Momentary and Partial Interval Time Sampling: A Reply to Harrop, Daniels and Foulkes (1990)

1991 ◽  
Vol 19 (4) ◽  
pp. 333-336 ◽  
Author(s):  
Robert M. Adams
Keyword(s):  
Intervention Studies ◽  
Sampling Error ◽  
Small Sample Size ◽  
Large Error ◽  
Small Sample ◽  
Time Sampling ◽  
Momentary Time Sampling ◽  
Target Behaviour ◽  
Sampling Intervals ◽  
Partial Interval

Momentary time sampling (MTS) is described as an unbiased method of estimating time spent engaging in target behaviour. Conclusions that MTS with long sampling intervals or for short sessions results in large error are based on studies which use too few observations (a too-small sample size) to expect small sampling error. Partial interval (PI) sampling is a biased method, and the degree of bias changes as the characteristics of the behaviour change, a particular problem for intervention studies.

The Use of Momentary Time Sampling and Partial Interval Recording in Behavioural Research

1990 ◽  
Vol 18 (2) ◽  
pp. 121-127 ◽  
Author(s):  
Alex Harrop ◽  
Michael Daniels ◽  
Christine Foulkes
Keyword(s):  
Computer Simulation ◽  
Behaviour Change ◽  
Sampling Techniques ◽  
Time Sampling ◽  
Momentary Time Sampling ◽  
Mode Of Operation ◽  
Behavioural Research ◽  
Partial Interval ◽  
Interval Recording ◽  
Absolute Duration

The inherent properties of momentary time sampling (MTS) and partial interval recording (PIR) are examined. Findings derived from computer simulation investigations are discussed in terms of the mode of operation of the two time-sampling techniques. It is seen that the advantage of MTS is that it can, under certain restricted circumstances, estimate absolute duration of behaviour occurring. The important disadvantage of MTS is that it is relatively insensitive when estimating degree of change of behaviour. In contrast, although PIR cannot accurately measure absolute duration it is more sensitive to behaviour change than is MTS. It is concluded that the practitioner who wishes to use one of these methods of time sampling must carefully consider the aims and possible effects of the investigation before deciding which method to use.

scholarly journals A COMPARISON OF MOMENTARY TIME SAMPLING AND PARTIAL-INTERVAL RECORDING FOR EVALUATING FUNCTIONAL RELATIONS

2007 ◽  
Vol 40 (3) ◽  
pp. 501-514 ◽  
Author(s):  
Maeve G. Meany-Daboul ◽  
Eileen M. Roscoe ◽  
Jason C. Bourret ◽  
William H. Ahearn
Keyword(s):  
Time Sampling ◽  
Momentary Time Sampling ◽  
Functional Relations ◽  
Partial Interval ◽  
Interval Recording

Reliability of Regression-Corrected Climate Forecasts

2014 ◽  
Vol 27 (9) ◽  
pp. 3393-3404 ◽  
Author(s):  
Michael K. Tippett ◽  
Timothy DelSole ◽  
Anthony G. Barnston
Keyword(s):  
Sample Size ◽  
Climate Model ◽  
Sampling Error ◽  
Small Sample Size ◽  
Small Sample ◽  
Climate Forecast ◽  
Regression Parameters ◽  
Climate Forecasts ◽  
Probability Forecasts ◽  
The Impact

Abstract Regression is often used to calibrate climate model forecasts with observations. Reliability is an aspect of forecast quality that refers to the degree of correspondence between forecast probabilities and observed frequencies of occurrence. While regression-corrected climate forecasts are reliable in principle, the estimated regression parameters used in practice are affected by sampling error. The low skill and small sample sizes typically encountered in climate prediction imply substantial sampling error in the estimated regression parameters. Here the reliability of regression-corrected climate forecasts is analyzed for the case of joint-Gaussian distributed ensemble forecasts and observations with regression parameters estimated by least squares. Hypothesis testing of the regression parameters provides direct information about the skill and reliability of the uncorrected ensemble-based probability forecasts. However, the regression-corrected probability forecasts with estimated parameters are systematically “overconfident” because sampling error causes a positive bias in the regression forecast signal variance, despite the fact that the estimates of the regression parameters are themselves unbiased. An analytical description of the reliability diagram of a generic regression-corrected climate forecast is derived and is shown to depend on sample size and population correlation skill, with small sample size and low skill being factors that increase overconfidence. The analytical reliability estimate is shown to capture the effect of sampling error in synthetic data experiments and in a 29-yr dataset of NOAA Climate Forecast System version 2 predictions of seasonal precipitation totals over the Americas. The impact of sampling error on the reliability of regression-corrected forecast has been previously unrecognized and affects all regression-based forecasts. The use of regression parameters estimated by shrinkage methods such as ridge regression substantially reduces overconfidence.

Comparison of EBLUP and EBLUP Modification in Estimating Small Areas (Study : Percentages of Poverty in Bogor District)

2018 ◽  
pp. 160-168
Author(s):  
Hary Merdeka ◽  
Kusman Sadik ◽  
Indahwati A
Keyword(s):  
Sample Size ◽  
Small Area ◽  
Small Sample Size ◽  
Large Error ◽  
Statistical Technique ◽  
Small Sample ◽  
Parameters Estimation ◽  
Small Areas ◽  
Modification Method ◽  
Size Estimates

A small area of the sample occurs when the sample size is very small. A large error will get if the parameters estimation is done with small the sample. One method to overcome it using a small area estimation (SAE) method. A small area estimator is a statistical technique to estimate the parameters of a sub-population with a small sample size. Estimates in the small area estimator method is based on the model and are indirect estimates. In this study the indirect method used is the EBLUP method and the modification of EBLUP estimator. The results of the alleged percentage of poverty in the Bogor district show that the EBLUP modification method is better compared to the expected method directly. This is based on the average of the RRMSE obtained.

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