The ERiK-Methodological Report III is the third in a series of methodological reports related to the 'Entwicklung von Rahmenbedingungen in der Kindertagesbetreuung -indikatorengestützte ...Qualitätsbeobachtung (ERiK)' study. The report focuses on the conception, sample selection, and survey designs of the ERiK-Surveys 2022. Together with the ERiK-Methodological Report I and II, that cover the ERiK Surveys 2020, it provides comprehensive background information on the ERiK-Surveys conducted in 2022 and describes their progression until December 31, 2021. The subsequent steps, such as implementing the ERiK-Surveys 2022, will be described in a later report.
Survey research methodology is widely used in marketing, and it is important for both the field and individual researchers to follow stringent guidelines to ensure that meaningful insights are ...attained. To assess the extent to which marketing researchers are utilizing best practices in designing, administering, and analyzing surveys, we review the prevalence of published empirical survey work during the 2006–2015 period in three top marketing journals—
Journal of the Academy of Marketing Science
(
JAMS
),
Journal of Marketing
(
JM
), and
Journal of Marketing Research
(
JMR
)—and then conduct an in-depth analysis of 202 survey-based studies published in
JAMS
. We focus on key issues in two broad areas of survey research (issues related to the choice of the object of measurement and selection of raters, and issues related to the measurement of the constructs of interest), and we describe conceptual considerations related to each specific issue, review how marketing researchers have attended to these issues in their published work, and identify appropriate best practices.
Consequences of Survey Nonresponse PEYTCHEV, ANDY
The Annals of the American Academy of Political and Social Science,
01/2013, Letnik:
645, Številka:
1
Journal Article
Recenzirano
Nonresponse is a prominent problem in sample surveys. At face value, it reduces the trust in survey estimates. Nonresponse undermines the probability-based inferential mechanism and introduces the ...potential for nonresponse bias. In addition, there are other important consequences. The effort to limit increasing nonresponse has led to higher survey costs—allocation of greater resources to measure and reduce nonresponse. Nonresponse has also led to greater survey complexity in terms of design, implementation, and processing of survey data, such as the use of multiphase and responsive designs. The use of mixed-mode and multiframe designs to address nonresponse increases complexity but also introduces other sources of error. Surveys have to rely to a greater extent on statistical adjustments and auxiliary data. This article describes the major consequences of survey nonresponse, with particular attention to recent years.
We correct, update, and elaborate Curtin, Presser, and Singer’s (2000) report that the University of Michigan’s Survey of Consumer Attitudes (SCA) experienced only a small response rate decline ...between 1979 and 1996, contrary to the widespread perception of plunging response rates. Our aims are to (1) correct errors in the SCA response rate data that affected Curtin, Presser, and Singer’s (2000) result, (2) examine the trend in SCA response rates after 1996, when caller identification technology became widespread, and (3) describe the roles played by the various sources of SCA nonresponse over time. The results show that the response rate decline from 1979 to 1996 was larger than described by Curtin, Presser, and Singer (2000); the response rate drop was significantly steeper from 1996 to 2003 than from 1979 to 1996; and the 1979 to 2003 trends differed substantially for refusals and noncontacts.
To conduct nutrition-related analyses on large-scale health surveys, two aspects of the survey must be incorporated into the analysis: the sampling weights and the sample design; a practice which is ...not always observed. The present paper compares three analyses: (1) unweighted; (2) weighted but not accounting for the complex sample design; and (3) weighted and accounting for the complex design using replicate weights.
Descriptive statistics are computed and a logistic regression investigation of being overweight/obese is conducted using Stata.
Cross-sectional health survey with complex sample design where replicate weights are supplied rather than the variables containing sample design information.
Responding adults from the National Nutrition and Physical Activity Survey (NNPAS) part of the Australian Health Survey (2011-2013).
Unweighted analysis produces biased estimates and incorrect estimates of se. Adjusting for the sampling weights gives unbiased estimates but incorrect se estimates. Incorporating both the sampling weights and the sample design results in unbiased estimates and the correct se estimates. This can affect interpretation; for example, the incorrect estimate of the OR for being a current smoker in the unweighted analysis was 1·20 (95 % CI 1·06, 1·37), t= 2·89, P = 0·004, suggesting a statistically significant relationship with being overweight/obese. When the sampling weights and complex sample design are correctly incorporated, the results are no longer statistically significant: OR = 1·06 (95 % CI 0·89, 1·27), t = 0·71, P = 0·480.
Correct incorporation of the sampling weights and sample design is crucial for valid inference from survey data.
Cross-sectional data is pervasive in information systems (IS) research. This editorial reviews cross-sectional studies, summarizes their strengths and limitations, and derives use cases of when ...cross-sectional data is and is not useful in answering research questions. We raise concerns about assertions of temporal causality using data collected employing cross-sectional methods with no temporal order, which makes cause and effect difficult to establish. Based on our discussion of research using cross-sectional data and its limitations, we offer four recommendations for when and how to use such data: (1) improve credibility by reporting research in detail and transparently, (2) ensure appropriate sampling, (3) take configurational perspectives, and (4) integrate cross-sectional data into mixed- or multi-method designs. By doing so, we help IS researchers position and use cross-sectional studies appropriately within their methodological repertoire.
•Cross-sectional study designs are predominant in Information Systems research.•Cross-sectional research follows an efficient and inexpensive execution.•Cross-sectional data limit statements related to temporal causality.•Use configurational or mixed-method studies when having cross-sectional data.•We offer four recommendations for when and how to use cross-sectional data.
Post-translational modifications (PTMs) occur in almost all proteins and play an important role in numerous biological processes by significantly affecting proteins' structure and dynamics. Several ...computational approaches have been developed to study PTMs (e.g., phosphorylation, sumoylation or palmitoylation) showing the importance of these techniques in predicting modified sites that can be further investigated with experimental approaches. In this review, we summarize some of the available online platforms and their contribution in the study of PTMs. Moreover, we discuss the emerging capabilities of molecular modeling and simulation that are able to complement these bioinformatics methods, providing deeper molecular insights into the biological function of post-translational modified proteins.
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