Lag*one autocorrelation in short series: estimation and hypotheses ... |
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Overview
Tests associated with the different estimators. These estimates evidence the. Small probability of detecting autocorrelation in series with less than 20. The present study focuses on autocorrelation estimators reviewing. Most of them and proposing a new one. Hypothesis testing is also explored. And discussed as the statistical significance of the estimates may be of. Interest. These topics are relevant for methodological and behavioural. Sciences, since they have impact on the techniques used for assessing. This research was supported by the Comissionat per a Universitats i Recerca del.
Departament d’Innovació, Universitats i Empresa of the Generalitat de Catalunya and the. European Social Fund. Correspondence concerning this article should be addressed to. Antonio Solanas, Departament de Metodologia de les Ciències del Comportament, Facultat. De Psicologia, Universitat de Barcelona, Passeig de la Vall d’Hebron, 171, 08035Barcelona, Spain. Phone number: +34933125076. Electronic mail may be sent to Antonio. It has to be taken into consideration that the previous decades’. Controversy on the existence of autocorrelation in behavioural data (Busk &. Marascuilo, 1988; Huitema, 1985; 1988; Sharpley & Alavosius, 1988; Suen.
& Ary, 1987) was strongly related to the properties of the autocorrelation. Estimators. The evidence on the presence of serial dependence (Matyas &. Greenwood, 1997; Parker, 2006) has led to exploring the effects of violating. The assumptions of independence of several widely used procedures. In this. Relation, liberal Type I error rates have been obtained in presence of positive. Serial dependence for traditional analysis of variance (Scheffé, 1959) and its. Modifications (Toothaker, Banz, Noble, Camp, & Davis, 1983)..
Additionally, randomization tests – a procedure that does not explicitly. Assume independence (Edgington, & Onghena, 2007) – have shown to be. Affected by positive autocorrelation both in terms in reducing statistical. Power (Ferron & Ware, 1995) and, more recently, in distorting Type I error. Rates (Manolov & Solanas, 2009). The independence of residuals required. By regression analysis (Weisberg, 1980) has resulted in proposing that after. Fitting the regression model, a statistically significant autocorrelation in the.
Errors has to be eliminated prior to interpreting the regression coefficients.. For instance, generalized least squares procedures such as the one proposed. By Simonton (1977) and the Cochrane-Orcutt and Prais-Winsten versions. Require estimating the autocorrelation of the residuals. Imprecisely. Estimated serial dependence may lead to elevated Type I error rates when. Assessing intervention effectiveness in short series.. Autoregressive integrated moving average (ARIMA) modeling has.
Also been proposed for dealing with sequentially related data (Box &. Jenkins, 1970). This procedure includes an initial step of model. Identification including autocorrelation estimation prior to controlling it and. Determining the efficacy of the interventions. However, it has been shown. That serial dependence distorts the performance of ARIMA in short series. (Greenwood & Matyas, 1990). Unfortunately, the required amount of. Measurements is not frequent in applied psychological studies and,.
Moreover, it does not ensure correct model identification (Velicer & Harrop,. Several investigations (Arnau & Bono, 2001; DeCarlo & Tryon,. 1993; Huitema & McKean, 1991, 2007a, b; Matyas & Greenwood, 1991;. McKean & Huitema, 1993) have carried out Monte Carlo simulation. Comparisons of autocorrelation estimators for different lags. These studies. Have shown that estimation and hypothesis testing are both problematic in. Short data series. Most of the estimators studied had considerable bias and.
Were scarcely efficient for short series. As regards the asymptotic test based. On Bartlett’s (1946) proposal, it proved to be unacceptable. These topics. Have to be taken into consideration when using widespread statistical.
Departament d’Innovació, Universitats i Empresa of the Generalitat de Catalunya and the. European Social Fund. Correspondence concerning this article should be addressed to. Antonio Solanas, Departament de Metodologia de les Ciències del Comportament, Facultat. De Psicologia, Universitat de Barcelona, Passeig de la Vall d’Hebron, 171, 08035Barcelona, Spain. Phone number: +34933125076. Electronic mail may be sent to Antonio. It has to be taken into consideration that the previous decades’. Controversy on the existence of autocorrelation in behavioural data (Busk &. Marascuilo, 1988; Huitema, 1985; 1988; Sharpley & Alavosius, 1988; Suen.
& Ary, 1987) was strongly related to the properties of the autocorrelation. Estimators. The evidence on the presence of serial dependence (Matyas &. Greenwood, 1997; Parker, 2006) has led to exploring the effects of violating. The assumptions of independence of several widely used procedures. In this. Relation, liberal Type I error rates have been obtained in presence of positive. Serial dependence for traditional analysis of variance (Scheffé, 1959) and its. Modifications (Toothaker, Banz, Noble, Camp, & Davis, 1983)..
Additionally, randomization tests – a procedure that does not explicitly. Assume independence (Edgington, & Onghena, 2007) – have shown to be. Affected by positive autocorrelation both in terms in reducing statistical. Power (Ferron & Ware, 1995) and, more recently, in distorting Type I error. Rates (Manolov & Solanas, 2009). The independence of residuals required. By regression analysis (Weisberg, 1980) has resulted in proposing that after. Fitting the regression model, a statistically significant autocorrelation in the.
Errors has to be eliminated prior to interpreting the regression coefficients.. For instance, generalized least squares procedures such as the one proposed. By Simonton (1977) and the Cochrane-Orcutt and Prais-Winsten versions. Require estimating the autocorrelation of the residuals. Imprecisely. Estimated serial dependence may lead to elevated Type I error rates when. Assessing intervention effectiveness in short series.. Autoregressive integrated moving average (ARIMA) modeling has.
Also been proposed for dealing with sequentially related data (Box &. Jenkins, 1970). This procedure includes an initial step of model. Identification including autocorrelation estimation prior to controlling it and. Determining the efficacy of the interventions. However, it has been shown. That serial dependence distorts the performance of ARIMA in short series. (Greenwood & Matyas, 1990). Unfortunately, the required amount of. Measurements is not frequent in applied psychological studies and,.
Moreover, it does not ensure correct model identification (Velicer & Harrop,. Several investigations (Arnau & Bono, 2001; DeCarlo & Tryon,. 1993; Huitema & McKean, 1991, 2007a, b; Matyas & Greenwood, 1991;. McKean & Huitema, 1993) have carried out Monte Carlo simulation. Comparisons of autocorrelation estimators for different lags. These studies. Have shown that estimation and hypothesis testing are both problematic in. Short data series. Most of the estimators studied had considerable bias and.
Were scarcely efficient for short series. As regards the asymptotic test based. On Bartlett’s (1946) proposal, it proved to be unacceptable. These topics. Have to be taken into consideration when using widespread statistical.
