Coefficient Logit Research Articles

Technology-assisted instruction with teacher prompts on fraction multiplication word problems: A single-case design with visual analysis and Bayesian multilevel modeling

ABSTRACT This study examines the effects of technology-assisted instruction with teacher prompts on the ability to visualize and solve fraction multiplication word problems for four middle school students with learning disabilities. A multiple-probe design across participants, a type of single-case design, showed a functional relationship between the intervention and the targeted mathematical outcomes. Although there were some fluctuations over time, students demonstrated improvements from the baseline to intervention phases (Tau ranged from 0.76 to 1.00 for visualization and was 1.00 for problem-solving). Researchers employed Bayesian cumulative link mixed effects models to examine the moderating effects of word problem question types. Students showed greater maintenance effects on problem-solving than on visualization tasks, as reflected in changes in level (logit coefficient = 2.6) and trend (logit coefficient = 0.22). Students and teachers perceived vocabulary and multiplication fact practices, as well as the cognitive and metacognitive features embedded in the technology-assisted intervention, to be useful in learning targeted mathematics concepts. This study underscores the role of technology integration in enhancing teachers’ instructional approach and in aiding students’ acquisition and retention of mathematical concepts and skills.

Determinants of life satisfaction among migrants in South Africa: an analysis of the GCRO’s quality of life survey (2009–2021)

BackgroundMigrant populations in any country are a vulnerable group, and psycho-demographic research measuring life satisfaction has been used to assess migrants’ well-being in developed and developing countries. However, South Africa, with its high influx of migrant populations, has investigated these topical concerns from the perspective of xenophobia, with mixed findings. However, no, or very few studies have examined life satisfaction among migrants in South Africa. This study, therefore, extends previous literature by examining the determinants of life satisfaction among South Africa’s internal and international migrant populations.MethodsWe conducted a cross-sectional study from the 2009 to 2021 Gauteng City-Region Observatory (GCRO) Quality of Life (QoL) surveys among migrant populations in two ways: a full sample and a gender-stratified sample. A sample of male and female migrants ranging from 15 to 49 years of age were recruited into this study. Cantril’s Self-Anchoring Ladder Life Satisfaction scale captured their life satisfaction alongside relevant social demographic factors. Descriptive statistics were applied for the data analysis of the demographic factors. Bivariate and multivariate logistics regression analyses were conducted to assess the associations and the predictive factors of life satisfaction among migrants, both internal and international.ResultsThe key findings were the gender distribution of life satisfaction, showing that more international (male – 66.0% and female – 67.1%) migrants reported having a thriving life satisfaction than internal migrants (male – 61.7% and female – 61.5%). Findings from the Pearson correlation coefficient revealed a significant association between the individual, household, and community factors by migrant status (ρ < 0.05). However, the probit coefficients revealed that individual factors (age 48+: AOR = 2.18, 95% CI: 1.13, 3.23, and secondary/higher education: AOR = 1.1., 95% CI: 0.01, 1.19) and household factors (two persons living in households (H/H): AOR = 1.05, 95% CI: 0.50, 1.10), and community factors (international migrant status: AOR = 2.12, 95% CI: 0.08, 2.16) significantly increase the prediction of higher odds of life satisfaction by gender among migrants. The ordered logit coefficients also showed that individual factors (middle and high income and having health insurance) and household factors (receiving SASSA social grant) predicted the highest life satisfaction among migrants (internal and international).ConclusionWe found substantial evidence that individual-, household-, and community-level factors were associated with life satisfaction among migrants. In particular, the pattern of life satisfaction varied slightly between male and female migrants, as well as with migrant status in South Africa. These findings collectively may provide helpful information for policymakers and practitioners to optimise interventions for migrant populations to improve their life satisfaction. Evidence from this study also calls on the government of South Africa to begin tracking the life satisfaction of its nationals, whether migrants or not.

Travel time variation and its impact on the elasticity of route choice model parameters

Travel time is an uncertain variable with inherited uncertainty for many reasons, such as measurement error, perception error, and temporal aggregation variation impacts. Many researchers have considered travel time as an exact value for different limitations, including models’ restrictions and being unaware of the travel time distribution. However, previous studies have shown that standard logit and probit models can result in biased estimations with uncertain variables or variables with errors (EIV). Nevertheless, limited evidence is available on the magnitude of this bias and the impact of the uncertain variable distribution on the estimated parameters. In this paper, we use a simulation-based approach to explore the uncertainty of variables in the models’ estimation. We check the difference between standard and simulation-assisted models for four previously observed distributions with uncertainty values in the range of common coefficient of variation (Cov) of travel time. Results show that the logit's coefficients and elasticity values with EIV consideration can be 63% and 70% different from those of a standard logit model. The probit coefficients and elasticity differ up to 75% and 124%, respectively, in a binary choice situation. Changing the data and models for a multinomial situation shows that logit has 60% biasedness again while probit undergoes up to 800%. A sensitivity analysis on elasticity values of CoV is done, which unveils that both models’ elasticities change according to a similar pattern but with different intensities. The results suggest that the standard logit handles uncertainty better than the probit model. Yet we cannot present any intuitive reason to support this observation or against it. Procedures like those implemented in this research are suggested to address the biasedness of the results.

What are the Determinants of Attitudes Towards Immigrants in Turkey? Evidence from the World Values Survey

In this study, I aim to find the impact of social values and demographic characteristics on attitudes toward immigrants in Turkey. For the empirical analysis, wave 7 data from the World Values Survey (WVS) is used. Individual-level data from 2018 shows that religion and nationalism play a key role in increasing negative attitudes toward immigrants. As individuals' social class improves, their anti-immigrant sentiments tend to decrease. However, when there is concern about potential competition for scarce jobs in the labor market, social class jumps result in discrimination against immigrants. The logit regression coefficients for political opinion, education, age and marital status do not show robust results in different models using different measures of negative attitudes. Thus, demographic characteristics do not systematically influence opinions toward immigration in Turkey. Rather, values and socioeconomic position in society have an impact on opinions toward immigrants.

Comparing logit & probit coefficients between nested models

Social scientists are often interested in seeing how the estimated effects of variables change once other variables are controlled for. For example, a simple analysis may reveal that income differs by race – but why does it differ? To answer such a question, a researcher might estimate a model where race is the only independent variable, and then add variables such as education to subsequent models. If the original estimated effect of race declines, this may be because race affects education, which in turn affects income. What is not universally realized is that the interpretation of such nested models can be problematic when logit or probit techniques are employed with binary dependent variables. Naïve comparisons of coefficients between models can indicate differences where none exist, hide differences that do exist, and even show differences in the opposite direction of what actually exists. We discuss why problems occur and illustrate their potential consequences. Proposed solutions, such as Linear Probability Models, Y-standardization, the Karlson/Holm/Breen method, and marginal effects, are explained and evaluated.

Discrete choice experiment exploring women’s preferences in a novel device designed to monitor the womb environment and improve our understanding of reproductive disorders

ObjectivesThe study aims to determine the relative importance of key attributes of a novel intrauterine device. The device monitors uterine oxygen, pH and temperature in real time with the aim...

The Effect of Distance Intervals on Walking Likelihood in Different Trip Purposes

Increasing private car ownership and car dependency has led to a low share of walking as an active mode as well as congestion, air pollution, and health problems in developing countries. This paper aims to identify and compare the impacts of a selection of socio-economic, travel-related, and built environment variables on walking likelihood and respondents’ sensitivity to the walking distance, both for discretionary and mandatory trips. The analysis drew its origin from 14,463 responses acquired through an extensive travel survey conducted in the city of Qazvin, Iran. The estimated binary logit coefficients show people’s heterogeneity in the walking behavior for discretionary and mandatory trips. The results report a higher likelihood of walking on mandatory trips at almost all distances than the discretionary ones. Furthermore, investigating individual heterogeneity in different trip distances reveals that people aged less than 14 are more likely to choose walking on mandatory trips longer than 2400 m. Besides, those aged 25–44 years old or above 65 have less tendency to choose walking on mandatory trips with distances of 2000–2400 m and 800–1200 m, respectively. These findings are almost different on discretionary trips; compared to other age groups, people aged 15–24 years are less likely to choose walking on discretionary trips with a distance of 800–1200 m. Moreover, in trip distances of 1200–1600 m, the elderlies have a greater tendency to choose walking compared to other age groups. Some implications for more sustainable mobility in human-oriented urban environments are also presented and critically discussed.

Contagions in interconnected power markets

PurposeThis paper offers an alternative approach to assessing contagions in price and load in the Australian interconnected power markets. This approach enabled us to identify a high-risk region and assess the direction of contagions from both buyers' and sellers' perspectives.Design/methodology/approachThe author used a multinomial logit method to measure contagions. Having identified the exceedance and coexceedances, the author estimated the multinomial logit coefficients of the covariates explaining the probability of a certain number of coexceedances.FindingsMarket participants should recognize the presence of contagion risk and scrutinize price and load dynamics in the NSW and VIC regions to anticipate any simultaneous extreme changes. Regulators need to stabilize the demand and supply sides in those regions to minimize any possible contagions.Originality/valueThis paper presents a pioneering study investigating contagion in the Australian interconnected power markets.

Logistic or linear? Estimating causal effects of experimental treatments on binary outcomes using regression analysis.

When the outcome is binary, psychologists often use nonlinear modeling strategies such as logit or probit. These strategies are often neither optimal nor justified when the objective is to estimate causal effects of experimental treatments. Researchers need to take extra steps to convert logit and probit coefficients into interpretable quantities, and when they do, these quantities often remain difficult to understand. Odds ratios, for instance, are described as obscure in many textbooks (e.g., Gelman & Hill, 2006, p. 83). I draw on econometric theory and established statistical findings to demonstrate that linear regression is generally the best strategy to estimate causal effects of treatments on binary outcomes. Linear regression coefficients are directly interpretable in terms of probabilities and, when interaction terms or fixed effects are included, linear regression is safer. I review the Neyman-Rubin causal model, which I use to prove analytically that linear regression yields unbiased estimates of treatment effects on binary outcomes. Then, I run simulations and analyze existing data on 24,191 students from 56 middle schools (Paluck, Shepherd, & Aronow, 2013) to illustrate the effectiveness of linear regression. Based on these grounds, I recommend that psychologists use linear regression to estimate treatment effects on binary outcomes. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

What are the Critical Elements of Satisfaction and Experience in Labor and Childbirth-A Cross-Sectional Study.

The labor experience and satisfaction with childbirth are affected by the care provided (external factors) and individual variables (internal factors). In this paper, we present a descriptive analysis that aims to indicate the strongest correlates of birth experience among a wide range of indicators. The study is a prospective, cross-sectional, self-report survey. It includes the experiences of women giving birth in public and private hospitals in Poland. The two main variables were birth experience and satisfaction with care. The analysis consists of three parts: data pre-processing and initial analysis, explorative investigation, and regression analysis. Among the 15 variables with the highest predictive value regarding birth experience were being informed by the medical personnel, communication, and birth environment. The most significant variables among 15 variables, with the highest predictive value regarding care, were those concerning support, information, and respectful care. The strongest predictor for both, birth experience and satisfaction with care, is the sense of information, with logit coefficients of 0.745 and 1.143, respectively, for birth experience and satisfaction (0.367 and 0.346 for standardized OLS coefficient). The findings demonstrate that by using explanatory variables, one can predict a woman’s description of her satisfaction with perinatal care received in the hospital. On the other hand, they do not have such a significant and robust influence on the birth experience examined by the variables. For both the birth experience and satisfaction with care, the sense of being informed is the highest predictor.

Heterogeneous choice in the demand for agriculture credit in China: results from an in-the-field choice experiment

PurposeIn this study, the authors examined demand-side credit in rural China with the aims of understanding attribute preferences and the willingness of farmers to pay for credit.Design/methodology/approachThe authors implemented an in-the-field discrete choice experiment (DCE) using a D-optimal block (6 × 9 × 3) design applied to 420 farm households across five Chinese provinces (Shandong, Sichuan, Shaanxi, Jiangsu and Henan) in the summer and fall of 2018. The DCE included six attributes including the interest rate, term of loan, type of loan, type of repayment, type of institution and mobile banking services.FindingsConditional and mixed logit results indicated a downward sloping credit demand curve with variable elasticity across regions. Provincial willingness-to-pay (WTP) indicators suggested that farmers were willing to pay a premium for long-term ( 0.03–0.687%) and low collateral credit loans ( 0.79–2.93%). Also, four of five provinces indicated a preference for loan amortization rather than lump-sum payment. Interestingly, in comparison to the Agricultural Bank of China (ABC), only farmers in Shandong, Sichuan and Shaanxi indicated a preference for rural credit cooperatives (RCCs)/banks and the Postal Savings Bank of China (PSBC). Another quite surprising result was bank services, in our case, access to mobile banking did not appear to induce WTP for agricultural credit. While conditional and mixed logit regression coefficients were similar (and therefore robust), the authors found that there was substantial heterogeneity across attribute preferences on term of loan, type of loan and amortization. Preferences for type of lender and mobile banking were generally homogenous. This result alone suggested that lenders should consider offering a suite of credit products with different attributes in order to maximize the potential pool of borrowers. While there were some differences across provinces, farmers appeared to be indifferent to lenders, and it did not appear that offering banking services such as mobile banking had any bearing on credit decisions.Research limitations/implicationsThis paper presents a first step in using in-the-field choice experiments to better understand rural finance in China. Although the sample size satisfies conventional levels of significance and rank conditions, the authors caution against attributing results to China as a whole. Different provinces have different institutional structures and agricultural growing conditions and economies and these effects may differentially affect WTP for credit. Although by all indications farmers were aware of credit, not all farmers, in fact a minority, actually borrowed from a financial institution. This is not unusual in China, but for these farmers, the DCE was posed as hypothetical. Likewise, the study’s design was based on a generic credit product typical of rural China, and the authors caution against making inferences about other products with different attributes and risk structures.Social implicationsThis study is motivated by the rapidly changing dynamic in China's agricultural economy. With specific reference to new laws and regulations about the transfer of land use rights (LURs), China's agricultural economy is undergoing significant and rapid change which will require better understanding by policy makers, lenders and practitioners of the changing credit needs of farmers, including the new and emerging class of commercial farmers.Originality/valueTo the best of the authors’ knowledge, the authors believe that the result provided in this paper present the first use of in-the-field DCE and are the first to be reported in either the English or Chinese literature on rural credit product design.

Examination of Urban Agriculture Contribution to the Household Livelihood Outcome the Case of Bahir Dar City, Ethiopia

The general objective of this study is to examine the contribution of urban agriculture to the household livelihood in case of Bahir Dar city in Ethiopia. The motive for this study were the problems of unemployment, growing poverty, hunger, poor diets, bad air condition, depression as well as the special opportunities provided by the city including the growing demand for food, proximity to markets and availability of cheap resources such as urban organic wastes. The study used both primary and secondary data source. Stratified quota sampling was used by the study to collect primary data. Average annual urban agricultural net revenue per capita was taken as a common measure of all urban agricultural outcomes for target predictor’s. Other predictor variables that assumed to be the determinant of urban agriculture contribution also included in the model. Binary logistic regression technique is used to estimate the logit coefficient. The studies found that the greater correlation of livelihood security’s with average annual urban agricultural net revenue per capita than average annual non- urban agricultural net revenue per capita. The correlation between food, economic, education, health and empowerment security with average annual urban agricultural net revenue per capita were about 0.29, 0.6, 0.19, 0.21 and 0.22 respectively. This target explanatory variable was positive significant effect for food security dependent variable at 5% significance level and at its mean value the probability of more food security agreement were about 0.77, while other predictors also held at their mean value. Finally, this studies paper suggests that urban agriculture helps for household livelihood outcome of food, education, health, empowerment and economic security and should be considered in urban planning.

Determining the Marginal Willingness to Pay for the Cervical Cancer Screening Program in Croatia: A Best-Worst Scaling Approach1

Croatia is faced with a low response to cancer-screening programs, especially the national cervical cancer screening program, which ultimately resulted in its suspension. If judged solely on the basis of revealed preferences, such a poor response would imply that the population assigns a low social value to preventive screening programs. However, the question arises as to whether revealed preferences (the population's response), in the case of the absence of response to a preventive program, provide insight into its value (utility). Therefore, the objective of this paper is to determine the value that respondents assign to different attributes of cervical screening and, in a broader sense, to decide whether the best-worst scaling (BWS) approach is appropriate for determining the marginal willingness to pay (MWTP) for public health programs. The MWTP for certain attributes of cervical cancer screening is derived from the results of a BWS study conducted in Primorje-Gorski Kotar County, Croatia. The cost function was estimated by regressing the conditional logit coefficients (level of utility) of three levels of the cost attribute on its corresponding values, that is, the hypothetical price. Because the sum of the MWTP corresponds with the market price of a gynecological examination in private practice, we conclude that the results obtained by the BWS confirm the revealed preferences (the market value of the service).

On building early-warning systems for preventing the deterioration of financial institutions’ performance

Abstract This paper assesses the financial performance of Romania’s non-banking financial institutions (NFIs) using a neural network training algorithm proposed by Kohonen, namely the Self-Organizing Maps algorithm. The algorithm takes the financial dataset and positiones each observation into a self-organizing map (a two-dimensional map) which can be latter used to visualize the trajectories of an individual NFI and explain it based on different performance dimensions, such as capital adequacy, assets’ quality and profitability. Further, we use the map as an early-warning system that would accurately forecast the NFIs future performance (whether they would stay or be eliminated from the NFI’s Special Register three quarters into the future). The results are promising: the model is able to correctly predict NFIs’ performance movements. Finally, we compared the results of our SOM-based model with those obtained by applying a multivariate logit-based model. The SOM model performed worse in discriminating the NFIs’ performance: the performance classes were not clearly defined and the model lacked the interpretability of the results. In the contrary, the multivariate logit coefficients have nice interpretability and an individual default probability estimate is obtained for each new observation. However, we can benefit from the results of both techniques: the visualization capabilities of the SOM model and the interpretability of multivariate logit-based model.

A Swiss nationwide survey shows that dual guidance is the preferred approach for peripheral nerve blocks

Ultrasound has significantly increased safety and effectiveness in regional anesthesia. However, little is known about its clinical use. We studied clinical approaches currently used by anesthesiologists, conducted a nationwide survey, and analyzed data collected in ordered logistic regression models. All active members of the Swiss Society for Anaesthesiology and Resuscitation (SSAR/SGAR) were asked to participate. Reported practice in nerve localization, safety, and techniques used for peripheral nerve blocks (PNB) were main outcome measures. Experience ranged from 3 to >30 years. The mean number of block techniques mastered was 11.5 ± 5.9. Standard monitoring was regularly used, whereas sterile coats were less frequently used by anesthesiologists who self-estimated a higher level of expertise in PNB (ordered logit coefficient −0.05, 95% CI −0.07 to −0.02, P < 0.001; pseudo r2 = 0.019; probability > Chi2 = 0.02). The more self-estimated expertise anesthesiologists had, the less likely they were to use nerve stimulation in combination with ultrasound (dual guidance) (ordered logit coefficient −0.31; 95% CI −0.85 to −0.03: P = 0.03; pseudo r2 = 0.007; probability > Chi2 = 0.05). The high share of reported standard monitoring meets the recommendations of the Helsinki Patient Safety Declaration. Dual guidance appears to be the preferred approach for safely localizing nerves for PNB in Switzerland.

Binary Response Models with Underlying Heterogeneity: Identification and Interpretation of Effects

Comparison of logit coefficients across groups can be misleading if underlying variance heterogeneity is present, in particular one might find spurious differences of effects that are caused by different variation in the groups. This is quite unsettling given the problem seems not to be well recognized, but the logit model is widely used in applications. In particular the heterogeneous choice model has been used to account for the underlying heterogeneity. However, the heterogeneous choice model can be shown to be a specific interaction or varying-coefficientmodel. Therefore, one has a representation as a heterogeneity model and as an interaction model, but interpretation of effects is quite different for these models. We use the general framework of varying-coefficient models to obtain strategies that might be used to decide if the identified variation of coefficients is due to variance heterogeneity and therefore spurious or a true interaction effect. We will reconsider a data set that has been used by Allison (1999) when identifying the problem of heterogeneity and use further data sets to illustrate the method.

How to Generalize from a Hierarchical Model?

Models of consumer heterogeneity play a pivotal role in marketing and economics, specifically in random coefficient or mixed logit models for aggregate or individual data and in hierarchical Bayesian models of heterogeneity. In applications, the inferential target often pertains to a population beyond the sample of consumers providing the data. For example, optimal prices inferred from the model are expected to be optimal in the population and not just optimal in the observed, finite sample. The population model, random coefficients distribution, or heterogeneity distribution is the natural and correct basis for generalizations from the observed sample to the market. However, in many if not most applications standard heterogeneity models such as the multivariate normal, or its finite mixture generalization lack economic rationality because they support regions of the parameter space that contradict basic economic arguments. For example, such population distributions support positive price coefficients or preferences against fuel-efficiency in cars. Likely as a consequence, it is common practice in applied research to rely on the collection of individual level mean estimates of consumers as a representation of population preferences that often substantially reduce the support for parameters in violation of economic expectations. To overcome the choice between relying on a mis-specified heterogeneity distribution and the collection of individual level means that fail to measure heterogeneity consistently, we develop an approach that facilitates the formulation of more economically faithful heterogeneity distributions based on prior constraints. In the common situation where the heterogeneity distribution comprises both constrained and unconstrained coefficients (e.g., brand and price coefficients), the choice of subjective prior parameters is an unresolved challenge. As a solution to this problem, we propose a marginal-conditional decomposition that avoids the conflict between wanting to be more informative about constrained parameters and only weakly informative about unconstrained parameters. We show how to efficiently sample from the implied posterior and illustrate the merits of our prior as well as the drawbacks of relying on means of individual level preferences for decision-making in two illustrative case studies.

No rationale for 1 variable per 10 events criterion for binary logistic regression analysis.

BackgroundTen events per variable (EPV) is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies.MethodsThe current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth’s correction, are compared.ResultsThe results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect (‘separation’). We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth’s correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation.ConclusionsThe current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis.

Retailer Motivation to Adjust Milk Prices: An Analysis Using Superelasticity of Demand

ABSTRACTSuperelasticity of demand offers a quantitative measure of demand curvature which is an important consideration in firm pricing decisions. We derive superelasticity analytic formulations for logit and random coefficient logit (RCL) demand models that can be used to shed light on firm incentives to alter price, firm cost pass‐through, and market power potential. We use the RCL superelasticities that are obtained via simulated generalized method of moments (GMM) to examine retail market power potential in regards to the marketing of national and store‐brand milk in the U.S. Our findings show that elasticity of demand rises in price for both brands, with elasticity for store brand milk lagging behind that for national brand in magnitude. Similarly, elasticities for national brands tend to decrease faster when prices are on the decline. Such a condition suggests that retailers can identify a pricing position, possibly in relation to national brand prices, at which there is a low incentive to increase or reduce store brand prices. [JEL Codes: D22, L13.]

Trajectory of Suicidal Ideation in Relation to Perceived Overweight From Adolescence to Young Adulthood in a Representative United States Sample

To examine the trajectory of suicidal ideation in relation to perceived overweight from adolescence to young adulthood. Four waves (1995-2008) of the National Longitudinal Study of Adolescent Health that prospectively followed up a nationally representative sample of United States middle and high school students (N= 6,504) were analyzed using hierarchical generalized linear models with a logit link function. Overall, suicidal ideation tended to decrease with age (logit coefficient [coef.]=-.07, p<.001). Participants who perceived themselves as overweight were more likely to think about committing suicide, especially in females, even after controlling for rigorously measured depressive symptoms (coef.= .14, p < .01). Girls were more likely than boys to have suicidal ideation at age 10 (coef.=-.31, p < .001). However, the association between suicidal ideation and perceived overweight became weaker and the prevalence of suicidal ideation became lower as participants grew older. The downward trend in suicidal ideation was steeper in females than in males (coef.= .03, p < .001), with males actually showing a higher prevalence of suicidal ideation than females around 20-30 years of age. Random effects of age, depression, and perceived overweight were insignificant, which indicated that participants did not vary significantly in terms of effects of these variables on suicidal ideation. The results of this study underline the need for development of family, school, and community interventions to address body weight perception to reduce suicidal ideation and attempts, especially among female teenagers.