Multicollinearity test to examine the relationship between explanatory variables.

This study aimed to investigate whether attachment-anxiety, intolerance of uncertainty, and metacognition have indirect effects in the association between ACEs and Obsessive-Compulsive Personality Traits (OCPT) in various network models. Undergraduate psychology students (N = 291) participated in an anonymous 30-minute online survey consisting of a series of self-report questionnaires regarding adverse childhood experiences, attachment, intolerance of uncertainty, metacognition, OCPT, and depression. Bootstrapped serial mediation revealed attachment-anxiety and intolerance of uncertainty had a serial-mediation effect in the association between ACEs and OCPT. Serial mediation was not found for metacognition and attachment-anxiety. However, metacognition alone mediated between child emotional abuse and OCPT. These findings expand our currently limited knowledge regarding the etiology of OCPT and suggest that attachment-anxiety, intolerance of uncertainty, and metacognition may be important contributors for understanding the development of OCPT following ACE exposure.

This item contains the raw data and the STATA dta. file for this project.

List of independent variables for the assessment of virological outcomes of antiretroviral therapy and its determinants among HIV patients in Ethiopia.

This is the public replication file for "Can Raising the Stakes of Election Outcomes Increase Participation? Results from a Large-Scale Field Experiment in Local Elections" forthcoming at BJPols. The archive contains the raw Stata Dataset and the Stata .do file necessary to reproduce the analysis reported in the paper.

This data package aims to replicate the empirical regression results of the paper and includes the following components:A comprehensive dataset in Stata format, encompassing all variables and samples used in the empirical analysis.A Stata do-file containing the regression commands.Three Stata datasets for presenting the income matrices.An Excel spreadsheet for illustrating the income disparity between urban and rural households.

Binary and multivariate logisitic regression analysis of determinants of virological outcomes of antiretroviral therapy among HIV patients in Ethiopia.

BackgroundIonizing radiation is being used more frequently in medicine, which has been linked to recognized biological effects such as cancer and mortality. Radiology services are becoming more widely available in Ethiopian health facilities but there is no compiled record of worker’s radiation dose. So, assessing the magnitude and identifying the associated factors of occupational radiation exposure dose among radiology personnel help to design strategies for radiation protection.ObjectiveThe study was designed to assess the occupational radiation exposure dose and associated factors among radiology personnel in eastern Amhara, northeast Ethiopia, 2021.MethodsCross-sectional study was conducted from March 25 to April 30, 2021, in 57 health institutions among 198 radiology personnel. The study comprised all eligible radiology personnel. The data were collected using an electronic-based (Google form) self-administered questionnaire, and document review. The data were entered into an excel spread sheet and then, exported to Stata 14 software. Linear regression model was used to analyse the data after checking its assumptions. Variables with a p-value < 0.25 were entered into a multiple linear regression analysis, and those with a p-value < 0.05 were judged significant. VIF was used to check for multi-collinearity. Coefficient of determination was used to check the model fitness.ResultsThe mean (± SD) annual shallow and deep dose equivalents of radiology personnel were 1.20 (± 0.75) and 1.02 (± 0.70) mSv, respectively. Body mass index (β = 0.104, 95% CI: 0.07, 0.14), practice of timing (β = -0.43, 95% CI: -0.73, -0.13), working experience (β = -0.04, 95% CI: -0.048, -0.032), and practice of distancing (β = -0.26, 95% CI: -0.49, -0.17) were found to be statistically significant factors of annual deep dose equivalent. In addition, body mass index (β = 0.113, 95% CI: 0.08, 0.15), practice of timing (β = -0.62 95% CI: -0.93, -0.31) and, working experience (β = -0.044, 95% CI: -0.053, -0.036 had statistically significant associations with annual shallow dose equivalent.ConclusionThe annual dose equivalents were two times higher than the global average of annual per caput effective dose due to medical exposure. Body mass index, practice of timing, working experience, and practice of distancing were factors of occupational radiation exposure dose. Strategies focusing on increasing the skill, experience, and lifestyle of radiology personnel would be supreme important means to reduce occupational radiation exposure dose.

Abstract

Abstract

Background: Adolescent girls in Kenya are disproportionately affected by early and unintended pregnancies, unsafe abortion and HIV infection. The In Their Hands (ITH) programme in Kenya aims to increase adolescents' use of high-quality sexual and reproductive health (SRH) services through targeted interventions. ITH Programme aims to promote use of contraception and testing for sexually transmitted infections (STIs) including HIV or pregnancy, for sexually active adolescent girls, 2) provide information, products and services on the adolescent girl's terms; and 3) promote communities support for girls and boys to access SRH services.

Objectives: The objectives of the evaluation are to assess: a) to what extent and how the new Adolescent Reproductive Health (ARH) partnership model and integrated system of delivery is working to meet its intended objectives and the needs of adolescents; b) adolescent user experiences across key quality dimensions and outcomes; c) how ITH programme has influenced adolescent voice, decision-making autonomy, power dynamics and provider accountability; d) how community support for adolescent reproductive and sexual health initiatives has changed as a result of this programme.

Methodology ITH programme is being implemented in two phases, a formative planning and experimentation in the first year from April 2017 to March 2018, and a national roll out and implementation from April 2018 to March 2020. This second phase is informed by an Annual Programme Review and thorough benchmarking and assessment which informed critical changes to performance and capacity so that ITH is fit for scale. It is expected that ITH will cover approximately 250,000 adolescent girls aged 15-19 in Kenya by April 2020. The programme is implemented by a consortium of Marie Stopes Kenya (MSK), Well Told Story, and Triggerise. ITH's key implementation strategies seek to increase adolescent motivation for service use, create a user-defined ecosystem and platform to provide girls with a network of accessible subsidized and discreet SRH services; and launch and sustain a national discourse campaign around adolescent sexuality and rights. The 3-year study will employ a mixed-methods approach with multiple data sources including secondary data, and qualitative and quantitative primary data with various stakeholders to explore their perceptions and attitudes towards adolescents SRH services. Quantitative data analysis will be done using STATA to provide descriptive statistics and statistical associations / correlations on key variables. All qualitative data will be analyzed using NVIVO software.

Study Duration: 36 months - between 2018 and 2020.

Geographic coverage

Homabay,Kakamega,Nakuru and Nairobi counties

Analysis unit

Private health facilities that provide T-safe services under the In Their Hands(ITH) Program.

Universe

1.Adolescent girls aged 15-19 who enrolled on the T-safe platform and received services and those who enrolled but did not receive services from the ITH facilities. 2.Service providers incharge of provision of T-safe services in the ITH facilities. 3.Mobilisers incharge of adolescent girls aged 15-19 recruitment into the T-safe program.

Sampling procedure

Qualitative Sampling

IDI participants were selected purposively from ITH intervention areas and facilities located in the four ITH intervention counties; Homa Bay, Nakuru, Kakamega and Nairobi respectively which were selected for the midline survey. Study participants were identified from selected intervention facilities. We interviewed one service provider of adolescent friendly ITH services per facility. Additionally, we conducted IDI's with adolescent girls' who were enrolled and using/had used the ITH platform to access reproductive health services or enrolled but may not have accessed the services for other reasons.

Sample coverage We successfully conducted a total of 122 In-depth Interviews with 54 adolescents enrolled on the T-Safe platform, including those who received services and those who were enrolled but did not receive services, 39 IDIS with service providers and 29 IDIs with mobilizers. The distribution per county included 51 IDI's in Nairobi City County (24 with adolescent girls, 17 with service providers and 10 with mobilisers), 15 IDI's in Nakuru County (2 with adolescent girls,8 with service providers and 5 with mobilisers), 34 IDI's in Homa Bay County (18 with adolescent girls,8 with service providers and 8 with mobilisers) and 22 IDI's in Kakamega County (10 with adolescent girls,6 with service providers and another 6 with mobilisers.)

Sampling deviation

N/A

Mode of data collection

Face-to-face [f2f]

Research instrument

The midline evaluation included qualitative in-depth interviews with adolescent T-Safe users, adolescents enrolled in the platform but did not use the services, providers and mobilizers to assess the adolescent user experience and quality of services as well as provider accountability under the T-Safe program. Generally,the aim of the qualitative study was to assess adolescents' T-Safe users experience across quality dimensions as well as provider's experiences and accountability. The dimensions assessed include adolescent's journey with the platforms, experience with the platform, perceptions of quality of services and how the ITH platforms changed provider behavior and accountability.

Adolescent in-depth interview included:Adolescent journey,Barriers to adolescents access to SRH services,Community attitudes towards adolescent use of contraceptives,Decision making,Factors influencing decision to visit a clinic,Motivating factors for girls to join ITH,Notable changes since the introduction of ITH,Parental support ,and Perceptions about T-Safe.

Service providers in-depth interview included;Personal and professional background,Provider's experience with ITH/T-safe platform,Notable changes/influences since the introduction of ITH/T-safe,Influence/Impact on the preference of adolescent service users and health care providers as a result of the program,Impact/influence of ITH on quality of care,Facilitators and barriers for adolescents to access SRH services,Mechanisms to address the barriers,Challenges related to the facility,Feedback about facility from adolescents,Types of support needed to improve SRH services provided to adolescents Scenarios of different clients accessing SRH services,and Free node.

Mobilisers in-depth interview included;Mobilizer responsibilities and designation,Job description,Motivation for joining ITH,Personal and professional background,Training,Mobilizer roles in ITH,Mobilization process ,Experience with ITH platform,Key messages shared with adolescent about ITH/ Tsafe during enrollment,Motivating factors for adolescents to join ITH/Tsafe,Community's attitude towards ITH/Tsafe,Challenges faced by mobilizers when mobilizing adolescents for Tsafe,Adolescents view regarding platform,Addressing the challenges ,andFree node

Cleaning operations

Qualitative interviews were audio-recorded and the audio recordings were transmitted to APHRC study team by uploading the audios to google drive which was only accessible to the team. Related interview notes, participant's description forms and Informed consent forms were transported to APHRC offices in Nairobi at the end of data collection where the data transcription and coding was conducted. Audio recordings from qualitative interviews were transcribed and saved in MS Word format. The transcripts were stored electronically in password protected computers and were only accessible to the evaluation team working on the project. A qualitative software analysis program (NVIVO) was used to assist in coding and analyzing the data. A “thematic analysis” approach was used to organize and analyze the data, and to assist in the development of a codebook and coding scheme. Data was analyzed by first reading the full IDI transcripts, becoming familiar with the data and noting the themes and concepts that emerged. A thematic framework was developed from the identified themes and sub-themes and this was then used to create codes and code the raw data.

Response rate

N/A

Sampling error estimates

N/A

It is a widely accepted fact that evolving software systems change and grow. However, it is less well-understood how change is distributed over time, specifically in object oriented software systems. The patterns and techniques used to measure growth permit developers to identify specific releases where significant change took place as well as to inform them of the longer term trend in the distribution profile. This knowledge assists developers in recording systemic and substantial changes to a release, as well as to provide useful information as input into a potential release retrospective. However, these analysis methods can only be applied after a mature release of the code has been developed. But in order to manage the evolution of complex software systems effectively, it is important to identify change-prone classes as early as possible. Specifically, developers need to know where they can expect change, the likelihood of a change, and the magnitude of these modifications in order to take proactive steps and mitigate any potential risks arising from these changes. Previous research into change-prone classes has identified some common aspects, with different studies suggesting that complex and large classes tend to undergo more changes and classes that changed recently are likely to undergo modifications in the near future. Though the guidance provided is helpful, developers need more specific guidance in order for it to be applicable in practice. Furthermore, the information needs to be available at a level that can help in developing tools that highlight and monitor evolution prone parts of a system as well as support effort estimation activities. The specific research questions that we address in this chapter are: (1) What is the likelihood that a class will change from a given version to the next? (a) Does this probability change over time? (b) Is this likelihood project specific, or general? (2) How is modification frequency distributed for classes that change? (3) What is the distribution of the magnitude of change? Are most modifications minor adjustments, or substantive modifications? (4) Does structural complexity make a class susceptible to change? (5) Does popularity make a class more change-prone? We make recommendations that can help developers to proactively monitor and manage change. These are derived from a statistical analysis of change in approximately 55000 unique classes across all projects under investigation. The analysis methods that we applied took into consideration the highly skewed nature of the metric data distributions. The raw metric data (4 .txt files and 4 .log files in a .zip file measuring ~2MB in total) is provided as a comma separated values (CSV) file, and the first line of the CSV file contains the header. A detailed output of the statistical analysis undertaken is provided as log files generated directly from Stata (statistical analysis software).

The dataset contains the data necessary for replicating the analysis of Brown and Yucel in their paper "What Drives Natural Gas Prices?" The dataset also includes the expanded dataset with observations until June 2017. The Stata dofile runs all of the commands necessary to produce the estimates in my replication study of "What Drives Natural Gas Prices?" entitled "Revisiting the Drivers of Natural Gas Prices." If the dataset is opened in Stata, then the dofile will produce the replication estimates.

De-identified Stata dataset and do-file used to publish the report to 3ie on the project, "The use of peer referral incentives to increase demand for voluntary medical male circumcision in Zambia" (project code TW3.16). This project was funded under Thematic Window 3 on voluntary medical male circumcision.

Descriptive statistics of HIV patients on antiretroviral therapy in Ethiopia.

The data set (saved in Stata *.dta and .txt) contains all observations (Norwegian supreme court cases 2008-2018 decided in five-justice panels) and variables (independent variables measuring complexity of cases and the dependent variable measuring time in hours scheduled for oral arguments) relevant for a complete replication of the the study. ABSTRACT OF STUDY: While high courts with fixed time for oral arguments deprive researchers of the opportunity to extract temporal variance, courts that apply the “accordion model” institutional design and adjust the time for oral arguments according to the perceived complexity of a case are a boon for research that seeks to validate case complexity well ahead of the courts’ opinion writing. We analyse an original data set of all 1,402 merits decisions of the Norwegian Supreme Court from 2008 to 2018 where the justices set time for oral arguments to accommodate the anticipated difficulty of the case. Our validation model empirically tests whether and how attributes of a case associated with ex ante complexity are linked with time allocated for oral arguments. Cases that deal with international law and civil law, have several legal players, are cross-appeals from lower courts are indicative of greater case complexity. We argue that these results speak powerfully to the use of case attributes and/or the time reserved for oral arguments as ex ante measures of case complexity. To enhance the external validity of our findings, future studies should examine whether these results are confirmed in high courts with similar institutional design for oral arguments. Subsequent analyses should also test the degree to which complex cases and/or time for oral arguments have predictive validity on more divergent opinions among the justices and on the time courts and justices need to render a final opinion.

De-identified Stata dataset and do-file used to publish the report to 3ie on the project, "Optimising the use of economic interventions to increase demand for voluntary medical male circumcision in Kenya" (project code TW3.05). This project was funded under Thematic Window 3 on voluntary medical male circumcision.

Abstract

There is one Stata dataset (faim_data_labeled.dta) associated with this study. It was constructed by the research team through original data collection in multiple waves.

Documentation

Guide to dataset:

Full project name: The Effect of Savings Accounts on Interpersonal Financial Relationships: Evidence from a Field Experiment in Rural Kenya

PIs: Pascaline Dupas, ,Anthony Keats, Jonathan Robinson

Unique ID: 510

Location: Western Kenya

Sample: 989 households

Timeline: 2009 to 2012

Target Group: Rural population

Outcome of Interest: Household finance

Intervention Type: Savings

Associated publications:

More information: https://www.povertyactionlab.org/evaluation/expanding-banking-access-rural-poor-kenya-challenges-and-opportunities

Section 2

Description and codebook for subset of harmonized variables:

Section 3

Survey instruments:

Randomised and quasi-randomised controlled trials of brief lifestyle interventions delivered at any stage during pregnancy, and across the BMI spectrum, were included. Studies of that included pregnant women diagnosed with any complications that might affect diet or physical activity behaviours were excluded. Eligible interventions had to be ‘brief’, where the intervention could be delivered during a routine point of contact (face to face or via telephone) (Werch et al., 2006). An inclusive approach to study selection was taken. Interventions could be delivered over more than one point of contact if the duration was kept intentionally brief and could realistically be delivered within a national healthcare system, without requiring significant expansion of workforce or training. For one intervention where duration of contact between participants and the healthcare practitioner was unclear, the study was retained for the purpose of the review (Jeffries, Shub, Walker, Hiscock, & Permezel, 2009).

Comparator groups in the eligible trials needed to be a standard care control group. Interventions had to report on the effectiveness of changing energy balance behaviours (either diet, physical activity and/or weight monitoring behaviours) in pregnant women. The primary outcome of interest from the brief interventions was total GWG in kilograms, reported as the change in weight from first point of entry into the antenatal care pathway (i.e. baseline) to just before delivery (at variable time points in the third trimester).
Meta-analyses were conducted on GWG as a continuous outcome (in kg) and as a binary outcome (proportion of pregnant women exceeding IOM GWG guidelines). Mean differences in total GWG in kilograms between the intervention and control groups were calculated for studies that reported continuous outcomes. In studies that compared the brief intervention to a more intense intervention group, only the comparison against standard care was taken forward for quantitative pooling. For all dichotomous outcomes, odds ratios for the likelihood of exceeding IOM-recommended GWG were calculated. Intention–to-treat data were used where reported by the individual studies. To estimate the overall pooled weighted mean effect size of the interventions, random effects models were chosen to allow for anticipated between-study variance (DerSimonian & Laird, 1986). Subgroup analyses were conducted, comparing interventions for women who entered pregnancy with overweight or obesity (BMI >25 kg/m2) compared to interventions delivered to women across the BMI spectrum. Further subgroup analyses by risk of bias and the brief intervention delivery strategy were also undertaken.
For meta-analysis, assessment of between-study heterogeneity was judged by the p-value for heterogeneity and calculation of the I2 value. Significance of subgroup and sensitivity analysis was judged by the p value for heterogeneity (Higgins & Green, 2008). P-values of <0.05 were considered statistically significant. All statistical analyses were undertaken in Stata 15/SE (StataCorp, 2017).
These are the datasets used for the meta-analysis.

Annual data on population of 30 Alaska boroughs, Census areas or municipalities, 6 larger Alaska regions, and the state as a whole for 1990 through 2022. Also includes summary measures of longer-term population change, natural increase, net migration, and average annual growth. Dataset combines, and organizes with a place/time format, information from multiple tables published by the Alaska Department of Labor and Workforce Development. Contains 25 variables and 1,184 observations (place/years); Complete datasets are provided in both csv and Stata formats.

The summary of the pooled estimates of the HR, RR, and OR per predicting factors of mortality in patients with drug-resistant tuberculosis.

The list of independent variables and their definitions and measurements.

The Panel Study of Income Dynamics–Social, Health, and Economic Longitudinal File (PSID-SHELF) provides an easy-to-use and harmonized longitudinal file for the Panel Study of Income Dynamics (PSID), the longest-running nationally representative household panel survey in the world.PSID-SHELF accentuates the PSID's strengths through (1) its household panel structure that follows the same families over multiple decades; and (2) its multigenerational genealogical design that follows the descendants of panel families that were originally sampled in 1968, with immigrant sample refreshers in 1997–1999 and 2017. Every individual who has ever been included in the PSID's main study is included in the PSID-SHELF data, with over 80,000 people observed, some of them across more than 40 survey waves (1968–present). The current version of PSID-SHELF includes 41 waves of survey data, ranging from 1968 to 2019.The file contains measures on a wide range of substantive topics from the PSID's individual and family files, including variables on demographics, family structure, educational attainment, family income, individual earnings, employment status, occupation, housing, and wealth—as well as the essential administrative variables pertaining to key survey identifiers, panel status, sample weights, and household relationship identifiers. PSID-SHELF thus covers some of the most central variables in PSID that have been collected for many years. PSID-SHELF can easily be merged with other PSID data products to add other public-use variables by linking variables based on a survey participant’s individual and family IDs.Despite a focus on longitudinally consistent measurement, many PSID variables change over waves, e.g., thanks to new code frames, topcodes, question splitting, or similar. PSID-SHELF provides harmonized measures to increase the ease of using PSID data, but by necessity this harmonization involves analytic decisions that users may or may not agree with. These decisions are described at a high level in the PSID-SHELF User Guide and Codebook, but only a close review of the Stata code used to construct variables in the data will fully reveal each analytic decision. The Stata code underlying PSID-SHELF is openly accessible not only to allow for such review but also to encourage users, as they become more comfortable with PSID, to use and alter the full code or selected code snippets for their own analytic purposes. PSID-SHELF is entirely based on publicly released data and therefore can be recreated by anyone who has registered for PSID data use.Despite careful and multiple code reviews, it is possible that the code used to produce PSID-SHELF contains errors. The authors therefore encourage users to review the codes carefully, to report any mistakes and errors to us (psidshelf.help@umich.edu), and take no responsibility for any errors arising from the provided codes and files. Current VersionPSID-SHELF, 1968–2019, Beta Release 2023.01Recommended CitationsPlease cite PSID-SHELF in any product that makes use of the data. Anyone who uses PSID-SHELF should cite the data or the PSID-SHELF User Guide and Codebook—and, as required by the PSID user agreement, the main PSID data.PSID-SHELF data:Pfeffer, Fabian T., Davis Daumler, and Esther M. Friedman. PSID-SHELF, 1968–2019: The PSID’s Social, Health, and Economic Longitudinal File (PSID-SHELF), Beta Release. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor],