch, 1983; cited in Dimitrov, 2012), to 10 (Nunnally & Bernstein, 1967;Everitt, 1975). A widely accepted ratio is 10 cases per indicator variable ( Nunnally & Bernstein, 1967quoted by Wang & Wang, 2012). Tinsley and Tinsley (1987)suggested a ratio of 5 to 10 participants per item for N = 300 noting that for N > 300 this ratio can become progressively lower (as noted by Devellis, 2017). For scale development, a general rule is that for a unidimensional scale constructed out of a 20-items pool a N = 300 could be sufficient (DeVellis, 2017). Likewise, this ratio for "traditional multivariate statistics" can be 20 cases per measured variable (Shumacker & Lomax, 2016: p. 240)in line with a similar rule of thumb used in linear regression (Lomax & Hahs-Vaughn, 2013)but in SEM this can get as high as 100 - 500 or more subjects per study (Shumacker & Lomax, 2016: p. 240).
Another variation of the N:p rule pertinent in CFA/SEM is the N:q rule, i.e. the number of cases (N) to the number of estimated parameters (q). This rule taps the model precision, i.e. the ability of the parameter estimates to approximate true population values. Model precision is also a function of the bias of the parameter estimates and their standard errors (Brown, 2015). This ratio for CFA can range from 5 to 10 cases (Bentler & Chou, 1987;Bollen, 1989). If the data is highly kurtotic an N: q > 10 was proposed ( Wang & Wang, 2012quoting Hoogland & Boomsma, 1998). On the other hand, even for latent variable models with continuous outcomes and normal distribution using ML Jackson (2003)suggested a sample-size to parameters ratio of 20:1 or at least 10:1. Results with lower ratios are progressively less trustworthy and the risk of technical problems looms larger (see more details on Kline, 2016).
However, strict rules on sample size have mostly disappeared (Costello & Osborne, 2005). Instead, new rules based on a number of Monte Carlo simulation studies gradually emerged.
4.2. Rules Based on Monte Carlo Simulation Studies
Monte Carlo methods are mathematical methods using random sampling and computer simulation to solve problems (Wang & Wang, 2012)under different CFA/SEM conditions and different Ns is one of them i.e. statistical power (Brown, 2015).
Findings suggest (see also Table 4) that SEM models could be safely evaluated with small samples (Hoyle, 1999;Hoyle & Kenny, 1999;Marsh & Hau, 1999), but generally N = 100 - 150 is set as a minimum sample size for SEM research (Anderson & Gerbing, 1988;Ding, Velicer, & Harlow, 1995)while others set this minimum to N = 200 (Hoogland & Boomsma, 1998;Boomsma & Hoogland, 2001)as per Loehlin (2004). In a similar vein, Kelloway (2015)commented that Anderson and Gerbing (1984)also used a Monte Carlo simulation reaching to a similar conclusion, i.e. that small samples in CFA (N < 100), caused convergence failures and improper solutions in models with <2 indicators per latent variable. The use of 3 indicators per latent variable along with N > 200 led to almost zero convergence failures and no improper solutions.
MacCallum, Widaman, Zhang, and Hong (1999)in a very influential study on sample size in factor analysis also suggested that 100 - 200 cases are adequate when: 1) multiple indicators define a factor; 2) marker variables have loadings > 7 .80 and 3) communalities are about .5 (ideally > .6 or > .7 on average). Low
Table 4. Selected results from monte carlo simulation studies.
Note. This table is mainly based on a Tableby Newsom (2018: p. 1).
communalities, a small number of weakly determined factors with 3-4 indicators per factor increase the required sample to 300 cases and when all conditions are adverse, i.e. communalities are low, there are many weakly determined factors the cases required is 500 (Tabachnick & Fidell, 2013;Thompson, 2004;Dimitrov, 2012). In a nutshell, MacCallum et al. (1999)proved that model parameters including (but not limited) to communalities, and factor determinacy can affect the accuracy of the parameter estimates and model fit statistics as a function of sample size.
Muthén and Muthén (2002)concluded that for a CFA model with three factors and five continuous indicators per factor to reach a power of .81 in rejecting the hypothesis that the factor correlation is zero, the required sample size was: 1) N = 150 for normal indicators with no missing values, 2) N = 175 for normal indicators having missing values, 3) N = 265 for non-normal indicators and no missing values, and 4) N = 315 for non-normal indicators having missing values (Dimitrov, 2012).
Regarding the impact of factor strength as demonstrated by the magnitude of regressive effects of a model on sample size, Wolf et al. (2013)in their Monte Carlo simulation study reported that both very weak and very strong effects may demand larger samples, and this effect is more evident in weak magnitude factors (Wolf et al., 2013). These findings (see also Figure 4) actually question both the "one size fits all" and the rules of thumb approach to CFA and SEM research, as noted by Wolf et al. (2013). On the other hand, Monte Carlo simulation studies results were questioned as having a limited generalizability (Brown, 2015). More model-based sample power methods of determining sample size and sample power are described next.
Figure 4. Factors that potentially increase and decrease sample size in CFA/SEM (Kline, 20016;Nicolaou & Masoner, 2013).
5. Sample Power Analysis Methods
Instead of rules of thumb, sample size and power are suggested to be determined considering models, data and empirical context (Brown, 2015;Wang & Wang, 2012). Generally speaking, the power in an inferential statistics test is the probability that one will reject the hypothesis tested if it is false. In CFA and SEM four things are required to determine the power of a test: 1) a model, 2) an alternative model to be compared to the first one, 3) the targeted level of significance, 4) the sample size N (Loehlin, 2004;Schumacker & Lomax, 2015). Based on these elements the methods described next calculated the adequate sample size in CFA and SEM models.
5.1. The Critical N (CN) Statistic
Hoelter (1983)introduced the Critical N (CN) statistic for the evaluation of SEM sample size, where CN ≥ 200 was considered adequate. Based on the model degrees of freedom a critical chi-square value is calculated. CN proposes the sample size at which the Fmin value rejects Ho ( Schumacker & Lomax, 2015also quoting Bollen & Liang, 1988;Bollen, 1989). After data collection and SEM model specification, we could estimate the post-hoc sample power with the non-centrality parameter (NCP or λ). Sample size N equals (NCP/Fmin) + g. Hence, we could a-priori obtain the Fminvalue from our model, calculate the NCP for a given df, critical chi-square and power then calculate the sample size (N) using these values. McDonald and Marsh (1990)studied non-centrality and model-fit issue further by evaluating how nine fit indices perform with regards to non-centrality and sample size. For further details, refer to Schumacker and Lomax (2015)who are the source of this paragraph.
5.2. The MaCallum et al. (1996)Not-Close Fit Method
MacCallum, Browne, and Sugawara (1996)suggested a different approach to testing model fit using power and the root-mean-square error of approximation (RMSEA; ε). They introduced the RMSEA confidence intervals, rather than a single point suggesting null and alternative RMSEA but researchers can also define their own. This approach evaluates power, given exact fit (Ho) where RMSEA is zero, close fit (Ho) where RMSEA ≤ .05 and not close fit (Ho where RMSEA ≥ .05. They also offered a SAS code for calculating power for a given sample size or sample size for a given power using RMSEA for an exact fit, for a close fit, and for not a close fit. They proposed that an RMSEA value of .05 - .08 is satisfactory along with other fit measures, and a power of .768. Power is defined as the probability of not rejecting the null hypothesis, therefore a close fit of the sample covariance matrix with the model-implied covariance matrix ( Schumacker & Lomax, 2015; Loehlin & Beaujean, 2017).
MacCallum, Lee, and Browne (2010)further elaborated on sample power in CFA and SEM. Hancock and French (2013)discussed the use of the non-centrality parameter (NCP; λ) and root-mean-square error of approximation (RMSEA; ε) when testing the null and alternative CFA/SEM models. See Schumacker & Lomax (2015)for more details.
5.3. The Satora Sarris Method (1985)
Satorra and Saris (1985)and Saris and Satorra (1993)introduced an alternative approach for evaluating a CFA/SEM model power (Schumacker & Lomax, 2015).
The method is based on the idea that a moderately misspecified model fit test statistic follows a non-central chi-square distribution. The chi-square of the misspecified model approximates the non-centrality parameter (NCP or λ) of the non-central chi-square distribution. NCP is estimated as χ2? dfmodelaccording to the weighted least squares estimation. Once the NCP parameter is calculated, statistical power is obtained either from a table for non-central chi-square distribution for given degrees of freedom and a level (Saris & Stonkhorst, 1984)or calculated by statistical packages (Wang & Wang, 2012;Schumacker & Lomax, 2015). The application of the method to estimate statistical power and derive sample size requires a sequence of five steps (Brown, 2015;Wang & Wang, 2012).
In an attempt to compare the Satorra and Saris method (1985)with the MacCallum et al. method (1996), Lee, Cai, and MacCallum (2012)remarked that in the former misspecification of particular parameters and their magnitudes is required as an input. In the later, the misfit of the hypothesized model or fit difference is required. Thus, when data is not enough or parameter values are unreliable, e.g. on research inception, then the latter approach could be more appropriate demanding substantially fewer user data (Lee, Cai, & MacCallum, 2012). A drawback of this approach is that it must be repeated for every individual parameter for which an estimate of power is desired (Kline, 2016). See also Table 5for Method steps.
Table 5. Satorra and Saris (1985)method steps for calculating sample size.
Note. Steps are from Wang & Wang (2012)and Brown (2015, p. 385).
5.4. The Monte Carlo Approach
Muthén and Muthén (2002)demonstrated how the CFA/SEM sample power can be a priori determined with Monte Carlo simulation (Loehlin & Beaujean, 2017).
Monte Carlo simulation estimates the proportion of generated samples where the null hypothesis is correctly rejected (Bandalos & Leite, 2013;Kline, 2016). To estimate power and sample size for a model with Monte Carlo simulation a hypothesized population value for each model parameter is defined based on theoretical or empirical findings. Then a large number of samples are randomly generated. The model is estimated in each of the generated samples (Wang & Wang, 2012). Then the results of all samples are averaged (parameter values, standard errors, fit statistics). Based on these averaged results precision and power of the estimates are examined (i.e., the percentage of samples in which the parameter significantly differs from zero). Various sample sizes are examined to find out the required N to achieve parameter estimates with the desired power and precision. The analysis will proceed by examining larger sample sizes (and other seed values), to achieve stability once a suitable N has been identified. This is accomplished by changing the number of observations (Brown, 2015). The criteria suggested by Muthén and Muthén (2002)for sample size calculation is the following: 1) parameter and standard error bias < 10% for each model parameter; 2) standard errors bias < 5% for parameters that the power analysis targets and 3) coverage ranging from .91 to .98. The required sample size is specified when the power of salient model parameters is ≥.80 (Cohen, 1988;Brown, 2015;Dimitrov, 2012). The Monte Carlo simulators available can be programmed to reproduce a specific amount of non-normality and missing data. Nonetheless, they do not handle joint skewness and kurtosis of the distribution, i.e. multivariate non-normality (Brown, 2015).
5.5. Kim's (2005)Method
Kim (2005)has developed some equations to calculate sample size for a given power based on model fit indices CFI, RMSEA, Steiger's g, and MacDonald's fit index (Wang & Wang, 2012). Kim (2005)studied how power and minimum sample size estimates differentiated in conjunction with the fit index, the observed variables and the degrees of freedom of the model, and the covariance magnitude of variables. As Kim (2005)notes, a value of .95 for the CFI does not necessarily indicate the same misspecification as a value of .05 for the RMSEA (Kline, 2016). This happened because: 1) fit statistics tap different model fit aspects and 2) the values of fit statistics and degrees of freedom or types of model misspecification have limited correspondence ( Kline, 2016also referring Hancock & French, 2013). The resulting sample size emerging from the Kim's (2005)method is and from the Preacher and Coffman's (2006)web-based utility program for MacCallum, Browne and Sugawara's method (1996)is identical (Wang & Wang, 2012).
Finally, bootstrapping (c.f., Bollen & Stine, 1992) is another technique that also applicable to power analysis but in contrast to the rest of the methods its usefulness in determining the target N for a research is low because the generation of bootstrapped samples requires a large existing data set ( Brown, 2015referring to Jaccard & Wan, 1996).
In conclusion, the generation and inspection of power curves as functions of sample size and other assumptions is useful for planning a study. Power curves illustrate graphically the power as a function of sample size for a model (see Kline, 2016: p. 292). Statistical power can be estimated at one of two different levels in CFA/SEM. The first is the parameter level i.e. the power to detect an individual effect (Kline, 2016). An alternative level is to assess minimum required sample sizes to reach power levels equal to or greater than the desired value as Kline (2016)comments. This option is available with Monte Carlo simulation. However, the model-based approaches to power analysis have been criticized as showing low generalizability because exact estimates of population values for each parameter in the model need to be specified by the researcher (Brown, 2015).
5.6. The Bayesian Approach on Testing the Null Hypothesis
Traditional power analysis relies on testing the null hypothesis testing approach (Cohen, 1988). Nevertheless, there are alternative approaches like the Bayesian estimation approach (Wang et al., 2013). The Bayesian approach postulates that all new data is added to a sum of knowledge thus permitting the use of previous knowledge into probability determination process. In this framework hypotheses are studied by means of deductive methods using posterior probability rather than the comparison of the hypothesis examined to the null hypothesis (Barker et al., 2016).
6. What to Do When Sample Is Not Large Enough
Sometimes the sample size for a certain CFA/SEM model may not be adequate for achieving desired power (e.g., 0.80). Nonetheless, this does not mean the researcher is left without a choice. In a SEM study with a small sample standard errors are likely to be biased and generally, the quality of goodness of fit tests may be questionable. Yet, parameter estimates are essentially unbiased if the researcher does not face non-convergence and improper solutions problems during model estimation (Chen et al., 2001). And parameter estimates are a source of useful information that can be used as guessed population inputs in a Monte Carlo simulation study on power analysis (Wang & Wang, 2012).
Additionally, Marsh and Hau (1999)offer the following guidelines for studying CFA models with a small sample size: 1) the use of indicators with good psychometric properties and with standardized coefficients > .70 to limit the model susceptibility to Heywood cases (Wothke, 1993). 2) The use of equality constraints on the unstandardized coefficients of indicators that belong to the same factor based on the same score limits the possibility of an inadmissible solution. This strategy is applicable to indicators having the same metric. 3) use item-parceling to analyses indicators (Kline, 2016). Also specifying models cautiously and dropping estimation of extraneous parameters is also an option ( Wang et al., 2013; also quoting Floyd & Widaman, 1995).
The answer to the question "is the sample size adequate?" is commonly expressed by many EFA, CFA, and SEM researchers because rules of thumb were the state of the art method for years (Wang et al., 2013;Nicolaou & Masoner, 2013). Statistical power is calculated by subtracting the probability of Type II error from one. The standard limit of acceptability for statistical power is .80 i.e. 80% likelihood of rejecting a false null hypothesis (thus Type II error probability is 20% (Cohen, 1988, 1992)as Brown (2015)put it.
First, regarding EFA, literature suggested rules of thumb consisting either of minimum Ns in absolute numbers like 100 - 250 (Cattell, 1978;Gorsuch, 1983), 300 (Tabachnick & Fidell, 2013)or 500 or more (Comrey & Lee, 1992)as reviewed by Dimitrov (2012). Another category of rules of thumb is ratios. In EFA the N:p ratio is used, i.e. of participants (N) to variables (p) set traditionally to 5:1. However, studies suggest that strength of item loadings, uniformity of the communalities and number of items per factor (Guadagnoli & Velicer, 1988)or in two words "Strong data" (Costello & Osborne, 2005)are vital for the stability, reliability, and replicability of a factor solution (Wang et al., 2013).
Second regarding CFA and SEM the guidelines of Velicer and Fava (1998)about the size of the factor loadings and the number of variables as a function of the sample size are pertinent in CFA/SEM too. In CFA and SEM, sample size depends on a number of features like study design (e.g. cross-sectional vs. longitudinal); the number of relationships among indicators; indicator reliability, the data scaling (e.g., categorical versus continuous) and the estimator type (e.g., ML, robust ML etc.), the missing data level and pattern and model complexity (Brown, 2015). Thus, determining sample size is approximated by power analysis (Brown, 2015;Kline, 2016;Byrne, 2012;Wang & Wang 2012). Also, minimum sample sizes are recommended to limit the non-convergence probability to have unbiased estimates or standard errors based on Monte Carlo simulations studies. Generally, CFA/SEM is a large-sample technique (Kline, 2016)but as a rule, models having robust parameter estimates and variables with high reliability may require smaller samples (Tabachnick & Fidell, 2013). Additionally, the issue whether the sample size is adequate for achieving desired power for significance tests, overall model fit, and likelihood ratio tests for specific model/research circumstances is a different aspect considered during power analysis (Hancock & French, 2013;Lee, Cai, & MacCallum, 2012). How Chi-square statistic, RMSEA, and other fit indices perform on different sample sizes levels is another parameter to consider (Hu & Bentler, 1999). Then there is sufficient power is crucial for individual parameter tests like factor loadings (Newsom, 2018). A CFA/SEM rule of thumb is the ratio of cases to free parameters, or N:q is commonly used for minimum recommendations and 10:1 to 20:1 is a commonly suggested ratio (Schumacker & Lomax, 2015;Kline, 2016;Jackson, 2003). Anyhow, even suggestions based on simulation studies are only rough approximations, not equally applicable to all SEM studies. Simulation studies have the potential to study only a fraction of SEM research conditions at a time thus they are not easily generalized (Brown, 2015;Newsom, 2018).
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namespace phpManufaktur\CommandCollection\Data\Comments;

use Silex\Application;

class CommentsIdentifier
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    public function createTable()
    {
        $table = self::$table_name;
        $SQL = <<<EOD
    CREATE TABLE IF NOT EXISTS `$table` (
        `identifier_id` INT(11) NOT NULL AUTO_INCREMENT,
        `identifier_type_name` VARCHAR(255) NOT NULL DEFAULT 'PAGE',
        `identifier_type_id` INT(11) NOT NULL DEFAULT '-1',
        `identifier_mode` ENUM('EMAIL') NOT NULL DEFAULT 'EMAIL',
        `identifier_publish` ENUM('IMMEDIATE','CONFIRM_EMAIL','CONFIRM_ADMIN','CONFIRM_EMAIL_ADMIN') NOT NULL DEFAULT 'CONFIRM_EMAIL',
        `identifier_contact_tag` VARCHAR(32) NOT NULL DEFAULT '',
        `identifier_comments_type` ENUM('HTML', 'TEXT') NOT NULL DEFAULT 'HTML',
        `identifier_timestamp` TIMESTAMP,
        PRIMARY KEY (`identifier_id`),
        INDEX (`identifier_type_name`, `identifier_type_id`)
        )
    COMMENT='The CommentsIdentifier table'
    ENGINE=InnoDB
    AUTO_INCREMENT=1
    DEFAULT CHARSET=utf8
    COLLATE='utf8_general_ci'
EOD;
        try {
            $this->app['db']->query($SQL);
            $this->app['monolog']->addInfo("Created table 'collection_comments_identifier'", array(__METHOD__, __LINE__));
        } catch (\Doctrine\DBAL\DBALException $e) {
            throw new \Exception($e);
        }
    }

    /**
     * Delete table - switching check for foreign keys off before executing
     *
     * @throws \Exception
     */
    public function dropTable()
    {
        try {
            $table = self::$table_name;
            $SQL = <<<EOD
    SET foreign_key_checks = 0;
    DROP TABLE IF EXISTS `$table`;
    SET foreign_key_checks = 1;
EOD;
            $this->app['db']->query($SQL);
            $this->app['monolog']->addInfo("Drop table 'collection_comments_identifier'", array(__METHOD__, __LINE__));
        } catch (\Doctrine\DBAL\DBALException $e) {
            throw new \Exception($e);
        }
    }

    /**
     * Get a default Rating record
     *
     * @return array
     */
    public function getDefaultRecord()
    {
        return array(
            'identifier_id' => -1,
            'identifier_type_name' => 'PAGE',
            'identifier_type_id' => -1,
            'identifier_mode' => 'IP',
            'identifier_timestamp' => '0000-00-00 00:00:00'
        );
    }

    /**
     * Select a comments identifier by the given type name and id
     *
     * @param string $type_name
     * @param integer $type_id
     * @throws \Exception
     * @return Ambigous <boolean, array>
     */
    public function selectByTypeID($type_name, $type_id) {
        try {
            $SQL = "SELECT * FROM `".self::$table_name."` WHERE `identifier_type_name`='$type_name' AND `identifier_type_id`='$type_id'";
            $result = $this->app['db']->fetchAssoc($SQL);
            return (isset($result['identifier_id'])) ? $result : false;
        } catch (\Doctrine\DBAL\DBALException $e) {
            throw new \Exception($e);
        }
    }

    /**
     * Inser a new comments identifier
     *
     * @param array $data
     * @param integer $identifier_id
     * @throws \Exception
     */
    public function insert($data, &$identifier_id=-1)
    {
        try {
            $this->app['db']->insert(self::$table_name, $data);
            $identifier_id = $this->app['db']->lastInsertId();
            return $identifier_id;
        } catch (\Doctrine\DBAL\DBALException $e) {
            throw new \Exception($e);
        }
    }

    /**
     * Select a comments identifier by the given identifier ID
     *
     * @param integer $identifier_id
     * @throws \Exception
     * @return Ambigous <boolean, array>
     */
    public function select($identifier_id)
    {
        try {
            $SQL = "SELECT * FROM `".self::$table_name."` WHERE `identifier_id`='$identifier_id'";
            $result = $this->app['db']->fetchAssoc($SQL);
            return (isset($result['identifier_id'])) ? $result : false;
        } catch (\Doctrine\DBAL\DBALException $e) {
            throw new \Exception($e);
        }
    }

}

redpajama_set_name: RedPajamaGithub


My name is Michael Kohlegger. I am a Lecturer and Researcher in Information System at the University of Applied Sciences Kufstein. I am doing research and teach in the fields of (unintended) IT concequences, data engineering and data analytics. I have a professional background in Business Analysis and systems design.
As a researcher I try to understand how people and technology find together and make each other what they are. As a teacher I like to help people finding their own ways in the Information Systmes domain and exploit exciting new topics along the way.
In my spare time I am a family person and an enthusiastic runner on ultra distances and trails.


"Bryan provided the solo guitar music for my son and fiancés rehearsal dinner in Ft. Lauderdale. He's fantastic! A great guitarist and a delightful person to work with. It was an outdoor dinner by the pool and he provided the perfect music for this special evening. He has my highest recommendation!"
Solo jazz guitar is one of my favorite formats for playing. I simply love the balance of freedom and intimacy that it provides. And solo guitar offers the ideal background music for dinner/cocktail parties and small-to-medium sized gathering of all kinds. Solo guitar can fill a room without overpowering conversation, and the performance space required is minimal.
My repertoire consists of hundreds of timeless compositions by classic composers such as Duke Ellington, George Gerwshin, Cole Porter, and more. I play a mix of swing, bossa nova and pop tunes that creates a lush ambiance with plenty of variety.
Watch the video below for a sampling of several tunes that showcase the kinds of songs you can hear me playing as a solo guitarist. For a more comprehensive list of my solo repertoire click here.


Quest Camperdown hotel Sydney | Low rates. No booking fees.
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The hotel is located at 23-33 Missenden Rd, Camperdown in Sydney, South Sydney, New South Wales, Australia – see Sydney map ».
Hotels in the neighbourhood of the Quest Camperdown hotel Sydney are sorted by the distance from this hotel.
In the following list, large airports nearest to Quest Camperdown hotel Sydney are sorted by the approximate distance from the hotel to the airport.
Airports near to Quest Camperdown hotel Sydney, not listed above.
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The following list shows places and landmarks nearest to Quest Camperdown hotel Sydney sorted by the distance from the hotel to the centre of the listed place.


Notes: Able to detect notes down to 8.17 Hz (C-8).
Instrument tuning modes? 25 "Sweetened Tunings"
Peterson Electro-Musical Products is an American company founded by Richard H. Peterson in 1948. The company is based in Alsip, Illinois, a suburb of Chicago. Additional information is available on the Peterson Electro-Musical Products website. In 1985, Peterson bought the strobotuner division from Conn Instruments and continues to service Conn strobe tuners.
Go to Support > Contact Us on site's main menu.


Mercer Club Baseball player Noah Grant pitches in a game against Armstrong State in 2017.
"We just want to play…all the time," said Mercer Club Baseball player Layton Sheets.
The team started last year after Sophomore Ryan Fields and some friends were looking to play baseball and realized that there was no club or intramural teams to do so.
Today, they're in the middle of their second season, and they've grown to become a 20-member team.
"We're open to anyone who's interested, but as we grow as a team, we will start hosting tryouts," Sheets said.
There is a variety of players who are on the team this year, but most of them are freshmen and sophomores.
There have also been some football players who have come out and practiced with the team.
So far, the club baseball team has played Georgia Southern, Armstrong, Coastal Georgia, Alabama, Georgia Tech and Tennessee. Sometimes they travel to those schools and sometimes the schools come play in Macon.
The team practices at a baseball field next to Lakeside High School about 15 minutes away from campus. They also host their home games there.
The members of the team like it that way though. They enjoy playing year-round.
According to Fields, their first year performance wasn't too bad. Their weakness was their pitching.
"Hopefully we move past that and play a little better this year," Fields said.
Their number one goal is to play more games and keep the team together. It's been tough for them to decide on a practice time that everyone will show up to.
If you're interested in playing for the club baseball team, email ryanfields@bellsouth.net.


text:
Home » Resources » California » Browse Alphabetically » »
Consumer Action Edition
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Alameda County Community Development Agency
The Alameda County Community Development Agency is to enhance the quality-of-life of County residents and plan for the future well-being of the County's diverse communities; to balance the physical, economic, and social needs of County residents through land use planning, environmental management, neighborhood improvement, and community development; and to promote and protect agriculture, the environment, economic vitality and human health.
Alliance to Save Energy
The Alliance to Save Energy offers consumers all the information they need to save money, increase comfort, and even reduce pollution through energy efficiency. - Cut your home utility bills — freeing money for other things. - Increase your comfort. - Reduce pollution and greenhouse gas emissions. - Increase national security. - Relax while energy-efficient technologies and products continue producing these benefits for you day…
Appleseed
A non-profit network of 17 public interest justice centers in the United States and Mexico, Appleseed is dedicated to building a society in which opportunities are genuine, access to the law is universal and equal, and government advances the public interest. Appleseed uncovers and corrects injustices and barriers to opportunity through legal, legislative and market-based structural reform. Working with our huge pro bono network, we…
Architects Board
The California Architects Board (Board) was created in 1901 by the California Legislature to fulfill the mission of protecting the health, safety, and welfare of the public through the regulation of the practice of architecture in California. It is one of numerous boards, bureaus, commissions, and committees within the Department of Consumer Affairs responsible for consumer protection and the regulation of licensed professionals. The Board…
Asian Americans Advancing Justice - Los Angeles
Asian Americans Advancing Justice - Los Angeles (Advancing Justice - LA) is the nation's largest legal and civil rights organization for Asian Americans, Native Hawaiians, and Pacific Islanders (NHPI). Founded in 1983 as the Asian Pacific American Legal Center, Advancing Justice - LA serves more than 15,000 individuals and organizations every year. Through direct services, impact litigation, policy advocacy, leadership development, and capacity building, Advancing…
Asian Law Alliance
Over the past 32 years as a nonprofit corporation, the Asian Law Alliance has helped tens of thousands of people in obtaining decent housing, justice in the immigration process, and access to basic human and legal rights. Today, Asian/Pacific Islanders continue to be denied fundamental rights. ALA continues to keep its doors open for those individuals who are limited in English, who do not understand…
Asian Law Caucus (ALC)
Non-profit legal organization which offers assistance to low-income consumers. Promote, advances and represents the legal and civil rights of Asian and Pacific Islander communities. ALC provides help throughout the Bay Area. Free or low-cost, depending on income.
Asian Pacific American Dispute Resolution Center
The Asian Pacific American Dispute Resolution Center (APADRC) is a 501c3 nonprofit organization that provides mediation and conflict resolution services to the diverse communities in the Los Angeles area, with a focus on the Asian Pacific Islander Population. They strive to improve human relations and race relations by addressing complex, cross cultural community conflicts and their root causes with community partners through education, training, and…
Bay Area Legal Aid (BayLegal) is the largest provider of free civil legal services in the Bay Area. Their seven regional offices work together to provide high quality legal assistance to low-income people regardless of their location, language, or disability. BayLegal is uniquely positioned to help people across county lines. They assist people in the areas of housing, public benefits, health access, and domestic violence.
Berkeley Rent Stabilization Board
The mission of the Rent Stabilization Board is to regulate residential rent increases in the City of Berkeley and to protect against unwarranted rent increases and evictions and to provide a fair return to property owners. The Board works to ensure compliance with legal obligations relating to rental housing; and to advance the housing policies of the City with regard to low and fixed income…
Bet Tzedek, The House of Justice, provides free legal assistance to thousands of people who would otherwise be denied access to the legal system underpinning our democracy. Bet Tzedek was founded in 1974 by a few individuals who sought to act upon a central tenet of Jewish law and tradition, which appears in the Bible: "Tzedek, tzedek tirdof -- Justice, justice you shall pursue." This…
Bureau of Electronic and Appliance Repair
Looks into complaints about invoices which exceed the written estimate, incompetent repairs, etc. Call to get a complaint form. Complaints in letter form are also acceptable.
Bureau of Household Goods and Services
The Bureau of Home Furnishings and Thermal Insulation regulates upholstered furniture and bedding products sold in California to make sure they meet health, fire safety, and labeling standards.
Butte County - Housing Authority
The Housing Authority of the County of Butte (HACB) is located in Chico, California and is a non-profit public agency incorporated in 1946. The mission of HACB is to assist low and moderate income residents of Butte County to secure and maintain high quality affordable housing. Subsidized housing is provided to families, seniors and disabled individuals whose income is between 50% and 80% of median…
Calaveras County Housing Repair Program
Calaveras County can assist low income individuals with low-interest loans, deferred loans, or other types of loans depending on your income level and need. You may qualify for a home repair loan even if you have been turned down by other lenders.
California - Department of Consumer Affairs
The Department of Consumer Affairs (DCA) protects and serves California consumers while ensuring a competent and fair marketplace. DCA helps consumers learn how to protect themselves from unscrupulous and unqualified individuals. The Department also protects professionals from unfair competition by unlicensed practitioners.
California - Department of Insurance
The Consumer Services Division is responsible for gathering and responding to consumer inquiries regarding insurance company or producer activities.
California - Department of Real Estate
The mission of the California Bureau of Real Estate is to safeguard and promote the public interests in REAL ESTATE MATTERS through licensure, regulation, education and enforcement. Handles unfair, misleading or fraudulent treatment by a licensee in real estate transactions. Call for complaint form. State your specific complaint, list witnesses and include copies of all documents.
California - Housing Finance Agency (CHFA)
CHFA finances below-market-rate loans to affordable rental housing and assists first-time home buyers.
California Center for Law and the Deaf
The mission of the California Center for Law and the Deaf (CalCLAD) is to protect and advance the legal rights of deaf and hard-of-hearing people to enable them to live independent, productive lives, with full access to the rights, privileges, entitlements, services, educational and employment opportunities available to others. Legal advice, consultation, and information are provided to deaf and hard-of-hearing individuals in issues involving deafness…
California Department of Community Services and Development (CSD)
The mission of CSD is to administer and enhance energy and community service programs that result in an improved quality of life and greater self-sufficiency for low-income Californians. Community Services & Development (CSD) is a state department of the California Health and Human Services Agency. CSD administers federal programs to help low-income families achieve and maintain self-sufficiency, meet their home energy needs, and reside in…
California Foundation for Independent Living Centers (CFILC)
This foundation helps independent living centers promote themselves to disabled consumers. The website has vast list of web Independent Living resources and current job opportunities with contact information for people with disabilities.
This agency is responsible for administering two of California's major tax programs: Personal Income Tax and the Corporation Tax. They also have responsibility for administering the Homeowner and Renter Assistance (HRA) program, and other non-tax programs and delinquent debt collection functions, including child support debt collections and delinquent vehicle registration debt collections on behalf of the Department of Motor Vehicles.
Nonrefundable Renter's Credit is a…
California Human Development Corporation (CHDC)
CHDC is a nonprofit 501(c)(3) and provide human services in northern California. Their purpose is to help low-income people secure the education, training, job opportunities and improved social conditions they need to live with independence and greater human dignity.
California Online Directory
The California Online Directory is a service provided by the California Department of Technology. The Online Directory provides access to government information and services for state employees, local government and the general public. The directory services include state employee information, state agency information, and a State Government Organization Chart, as well as a keyword search.

redpajama_set_name: RedPajamaCommonCrawl


The two were just amazed by their discovery and had a hard time leaving their comfort place. Scroll down to see the adorable duo!
And it's tucking themselves into a heated pocket.
The two were just amazed by their discovery and had a hard time leaving their comfort place.


Your estate will have to pay federal estate taxes if its net value when you die is more than the exempt amount set by Congress at that time. The federal exemption is currently $5 million (adjusted for inflation); every dollar over the exempt amount is taxed at 40%. Some states also have their own death or inheritance tax, so your estate could be exempt from federal tax and still have to pay a state tax.
For example, let's say Bob and Sue have a combined net estate of $10 million. When Bob dies, he leaves everything to Sue, so no estate taxes are due at his death. When Sue dies, her estate of $10 million uses her $5 million exemption. This has been traditional planning for many married couples, but the problem is they waste Bob's exemption. With this approach, the tax bill on the remaining $5 million is a whopping $2 million!
Congress tried to fix this. Now, the executor of Bob's estate can transfer his unused exemption to Sue by filing a federal estate tax return at Bob's death. But if Sue remarries and outlives her new husband, she would lose Bob's unused exemption. Also, by leaving everything to Sue, Bob has no control over how his share of the assets are managed or distributed. Plus, any growth on the assets will be included in Sue's estate and taxed when she dies.
This also lets Bob keep control over how his share of the estate is managed and distributed (important if he has children from a previous marriage). The assets are valued and taxed only at his death, so no growth is included in Sue's estate. And the assets in Bob's trust can be available for anything Sue needs.
One way to reduce estate taxes is to reduce the size of your estate before you die. So go ahead and spend some, and enjoy it. Also, you probably know whom you want to have your assets after you die, so why not make some gifts now? It can be very satisfying to see the results of your gifts, something you can't do if you wait until you die.
Appreciating assets are best to give because any future appreciation will also be out of your estate. Gifted assets keep your cost basis (what you paid for them), so recipients may pay capital gains tax when they sell. But the top capital gain gains rate (20%) is still less than the estate tax rate (40%) that would apply if you hold onto the assets until your death.
Some popular strategies are introduced below. Note that these are irrevocable, so you can't change your mind later.
Federal law now lets you give up to $14,000 ($28,000 if married) to as many people as you wish each year. So if you give $14,000 to each of your two children and five grandchildren, you will reduce your estate by $98,000 a year (7 x $14,000), $196,000 if your spouse joins you. (This amount is adjusted from time to time due to inflation.) State laws may differ.
If you give more than this, the excess will be considered a taxable gift and will be applied to your $5+ million ($10+ million if married) "unified" gift and estate tax exemption. (If you use it while you are living, it's considered a gift tax exemption; if you use it after you die, it's an estate tax exemption.) Charitable gifts are still unlimited. So are gifts for tuition and medical expenses if you give directly to the institution.
A QPRT "leverages" your estate tax exemption. Since your children will not receive the house until the trust ends, its value as a gift is reduced. For example, if the current value of your home is $250,000 and you put it in a QPRT for 15 years, its value for tax purposes could be as little as $75,000. That leaves much more of your exemption for other assets.
Here's how they work. You and your spouse can set up an LLC or FLP and transfer assets to it. In exchange, you receive ownership interests. Though you have a fiduciary obligation to other owners, you control the LLC (as manager) or FLP (as general partner). You can give ownership interests to your children, which removes value from your taxable estate. These interests cannot be sold or transferred without your approval, and because there is no market for these interests, their value is often discounted. This lets you transfer the underlying assets to your children at a reduced value, without losing control.
Depending on your age and health, buying life insurance can be an inexpensive way to replace an asset given to charity and/or to pay any remaining estate taxes. The three-year rule mentioned earlier does not apply to new policies. But you should not be the owner of the policy — that would increase your taxable estate and estate taxes. To keep the death benefits out of your estate, set up an ILIT and have the trustee purchase the policy for you.
• Uses both spouses' estate tax exemptions, doubling the amount protected from estate taxes and saving a substantial amount for your loved ones.