Report and Recommendations on Two Chilean Labor Force Surveys

For many years, Chile has benefited from two surveys of labor force developments for the “Greater Santiago Area.” One of these surveys dates back to the 1950s and is conducted by the University of Chile. The other is a national survey, conducted by the National Institute of Statistics (NIS), from which data are also available for the Santiago Metropolitan Area. Results, especially the rate of unemployment, do not always coincide, and this has been particularly the case for all years since 1998. This report studies this problem of non concurrence, identifies a number of areas for possible explanation, and makes recommendations for improvement of survey operations. Both surveys were found to follow quite well recommendations of the International Labor Organization regarding the measurement of employment and unemployment. Two significant areas in the report concern the questionnaires used for the surveys and data estimation techniques. Fourteen recommendations for improvements in the surveys are offered, with major attention focused on plans by the NIS to introduce an entirely new questionnaire in the near future. With respect to the University’s survey, the authors recommend changes in the basic questionnaire and survey weighting procedures. They also recommend improving data analysis (NIS), maintaining error profiles for data collection (both surveys), and using seasonal adjustment for statistical analysis (both).

Methods for analysing complex panel data using multilevel models with an application to the Brazilian labour force survey

Data sets commonly used in the social sciences are often obtained by sample surveys with complex designs. These designs usually incorporate a multistage selection from a population with a natural hierarchical structure. In addition, these surveys can also be carried out in a repeated manner including a rotating panel design, which is a source of planned non-response. Unplanned non-response is also present in panel data in the form of panel attrition and intermittent nonresponse.Methods are developed to handle this type of data complexity. These methods follow the Multilevel Model framework which is reviewed. Longitudinal growth curve models accounting for the complex data hierarchy are fitted. Recognizing the need to account for the complex correlation structure present in the data, multivariate multilevel models are then adopted. Alternative modified correlation structures accounting for the rotating sample design are proposed. Multivariate multilevel models are fitted utilizing these alternative correlation structures. In addition, models estimated using robust methods are compared with those estimated using standard methods.A method for calculating a set of longitudinal sample weights that accounts for attrition is proposed. Models utilising the conditional sample weights and longitudinal weights are fitted using the Probability-weighted Iterative Generalized Least Squares (PWIGLS) estimation method. Furthermore, an extension to PWIGLS for multivariate multilevel models is developed. Models fitted through different estimation methods are compared and the best approaches are suggested.Data from the Brazilian labour force survey, Pesquisa Mensal de Emprego (PME) are used. The PME has a complex sampling design that includes a multistage selection of the sample units and a rotating panel design characterised as 4-8-4. The methods developed are used to investigate the labour income dynamics of employed heads of households in the PME survey

Evaluation of multiple-imputation procedures for three-mode component models

Three-mode analysis is a generalization of principal component analysis to three-mode data. While two-mode data consist of cases that are measured on several variables, three-mode data consist of cases that are measured on several variables at several occasions. As any other statistical technique, the results of three-mode analysis may be influenced by missing data. Three-mode software packages generally use the expectation–maximization (EM) algorithm for dealing with missing data. However, there are situations in which the EM algorithm is expected to break down. Alternatively, multiple imputation may be used for dealing with missing data. In this study we investigated the influence of eight different multiple-imputation methods on the results of three-mode analysis, more specifically, a Tucker2 analysis, and compared the results with those of the EM algorithm. Results of the simulations show that multilevel imputation with the mode with the most levels nested within cases and the mode with the least levels represented as variables gives the best results for a Tucker2 analysis. Thus, this may be a good alternative for the EM algorithm in handling missing data in a Tucker2 analysis.Development Psychopathology in context: famil

Chrono-Nutrition: The Relationship between Time-of-Day Energy and Macronutrient Intake and Children's Body Weight Status

The time of eating has been considered to have an important role in weight regulation. However, it is unknown if there are specific daily patterns of energy and macronutrient distribution that could be more beneficial for metabolic outcomes, especially obesity. This study aimed to assess the effect of time-of-day energy and macronutrient intake at 4 y of age on the weight status at 7 y of age. The study sample included 1961 children from the population-based birth cohort Generation XXI, with data on 3-day food diaries at 4 y and body mass index (BMI) z-scores at 7 y. Dietary patterns based on the collected data were obtained for the distribution of energy and macronutrients across eating occasions. Having a relatively higher energy intake at lunch and supper (OR = 1.19, 95% CI = 1.05 to 1.34) or at mid-afternoon (OR = 1.18, 95% CI = 1.05 to 1.34) at 4 y was associated with higher odds of becoming overweight/obese at 7 y. A relatively higher intake of fat at lunch was positively associated with later children’s odds for being overweight or obese (OR = 1.17, 95% CI = 1.03 to 1.32). These associations were independent of the effect on children’s eating behaviors related to appetite. Our results also show a detrimental relation between skipping breakfast and eating late in the day and children’s body weight. Considering all daily eating occasions, a higher proportion of energy and macronutrient intake at the main meals and a lower proportion during the afternoon and evening seems to be more beneficial for children’s weight. These results emphasize the important role of daily food intake rhythm on excessive weight gain in childhood

How to explore within‑person and between‑person measurement model differences in intensive longitudinal data with the R package lmfa

Intensive longitudinal data (ILD) have become popular for studying within-person dynamics in psychological constructs (or between-person differences therein). Before investigating the dynamics, it is crucial to examine whether the measurement model (MM) is the same across subjects and time and, thus, whether the measured constructs have the same meaning. If the MM differs (e.g., because of changes in item interpretation or response styles), observations cannot be validly compared. Exploring differences in the MM for ILD can be done with latent Markov factor analysis (LMFA), which classifies observations based on the underlying MM (for many subjects and time points simultaneously) and thus shows which observations are comparable. However, the complexity of the method or the fact that no open-source software for LMFA existed until now may have hindered researchers from applying the method in practice. In this article, we provide a step-by-step tutorial for the new user-friendly software package lmfa, which allows researchers to easily perform the analysis LMFA in the freely available software R to investigate MM differences in their own ILD

The Association between Heavy Episodic Drinking and Alcohol-Related Unsafe Sex among Canadian Undergraduate Student Drinkers

Young adults who engage in risky sexual behaviours are placing themselves at risk for serious health problems. This study assessed the extent of alcohol-related unsafe sex among Canadian undergraduate students and examined the association between unsafe sex and heavy episodic drinking as well as drinking motives, drinking locations, age when they first drank alcohol, and illicit drug use. Data were obtained from the 2004 Canadian Campus Survey (N = 4,437). Logistic regression and modified Poisson regression was used to examine associations with unsafe sex. The proportion of students reporting having had unsafe sex was estimated to be at 7.37%. Heavy episodic drinking (RR = 1.609, 95% CI = 1.240 - 2.088), marijuana (RR: 2.204, 95% CI: 1.683 - 2.887) and illicit drug use (RR: 3.397, 95% CI: 2.519 - 4.580) were found to be significantly associated with unsafe sex. These findings can have important implications for the development of interventions

Is school wide adoption of ICT change for the better?

The use of information and communication technologies (ICT) in schools is now an intrinsic part of students’ learning, both inside and outside the classroom. The adoption and impact of ICT on teaching practice and learning outcomes has been a source of keen interest among government policy makers, school leaders, teachers and researchers worldwide. Few empirical studies have been conducted in Australia, or worldwide, that focus on student attitudinal outcomes framed within a design-based paradigm that spans several years. The overarching purpose of this study is to investigate longitudinal change in school climate through its influence on students and teachers, during a period of school-wide transition as ICT were embedded throughout mainstream curricula. An assessment of the impact of ICT on student attitudinal outcomes, in particular, changes in self-esteem over a three-year period of school-wide ICT adoption, is provided through the examination of factors affecting teaching practice and students’ attitudes towards computers and school. A total of 219 teachers and 2560 students from six metropolitan public primary and secondary schools in South Australia participated in the study. The main method of data collection involves the use of online questionnaires suitable for repeated administration over the three-year lifespan of the study, and appropriate for all teachers and those students in Years 5 to 7 in primary school and Years 8 to 10 in secondary school.https://research.acer.edu.au/saier/1019/thumbnail.jp

Nonresponse in survey research: proceedings of the Eighth International Workshop on Household Survey Nonresponse, 24-16 September 1997

"This volume, the fourth in the ZUMA-Nachrichten Spezial series on methodological issues in empirical social science research, takes up issues of nonresponse. Nonresponse, that is, the failure to obtain measurements from all targeted members of a survey sample, is a problem which confronts many survey organizations in different parts of the world. The papers in this volume discuss nonresponse from different perspectives: they describe efforts undertaken for individual surveys and procedures employed in different countries to deal with nonresponse, analyses of the role of interviewers, the use of advance letters, incentives, etc. to reduce nonresponse rates, analyses of the correlates and consequences of nonresponse, and descriptions of post-survey statistical adjustments to compensate for nonresponse. All the contributions are based on presentations made at the '8th International Workshop on Household Survey Nonresponse'." (author's abstract). Contents: Larry Swain, David Dolson: Current issues in household survey nonresponse at Statistics Canada (1-22); Preston Jay Waite, Vicki J. Huggins, Stephen P. Mack: Assessment of efforts to reduce nonresponse bias: 1996 Survey of Income and Program Participation (SIPP) (23-44); Clyde Tucker, Brian A. Harris-Kojetin: The impact of nonresponse on the unemployment rate in the Current Population Survey (CPS) (45-54); Claudio Ceccarelli, Giuliana Coccia, Fabio Crescenzi: An evaluation of unit nonresponse bias in the Italian households budget survey (55-64); Eva Havasi and Adam Marton: Nonresponse in the 1996 income survey (supplement to the microcensus) (65-74); Metka Zaletel, Vasja Vehovar: The stability of nonresponse rates according to socio-demographie categories (75-84); John King: Understanding household survey nonresponse through geo-demographic coding schemes (85-96); Hakan L. Lindström: Response distributions when TDE is introduced (97-112); Vesa Kuusela: A survey on telephone coverage in Finland (113-120); Malka Kantorowitz: Is it true that nonresponse rates in a panel survey increase when supplement surveys are annexed? (121-138); Vasja Vehovar, Katja Lozar: How many mailings are enough? (139-150); Amanda White, Jean Martin, Nikki Bennett, Stephanie Freeth: Improving advance letters for major government surveys (151-172); Joop Hox, Edith de Leeuw, Ger Snijkers: Fighting nonresponse in telephone interviews: successful interviewer tactics (173-186); Patrick Sturgis, Pamela Campanelli: The effect of interviewer persuasion strategies on refusal rates in household surveys (187-200); Janet Harkness, Peter Mohler, Michael Schneid, Bernhard Christoph: Incentives in two German mail surveys 1996/97 and 1997 (201-218); David Cantor, Bruce Allen, Patricia Cunningham, J. Michael Brick, Renee Slobasky, Pamela Giambo, Jenny Kenny: Promised incentivcs on a random digit dial survey (219-228); Eleanor Singer; John van Hoewyk, Mary P. Maher: Does the payment of incentives create expectation effects? (229-238); Edith de Leeuw, Joop Hox, Ger Snijkers, Wim de Heer: Interviewer opinions, attitudes and strategies regarding survey participation and their effect on response (239-248); Geert Loosveldt, Ann Carton, Jan Pickery: The effect of interviewer and respondent characteristics on refusals in a panel survey (249-262); Brian A. Harris-Kojetin, Clyde Tucker: Longitudinal nonresponse in the Current Population Survey (CPS) (263-272); Ulrich Rendtel, Felix Büchel: A bootstrap strategy for the detection of a panel attrition bias in a household panel with an application to the German Socio-Economic Panel (GSOEP) (273-284); Seppo Laaksonen: Regression-based nearest neighbour hot decking (285-298); Rajendra P. Singh, Rita J. Petroni: Handling of household and item nonresponse in surveys (299-316); Susanne Raessler, Karlheinz Fleischer: Aspects concerning data fusion techniques (317-334); Siegfried Gabler, Sabine Häder: A conditional minimax estimator for treating nonresponse (335-349)