Practical Methods for Design and Analysis of Complex Surveys

Lehtonen, Risto; Pahkinen, Erkki

doi:10.1002/0470091649

Cited by 204 publications

(221 citation statements)

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“…This was particularly important for survey data because the sampling probabilities could be different among sampling clusters (Lehtonen and Pahkinen, 2004). Thus, we used the sampling weights that the KNHANES provided for all of the data analyses in this study.…”

Section: Discussionmentioning

confidence: 99%

Prevalence of the Metabolic Syndrome and Associated Factors in Korean Cancer Survivors

Lee¹,

Park²,

Song³

et al. 2013

Asian Pacific Journal of Cancer Prevention

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Section: Discussionmentioning

confidence: 99%

Prevalence of the Metabolic Syndrome and Associated Factors in Korean Cancer Survivors

Lee¹,

Park²,

Song³

et al. 2013

Asian Pacific Journal of Cancer Prevention

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“…For the cosi sample, we used a "power allocation" (Bankier 1988;Lehtonen and Pahkinen 2004), which is a modified form of optimal allocation, to determine the number of observations per stratum. Power allocation allows small but important strata to be given weight, and incorporates descriptive statistics (e.g., standard deviation or coefficient of variation) about the variable of interest.…”

Section: Observation Allocation (Including Power Allocation) In the Smentioning

confidence: 99%

“…8 provides a solution for a minimum sample size (n h ) for stratum h, where N h is the stratum population size (i.e., total number of pixels in stratum h), and CV hy is the coefficient of variation (CV = σ / μ) of the variable of interest, y (e.g., the reflectance), within stratum h. The coefficient q refers to the desired power, where 1≥q≥0, and is assigned by the user. Solutions of q = 0.3 or 0.5 are often used (Lehtonen and Pahkinen 2004). When q = 1, the result is similar to a Neyman allocation, which is a function of stratum size (Lehtonen and Pahkinen 2004;Neyman 1934), while if q = 0, stratum size is effectively not considered.…”

Section: Observation Allocation (Including Power Allocation) In the Smentioning

confidence: 99%

“…Solutions of q = 0.3 or 0.5 are often used (Lehtonen and Pahkinen 2004). When q = 1, the result is similar to a Neyman allocation, which is a function of stratum size (Lehtonen and Pahkinen 2004;Neyman 1934), while if q = 0, stratum size is effectively not considered. A value of q between 0 and 1 is a compromise between these allocations (Bankier 1988).…”

Section: Observation Allocation (Including Power Allocation) In the Smentioning

confidence: 99%

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C-correction of optical satellite data over alpine vegetation areas: A comparison of sampling strategies for determining the empirical c-parameter

Reese

Olsson

2011

Remote Sensing of Environment

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Semi-empirical topographic normalization methods (e.g., C-correction) have been widely used to correct illumination differences in optical satellite data. The objective of this study was to examine the precision and accuracy of the C-correction's empirical parameter, c, as a function of the sample from which it was derived. Three sampling methods were compared: a random sample, a sample stratified on north and south aspects, and a sample stratified by cosine of the solar incidence angle, i. In the latter, power allocation was used to determine the quantity of observations for each stratum. Four overlapping satellite images were used (two Landsat 5 TM and two SPOT 5 HRG) with different acquisition dates and large solar zenith angles over an alpine region in Sweden. The sample stratified by cosine of i produced c with the highest precision from repeated trials and had coefficients of determination (R 2 ) twice as high as those from the other sampling methods. Use of power allocation in the cosine of i stratified sample enabled better representation of spectral variability; this was particularly important for the NIR band where the outcome of c differed according to sampling method. Evaluations using t-tests and classification accuracy showed that c derived from the cosine of i stratified sample correctly normalized a larger percentage of the evaluation data. The distribution of cosine of i in the study area, the spectral variability and vegetation types exert influences to consider when sampling to derive c. Although sampling was restricted to alpine vegetation only, some

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“…If this score is above a specified critical value then the dependent variable is set to one category, else it is set to another. In other words, the procedure is used to 'predict' which of two or more categories each individual case will manifest, and in doing so creates a model based on the predictor variables (Gilbert 1993, Lehtonen andPahkinen 1995).…”

Section: Analysesmentioning

confidence: 99%

How prepared do newly-qualified teachers feel? Differences between routes and settings

Gorard

2016

Journal of Education for Teaching

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Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractDoes it matter whether teachers are trained in schools or universities? In England, there is an ongoing change in the routes to becoming a newly qualified teacher (NQT), and in the proportion of new teachers arriving via each. Given this, and widely-reported problems with teacher supply, it is important to consider whether there are discernible differences between the routes in terms of their outcomes. This paper is based on a re-analysis of the 2015 Department for Education survey of 7,770 NQTs. The outcomes considered are the levels of reported NQT satisfaction with their overall training and induction, preparedness to help improve pupil reading skills, whether the reported levels of satisfaction vary substantively between routes, phases, and types of provision, how much of any such difference is attributable to the prior characteristics of the students following these routes, and how much to the routes alone. Using logistic regression, it was possible to explain around 18% of the otherwise unexplained variation in NQT responses. Most of the variation between individual responses remains unexplained -perhaps because key but unknown variables are missing, and certainly because the level of satisfaction is generally so high. The average levels of satisfaction for NQTs are largely un-stratified by sex, disability, age and ethnicity. Within the two main routes of school-and university-led there is almost as much variability as there is between them. Once other factors are taken into account, the differences in reported satisfaction between routes and providers are small. There is, therefore, no particular reason to promote or support one route at the expense of the other -at least in terms of NQT satisfaction.

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Practical Methods for Design and Analysis of Complex Surveys

Cited by 204 publications

References 0 publications

Prevalence of the Metabolic Syndrome and Associated Factors in Korean Cancer Survivors

Prevalence of the Metabolic Syndrome and Associated Factors in Korean Cancer Survivors

C-correction of optical satellite data over alpine vegetation areas: A comparison of sampling strategies for determining the empirical c-parameter

How prepared do newly-qualified teachers feel? Differences between routes and settings

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