Not being able to keep up with student loan debt after dropping out can be problematic for two reasons: interest and late fees, and impact on credit.
Once a student who has dropped out is ready to tackle their loan debt, they may be faced with a bigger challenge than they anticipated. When you default on student loans, either federal or private, the default and any associated late or missed payments can be reported on your credit. Even if you can get approved, you may end up with a much higher interest rate than you would have received if you had good credit.
Over time, that higher rate can make borrowing much more expensive.
Neither school dropout nor academic success is determined by the learner alone. From an ecological perspective, there are contributing multi-level and cross-level influences. These influences are found at the individual e. Compiled results of a national household survey a representative sample of 4, households and focus groups with learners, parents, and educators identified four main reasons why learners left school. These included household poverty and cost of education i.
Given this, the current study controls for demographic factors including gender and race, academic achievement, family composition, and socio-economic status, which then allows us to focus on the two main factors of interest to this study, substance use and leisure experience.
Research on the connection between dropout and substance use finds mixed results. Some research suggests dropouts initiate use at an earlier age and demonstrate greater intensity of use Gasper, This association has been found in cross-sectional studies of SA adolescents, where dropouts exhibited greater use of tobacco, alcohol, and illegal substances e. However, using longitudinal data from 8th Graders, Flisher and colleagues found only tobacco to be directly associated with dropout and not alcohol or marijuana, suggesting that the snapshots of use obtained by cross-sectional data may not accurately capture substance use behaviours.
Townsend et al. Supported by both cross-sectional and longitudinal studies, use of tobacco was consistently associated with dropout even after controlling for known covariates e. Youth at-risk for dropout tended to be heavier cigarette smokers and began smoking at an earlier age than their low-risk peers. However, support for the association between other substance use and dropout is less clear and studies have found mixed results.
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At the conclusion of their review, Townsend et al. Leisure is one of the under-researched topics in school dropout that has relevance to the SA context. One of the reasons leisure might be healthy is because experientially, youth feel positive when engaged in meaningful and personally rewarding activities. In these situations, youth typically are not bored and feel more intrinsically motivated.
When youth do not have positive experiences in leisure such as when they are bored, negative outcomes are likely to occur. It is of particular concern when young people experience boredom in leisure and do not have the skills or motivation to change what they are doing into something more interesting. Some research has shown that youth who do have the skills to restructure their experience into something more interesting are more likely to engage in healthy behaviours rather than in risk behaviour Weybright et al. When looking at boredom within the school context, general levels of boredom have also been associated with academic disengagement Strassburg et al.
The current study sought to better understand the occurrence of dropout. Making use of secondary data consisting of eight waves of data between Grade Eight and Grade 11, we used survival analysis to identify the risk of dropping out for both male and female adolescents and examined the influence of substance use and leisure experience on high school learner dropout while controlling for demographic and known predictors.
We hypothesised that: 1 males would have a higher hazard function i.
This homogeneity controlled for factors such as socio-economic status and contextual factors in the environment making it more feasible to identify outcomes. Four schools were randomly assigned to receive the curriculum, and five schools were chosen as matched no-treatment control schools. Learners were followed from the beginning of Grade Eight to the end of Grade 11 with data collected on eight occasions spaced six-months apart between March and October Learners completed surveys during school hours for approximately 30 minutes using a handheld digital device.
Research staff was available at each survey administration to answer questions or assist with difficulties. For the present analysis, control group learners who demonstrated distinct patterns of school attendance were included. These learners This homogeneity in socio-economic indicators is expected given that the sample came from the same geographic region. Measures in the current study included school dropout, substance use, subjective leisure experiences, control variables, known predictors of academic achievement, and demographic variables. School dropout was identified based on the pattern of participation in the school-based survey.
As we will discuss further in the limitations section, using this method as a proxy of dropout has some complications, but given that we obtained comparable data to other studies that focused on SA dropout, we felt comfortable using this strategy. Two patterns were targeted for analyses including a Complete group and a Dropout group.
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The Complete group represented learners who were present and participated in all eight bi-annual measurement occasions from Grade Eight to 11 i. The Dropout group included those present for at least two initial measurement occasions in Grade Eight and not present for at least the two final measurement occasions in Grade 11 i.
Intermittent participators were excluded; for example, if a learner was present for the beginning and end of Grade Eight, missing for the beginning of Grade 9, and returned at the end of Grade Nine, they were excluded from analyses. For this study, we were interested in those who demonstrated a strong pattern of permanent dropout status. Substance use was measured as past month use of alcohol and tobacco at each survey administration. Past month use of alcohol and tobacco were entered as time-varying predictors.
Subjective perceptions of leisure i. Baseline descriptives for dropout predictors of dropout Included in analyses for dropout and non-dropout groups. Leisure boredom was measured using three items e. Gender, living with a parent, previously failing a grade, and number of days absent from school were included as control variables as measured in Wave 1.
Descriptives are included within Table 1. The maximum number of absences reported was included in analyses. The survival function, or rate, is the probability that a learner survives longer than time t. This allows for identification of probabilities of survival at each time t, or Wave. This means a higher hazard rate indicates a worse impact on survival. The counting process method was used, which allowed for substance use and leisure experience predictors to be included at each interval, as time-varying covariates.
Time-varying covariates provide for a more precise estimate of influence on dropout as compared to using stable predictors.
The college dropout rate carries financial implications for students
For example, within the current study past month substance use at Wave 3 is connected to dropout status at the same wave. Cox regression models were tested using three nested models i. Nesting models allowed for identification of the effect of the group of predictors and the additional benefit of adding in subsequent groups of predictors above and beyond the first group. A significant difference indicated that the inclusion of the group of predictors provided a better fit to the data than the previous model.
Model A included only stable demographic and fixed known predictors of dropout. Model B added time-varying substance use predictors past month alcohol and tobacco use. Model C added time-varying leisure experience predictors including boredom, amotivation, intrinsic, and extrinsic motivation. Being in the Dropout group was associated with being male, less likely to live with their mother or father, previously failing a grade, greater number of absences from school, higher rates of alcohol and tobacco use, and lower levels of intrinsic motivation.
Preliminarily, these differences suggested a need for further investigation into the relationship between demographic, substance use, and leisure experience predictors and dropping out of school. We started by estimating the survival and hazard functions for dropping out. Due to the conceptualisation of dropout, survival and hazard functions were stable for Waves 1, 2, 7, and 8. At Waves 1 and 2, survival functions were 1. For the entire sample, at Wave 3, the probability of survival was 0.
The hazard function for the overall sample is visually depicted in Figure 1. The hazard rate increased from Wave 3 to peak at 4 0. The probability of dropping out for both males and females given they made it to the initial interval wave was highest at Wave 4 end of Grade 9; 0. By Wave 6, the proportion surviving was. Figure 1 plots hazard functions by gender and visually depicts the higher hazard function of males. The hazard functions for Waves 1, 2, 7, and 8 remained constant at zero. As seen in Figure 1 , the hazard rate is higher for males than for females.
For both, it increased starting at Wave 2, peaked at Wave 4, decreased to Wave 5, and then demonstrated a slight increase steeper for females to Wave 6. This means the hazard for males to drop out at Wave 4 is 0. Nested Cox regression models were used to assess the relationship of control variables to survival time and to determine whether subsequent models adding substance use and then leisure experience would fit better than the known predictor and demographic model only. Model A results indicated gender, living with mother, and previously failing a grade significantly predicted dropout status.
As compared to males, the hazard of dropout for females was Finally, the hazard of dropout for a learner who has previously failed a grade was Models B and C controlled for these demographic and known predictors. In Model B, past month tobacco use, but not alcohol use, significantly predicted dropout status. The hazard of dropout for a learner who had used tobacco in the past month was Predictors of gender, living with mother, and previously failing a grade remained significant.
Of the leisure predictors added in Model C, only intrinsic motivation significantly predicted dropout status. Predictors significant in Model B of gender, living with mother, and past month tobacco use remained significant while previously failing a grade was no longer significant in Model C. Researchers can obtain information about questions which are most important for their research analysis. Also, implement skip logic in case there are too many questions to be covered in the survey so that according to the chosen options, respondents will be asked only a limited number of questions.
Type of questions: Researcher should pay close attention to the type of questions they include in the survey. Researchers should avoid asking open-ended questions since they take a longer time to answer. Including close-ended questions such as Net Promoter Score question, Likert scale question etc.
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Rewards: Everyone loves rewards — of any sort. Researchers are relying on the knack of offering rewards to prompt respondents to complete the survey. Rewards in form of Amazon gift cards, prepaid Visa reward, Starbucks gift card etc.
Restrict the Length of the Survey: The relationship between a number of questions and the time taken for completion is not always directly proportional. The type of question maneuvers the time taken for survey completion. A researcher should keep this in mind while designing a survey.
Send Reminder Emails: This tip is highly dependent on the target audience of the survey. If the survey is being sent out to a highly engaged audience, researchers can send them reminder emails as it mostly will not annoy them. But, sending reminder emails can be effective in ensuring that respondents take time out to complete the survey.
With a survey software platform such as QuestionPro, researchers can seamlessly analyze dropout rates and create strategies to reduce the survey dropout rates based on the analysis results.
see The reports section of the survey dashboard offers an option for survey dropout analysis. Here are quick steps on survey dropout analysis using QuestionPro:. Under the Reports, there is a Dashboard tab where an option for Participant Statistics will appear:. The first part of this dashboard will have Overall Participation Statistics. At the end of this dashboard is a section of Dropout Analysis. Though you're welcome to continue on your mobile screen, we'd suggest a desktop or notebook experience for optimal results. What is Survey Dropout Analysis and why is it needed?