<p>This thesis focuses on measurement errors that could be ascribed to the interviewers. To study interviewer variability a measurement error model is formulated which makes a clear distinction between three sources of randomness: the sample selection, interviewer assignment, and interviewing. </p><p>In the first paper the variance of the observed sample mean is derived, and it is seen how this variance depends on parameters of the measurement error model and on the number of interviewers. An estimator of the interviewer variance, which is seen to be unbiased, and a biased intra-interviewer correlation estimator are suggested. In a simulation study it is seen that the simulation variance of the interviewer variance estimator increases for both high and low interviewer assignments and seems to have a minimum somewhere in between. </p><p>The second paper presents an expression of the variance of the observed sample mean under stratified random sampling. Two possible estimators of the variance of the mean are considered, one of which has a slight positive bias, the other a negative bias, which can be large. Two different estimators of the interviewer variance are studied. Only one of them makes it possible to find a reasonable estimate of the intra-interviewer correlation. </p><p>In the third paper an expression for the variance of the interviewer variance estimator is derived. This result may prove useful in designing future studies of interviewer variance. For a large population it will be possible to use an approximate variance, irrespective of the underlying distribution of the unknown true values.</p><p>The fourth paper deals with some issues in planning and analyzing an interviewer variance study. Three problems are considered: (i) Determining the number of interviewers and the appropriate size of the interviewer assignments; (ii) Finding the probability of negative estimates of the interviewer variance; (iii) Testing for interviewer variance.</p>

This thesis focuses on measurement errors that could be ascribed to the interviewers. To study interviewer variability a measurement error model is formulated which makes a clear distinction between three sources of randomness: the sample selection, interviewer assignment, and interviewing. In the first paper the variance of the observed sample mean is derived, and it is seen how this variance depends on parameters of the measurement error model and on the number of interviewers. An estimator of the interviewer variance, which is seen to be unbiased, and a biased intra-interviewer correlation estimator are suggested. In a simulation study it is seen that the simulation variance of the interviewer variance estimator increases for both high and low interviewer assignments and seems to have a minimum somewhere in between. The second paper presents an expression of the variance of the observed sample mean under stratified random sampling. Two possible estimators of the variance of the mean are considered, one of which has a slight positive bias, the other a negative bias, which can be large. Two different estimators of the interviewer variance are studied. Only one of them makes it possible to find a reasonable estimate of the intra-interviewer correlation. In the third paper an expression for the variance of the interviewer variance estimator is derived. This result may prove useful in designing future studies of interviewer variance. For a large population it will be possible to use an approximate variance, irrespective of the underlying distribution of the unknown true values. The fourth paper deals with some issues in planning and analyzing an interviewer variance study. Three problems are considered: (i) Determining the number of interviewers and the appropriate size of the interviewer assignments; (ii) Finding the probability of negative estimates of the interviewer variance; (iii) Testing for interviewer variance.

This thesis has two purposes; emphasizing the importance of data quality of Big Data, and identifying and evaluating potential error sources in JavaScript tracking (a client side on - site online behavior clickstream data collection method commonly used in web analytics). The importance of data quality of Big Data is emphasized through the evaluation of JavaScript tracking. The Total Survey Error framework is applied to JavaScript tracking and 17 nonsampling error sources are identified and evaluated. The bias imposed by these error sources varies from large to small, but the major takeaway is the large number of error sources actually identified. More work is needed. Big Data has much to gain from quality work. Similarly, there is much that can be done with statistics in web analytics.