Methods Sixty-five students (59% male, 65%. Results Students averaged 323±143.0 counts.min -1 and 51±25.5 min.d -1 of MVPA. Minutes of MVPA.d -1 were greater on weekdays compared to the weekend (52±27.3 vs. 43±39.7 min.d -1, respectively; p=0.05). However, students wore the accelerometer less on the weekends (weekdays=17.2±3.0, weekend=14.9±6.8 hrs.d -1). Expressing minutes of MVPA as a percentage of the number of minutes of available data, students spent approximately 5% of their time in MVPA on weekdays and weekends. Forty-five percent of students had 7-days of data, 51% had 4-6 days, and 5% had fewer than four days.

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On average, students wore the accelerometer for 17±3.2 hrs.d -1 (range 12.0-23.8 hrs.d -1). Introduction Low physical activity and high inactivity are major public health problems in youth (,, ). The decline in physical activity begins in early adolescence and appears more pronounced in females, compared to males (). Also, low physical activity is more prevalent in certain minority populations, especially African-American and Hispanic youth (). Further, the prevalence of obesity () and type II diabetes (, ) among adolescents is increasing and there is evidence to suggest that low physical activity levels and high amounts of sedentary behaviour are at least partially responsible (,, ).

There is also evidence that low-income youth are disproportionately affected by obesity, and that physical inactivity, as well as certain dietary practices contribute to this disparity (). Minority and low-income youth are well represented in the nation's alternative high school system. Alternative high schools (AHS) serve students who are at high risk of failing or dropping out of regular high school or have been expelled because of behavioral problems (). Nationwide, well over one-half million students are enrolled in alternative school programs (, ).

School districts offering alternative programs are more likely than other districts to be urban, have high minority enrollments and high poverty concentrations (). Compared to youth attending traditional high schools (THS), AHS students report a higher prevalence of a number of health risk behaviors that include substance use, suicidal behaviour, violence-related injuries, sexual behavior, and unhealthy dietary practices (). There are also important differences in levels of physical activity among THS and AHS students. Although moderate physical activity levels (walking or bicycling ≥ 30 minutes on 5 of 7 days preceding the survey) are similar for AHS (25%) and THS students (24%), AHS students are significantly less likely (48%) to engage in vigorous physical activity (activities that made them sweat and breathe hard for ≥ 20 minutes on 3 of 7 days preceding the survey) or participate on school and community sports teams (25%) compared to THS students (63% and 58%, respectively) (, ). Indeed, AHS females appear to be among the least active youth in the country, with only 32% reporting VPA on 3 or more of the 7 days prior to the survey, compared with AHS males (59%) and THS females (55%) and males (70%) (, ). AHS students represent a high risk population of youth that are often overlooked for health promoting services and little is known about their physical activity behaviour (, ). The primary aim of this study was to objectively measure the physical activity of AHS students.

A secondary aim included assessment of student's compliance with wearing a physical activity accelerometer. The findings of this study will identify the most salient targets for physical activity intervention and facilitate accurate measurement of PA among this high-risk population. Schools and Subjects This paper presents baseline data from a school-based physical activity and dietary intervention pilot study conducted in alternative high schools in the Minneapolis and St. Paul metropolitan area. Six schools (urban=4; suburban=2) were contacted and agreed to participate in the Team COOL (Controlling Overweight and Obesity for Life) pilot study. The schools represent a convenience sample of alternative high schools in the Twin Cities metropolitan area whose principals had participated in previous research assessing the feasibility of conducting alternative school-based intervention research with a physical activity and dietary focus (). Schools ranged in size from 27 to 142 students.

There was a high percentage of minority students (44% African-American, 7% Latino, 7% Asian, and 3% American Indian) and students receiving free or reduced price school meals (61%). Data were collected in the fall of 2006, prior to school level randomization to intervention conditions. All students attending the study schools were invited to participate in measurement, which included a self-report survey, and height/weight measures taken by staff. Due to a limited number of accelerometers, a sub-sample of students was selected to wear accelerometers for 7 days. All participating students received a $5 retail gift card for completing the survey and anthropometric measures.

Those wearing accelerometers received an additional $5 gift card for every day the monitor was worn for at least 10 hours, and an additional $10 gift card for returning the monitor and completing an additional physical activity survey (not presented in this paper). Prior to scheduled measurement, research staff visited schools and classrooms to extend an invitation for participation, review measurement procedures and distribute parental consent forms to those younger than 18 years old. Written signed parental informed consent forms were returned prior to the beginning of measurement. On the day of measurement, all students provided signed assent prior to commencing measurement activities. Across the six schools, 145 students completed surveys and height/weight measures.

In the two larger suburban schools, a random sample of students was selected to wear an accelerometer; in the urban schools with fewer students, all were invited to wear an accelerometer. A total of 111 students were selected to wear an accelerometer for seven days. Accelerometer data collected from the two suburban schools were not available due to insufficiently charged batteries (n = 44). From the four urban schools, two monitors experienced technical failure resulting in accelerometer data for a total of 65 subjects. The study was approved by the University's Committee on the Use of Human Subjects in Research. Physical Activity The ActiGraph physical activity monitor, model GT1M (ActiGraph, LLC, Pensacola, FL) was used to collect seven days of physical activity data using 30-second epochs (data collection intervals).

The monitor is an objective measure of physical activity and has been previously validated for use with children in laboratory and field settings (,, ). It is a small (5.1×3.8×1.5 cm), lightweight (42.6 g) single plane (vertical) accelerometer that collects and stores accelerations from 0.05-2.00 G with a frequency response of 0.25–2.50 Hz. These settings capture normal human motion but will filter out high frequency vibrations such as operating a lawn mower or from mechanical sources (). The analog acceleration is filtered and converted to a digital signal and this value (count) is stored in user-specified time intervals; 30-seconds for this study. After data collection, each monitor was downloaded to a computer for subsequent data reduction and analysis.

At monitor distribution, trained research staff fit an elastic belt with an attached monitor to each student, according to a standardized protocol. The students were told not to adjust the belt once it was fitted.

Students were given written and verbal instructions on the use and care of the monitors and were instructed to wear the monitor during all waking hours except when swimming, bathing, or sleeping. Telephone calls were made to the students prior to the weekend to answer questions and remind students to wear the monitor. A second call was made the day before the monitor was due to be collected at school.

ActiGraph Data Reduction ActiGraph data were reduced using a custom developed software program (). All data contained within the time frame starting from when the monitor was initialized until the same time the following week (end time) were processed. For days 2 through 7, data from 00:00:30 until midnight was reduced to summary variables. Data from days one and eight were combined to form a composite seventh day. Daily inclusion criteria were established to determine days and times with acceptable accelerometer data. Blocks of time incorporating at least 30 continuous minutes of “0” output were considered to be times when the subject was not wearing the monitor.

Using this 30-minute rule resulted in an average of 2.5 ± 1.05 bouts of continuous zeroes per day. These data points were eliminated and not used in any calculations.

Also, days with less than 10 hours of data were eliminated from data reduction to account for unrepresentative days of activity. No data were imputed for these analyses. The reduced data were placed into three data sets (usual, weekdays, weekend). Previously, it has been shown that four days of activity monitoring are needed to provide a reliable estimate (ICC = 0.80) of usual physical activity (). Students with at least four out of seven days of data were retained for the usual data set.

The weekday data set contained reduced data for all weekdays meeting inclusion criteria (3-5 days). For the weekend data set, students were required to have at least one weekend day that met the daily inclusion criteria. After applying inclusion criteria to the data, summary compliance and physical activity variables were calculated.

Compliance was assessed by calculating the number of days with at least 10 hours of data and the average number of hours of data per day. D7 Premium Keygen. Several physical activity summary variables were calculated.

Average ActiGraph counts per minute was calculated as the total counts for all included days divided by the total number of minutes the monitor was worn for all included days. Time spent in MVPA was calculated in two ways: 1) the average number of minutes per day spent in MVPA and 2) the average percent of time spent in MVPA each day. The time spent in MVPA was calculated using age-specific count cutoffs for students less than 17 years old (e.g., for a 16-year old, ≥ 3.0 METs, ≥ 940 counts per 30-sec) (). The age-specific cutoff for 17 year olds is 1034 counts per 30-sec, which is greater than the adult cutoff of 976 counts per 30-sec ().

Therefore, for students who were at least 17 years old, the adult cutoff of 976 counts per 30-sec was applied. Time spent in sedentary behaviour was defined as the average number of epochs below 50 counts per 30-seconds (). Time spent in light intensity activity was defined as the average number of 30-sec epochs between the sedentary and MVPA count cutoff points. Statistical Analysis All analyses were performed using SAS version 9.1, with significance set at the p = 0.05 level.

Since gender differences in physical activity often exist, all analyses were stratified by gender. T-tests and chi-square analyses were performed to assess gender differences for sociodemographic variables. Chi-square and general linear models were used to identify demographic differences for compliance and physical activity variables. Log transformed values were used for analyses of skewed dependent variables. Repeated measures general linear models were used to identify differences in physical activity variables between weekdays and weekends. Subject Characteristics for the total sample and by gender Comparing the accelerometer sample (n=65) to the remaining sample of students (those with survey data but whom were not selected to wear the accelerometer, n=80), there were no significant differences by age, gender distribution, parental work or education status, and receipt of public assistance. However, the accelerometer sample had a greater percentage of African-American students (51% vs.

16%, respectively; χ 2 DF=2 = 28.9, p. Physical Activity contains the physical activity data by gender. When data are expressed in absolute minutes, males were more active than females but both genders accumulated a similar number of minutes sedentary behavior. When expressed relative to the total wear time, males still spent more time in MVPA but less time in sedentary behaviour, compared to females. Gender differences for vigorous physical activity were statistically significant for the absolute minutes per day, with males accumulating more vigorous physical activity compared to females (absolute, 4 ± 8.6 vs. ActiGraph-based physical activity variables calculated for students with at least 4 days of data (n=62) Students recorded greater average count per minute values on weekdays compared to weekends (323.9 ± 150.63 vs.

302.9 ± 188.12, p = 0.045) and males were more active than females on weekdays and weekends (p = 0.01 and 0.04, respectively). Presents the time frame (weekday and weekend) physical activity data by gender for absolute minutes of time spent in sedentary, light, and MVPA.

No significant time by gender interactions were detected. Therefore, tests for weekday and weekend differences were conducted on the full Actigraph sample. No significant gender differences were detected for sedentary or light activity (p = 0.61 and 0.81, respectively). Students accumulated significantly more minutes of MVPA on weekdays compared to weekends (p = 0.045), and males accumulated more minutes of MVPA on weekdays (p. Males and females physical activity on weekdays and weekends expressed as absolute minutes per day (Mean + SD) Since the students wore the monitors for approximately two hours less on the weekends, comparisons were also made based on the percentage of time spent in the intensity categories ().

When expressed relative to the amount of wear time, sedentary and light activity were similar for weekdays and weekends (p = 0.64 and 0.24, respectively). Similarly, there was no significant difference in the percent of time spent in MVPA between weekdays and weekends (p = 0.128). The gender difference for MVPA was only seen for the weekend (p = 0.008) but not for the weekdays (p = 0.147). Males and females physical activity on weekdays and weekends expressed as the percent of time spent in that intensity category (Mean + SD) An age-related decline in MVPA was detected for the entire sample with the 14-15, 16-17, and 18-19 year olds obtaining 60.9 ± 22.85, 52.6 ± 28.31, and 40.4 ± 20.85 MVPA min.day -1 (p = 0.04). When separated by gender, cell sizes within each age group category were small (range 5 – 14 students).

Still, decreasing age-related trends were observed for both genders with the males decreasing from 67.0 ± 24.61 to 47.9 ± 17.77 (p = 0.10) and the females decreasing from 47.4 ± 10.73 to 30.7 ± 21.44 MVPA min.day -1 (p = 0.26). Compliance The compliance results for the total sample and by gender are provided in. Overall, 45% of the students provided seven days of quality data with a greater percentage of males (52.6%) meeting this level of compliance, compared to females (33.3%). Ninety-five percent of students provided at least four days of data (); 94% provided at least three weekdays of data; 89% provided at least one weekend day of data. Slightly more males than females attained these compliance levels, but the difference was not statistically significant. On days that met inclusion criteria, students wore the monitors for approximately 17 hours per day across all days and weekdays, but only 15 hours per day during the weekend.

There were no gender differences in the hours worn per day for all days, weekdays, or weekend days. Compliance with Wearing the Accelerometer for the Full Sample and by Gender Differences in compliance (days/week and hours worn/day) by demographic characteristics revealed several significant differences.

The youngest students (14-15 years old) accumulated more hours per day (19.4 ± 3.31 hours) than the 16-17 year olds (16.3 ± 2.61 hours) and the 18-19 year olds (16.2 ± 2.93 hours) (p=0.0019), and a greater number of complete days of data (6.6 ± 0.73 days) compared to the 18-19 year olds (5.6 ± 1.34 days) (p = 0.05). Students receiving free or reduced school meals accumulated approximately one more day of complete data (6.2 ± 1.02 days) compared to those not receiving meal assistance (5.3 ± 1.75 days) (p = 0.02). Students working at least 40 hours or more per week accumulated significantly more hours per day of accelerometer data (20.5 ± 3.7 hours) compared to part-time workers (17.0 ± 2.99 hours) or non-workers (16.5 ± 3.01 hours) (p = 0.05).

Since there were only four students working at least full time, an additional analysis compared all working students to non-working students. Working students accumulated significantly more hours per day of accelerometer data (17.5 ± 3.30 hours) compared to those reporting no work for pay (16.5 ± 3.01 hours) (p = 0.02).

Discussion The aims of this paper were to describe the objectively measured physical activity levels and student compliance with wearing a physical activity accelerometer among alternative high school students. Overall physical activity, assessed by minutes spent in MVPA was low (51 minutes of MVPA per day) with age-related declines observed for both males and females. Low levels of physical activity were especially true for females who averaged only 38 minutes of MVPA per day and only one minute per day of vigorous physical activity. Compliance with wearing the accelerometers was good with nearly one-half wearing the accelerometer for at least 10 hours per day on all seven days of measurement. Furthermore, almost all students accumulated at least four days of quality, usable data.

These findings indicate that high-risk AHS students are in desperate need of physical activity intervention programs and accelerometry data are an acceptable method of data collection in this population. Comparison of physical activity levels of this sample of urban alternative high school students with other published studies requires caution due to the lack of standardization in the procedures used for reducing accelerometer data to summary variables. Comparisons of our study findings to the largest study to date using accelerometry in youth () suggests that male and female AHS students accumulate fewer average counts per minute than their counterparts in the NHANES sample (AHS: males = 364 ± 159.7 and females = 262 ± 85.5, and NHANES 16-19 year olds: males = 428.9 ± 11.3 and females = 327.8 ± 12.1).

Despite this, minutes of MVPA per day was actually higher in the current sample of AHS males (60 ± 25.8) and females (38 ± 18.6) compared to the 16-19 year old males (32.7 ± 2.2) and females (19.6 ± 2.4) from the NHANES sample. This discrepancy may be due to the inclusion of 14-15 year olds in the AHS sample. Also, there are several differences in accelerometer data processing between the NHANES and the present study. For students up to 17 years old, NHANES chose to use the same count cutoff equation as in the current study () but used 4-METS in the equation while we chose 3 METS. This choice is not clear cut as the true MET value of a given activity will likely decrease in a linear fashion with age rather than an abrupt change at 17 years old. The result of these different MET values applied to the Freedson/Trost cutoff equation would be a higher count cutoff for the NHANES sample and, all other things being equal, a lower number of minutes categorized as MVPA.

Also, the slightly higher count cutoff used by NHANES (2020 counts per minute) for the 18 to 19 year olds would have the same effect of reducing the number of minutes categorized as MVPA. Unfortunately, there is still considerable discrepancy between data processing methods and recent reports (Masse 2007) do indicate that these decisions can affect outcomes such as minutes of MVPA. However, the counts per minute outcome is not subject to these processing differences and would suggest that the urban AHS students sampled here may have accumulated less physical activity than those in the general population sampled by NHANES. Two additional studies were identified that used the ActiGraph accelerometer to measure youth physical activity and similar count cutoffs to categorize physical activity intensity (,, ). The Amherst Health and Activity (AHA) Study collected 7-days of data on 1 st through 12 th grade students (, ). Males and females in the 10 th through 12 th grades (similar in age to the students in the current study) accumulated a median of 61 and 55 minutes of MVPA per day, respectively.

Similarly, Patrick et al. Reported that among 330 11 to 15 year olds in the PACE+ intervention, males and females accumulated an average of 72 and 53 minutes of MVPA per day, respectively (). Males in the current study accumulated similar levels of MVPA as males in the previous studies (60 minutes per day), however, females accumulated only 38 minutes of MVPA per day. In contrast, middle school females from the Trial of Activity for Adolescent Females (TAAG) () accumulated even fewer minutes of MVPA with only 23.7 ± 11.7 for sixth graders and 22.2 ± 11.2 for eighth graders. However, the very low activity levels among the TAAG sample is likely due to the accelerometer cutoff of 1500 counts/30 sec whereas the AHA, PACE+ and the Team COOL studies used the age-specific count cutoffs established by Freedson et al. Using this age-specific cutoff, more 30-second time intervals would be classified as MVPA, compared to the TAAG cutoff.

The age-specific count cutoffs were applied to the current data due to the 6-year age span (14-19 years) of the Team COOL sample compared to the younger sample and smaller age range for the middle school females in TAAG. Another difference between the TAAG count cutoff and the Freedson age-specific cutoffs is the TAAG cutoff is based on MVPA being defined as ≥ 4.6 METS (or 4.6 times greater than resting metabolic rate).

The Freedson age-specific cutoffs used in Team COOL (and the AHA and PACE+ studies) were based on a definition of MVPA as ≥ 3.0 METS. Children's higher resting metabolic rates, compared to adolescents and adults, supports the use of the higher 4.6 METS for the TAAG sample.

However, given the older ages of the students in the current study, the ≥ 3-MET definition of MVPA was retained. The gender difference in physical activity observed among the AHS students is similar to differences seen in numerous other studies using objective monitoring in THS students (,, ) or self report (, ) Females in this sample were 37% less active than their male counterparts (38 vs.

60 minutes of MVPA). Females also spent a greater proportion of time in sedentary behavior compared to males. While the absolute values were not significantly different, the high proportion of time spent being sedentary is concerning and requires further attention, including focused interventions designed to decrease sedentary activities. In general, males were more physically active than females when data were viewed separately for weekdays and weekends.

Similar to previous studies of youth (, ) and college-aged students (), there were more minutes of MVPA on weekdays compared to weekends. However, when expressed relative to the total time the students wore the monitors, this difference was no longer significant ( and ). This inconsistency was likely due to the two fewer hours per day the accelerometers were worn on weekends compared to weekdays.

Alternatively, this may, in part, be a real reduction in MVPA on weekends due to a less structured day (no school). AHS students' compliance with wearing the accelerometers was slightly better than that of middle school students participating in the Eating and Activity Survey Trial (Project EAST) (). Just over 95% of the AHS students provided 4-7 days with at least 10 hours of accelerometer data compared to 86% of students in Project EAST. The percentage of students obtaining all seven days of data was more similar (45% versus 50%, respectively).

While Project EAST students received a $5.00 movie pass for returning their accelerometer, the Team COOL students received $5.00 on a gift card for each day the accelerometer was worn for at least 10 hours and an additional $10.00 when they returned the device and took the additional survey. It is likely that the larger incentives used with the AHS students contributed to the greater proportion of students providing at least four usable days of data. Importantly, the incentive structure directly encouraged the wearing of the accelerometer, not just its return. Additionally, this high compliance rate was obtained without the use of daily reminder phone calls or logs, two commonly used strategies to improve compliance with wearing accelerometers. This study observed less wear time for older students compared to younger students, suggesting greater incentives may be needed to achieve the same level of compliance with older high school students.

In contrast, no age difference in compliance was observed in Project EAST, possibly due to the younger age and more limited age range of students (). Similar to Project EAST, the current study found no difference in compliance by gender or race/ethnicity. In addition, we observed no difference in the average hours per day the accelerometer was worn between those that received and those that did not receive free or reduced school meals. However, we did observe that students receiving free or reduced school meals accumulated approximately one additional day of data compared to those not receiving assisted school meals.

No differences in compliance were observed for those receiving and not receiving public assistance. These results provide some limited evidence that the incentive structure used in this study may have been more attractive to those students that were relatively more economically disadvantaged. Lastly, students that worked for pay obtained a greater number of days of data and average number of hours per day compared to those that did not work. Those that were currently working may have recognized this research as an opportunity to supplement their typical income, more so than those that were not working for pay. Also, the additional work hours may indicate less leisure time and, possibly, fewer opportunities to remove the accelerometer, or their working status may reflect a greater level of responsibility that may have carried over to wearing the accelerometer. Higher compliance among those currently employed is an intriguing finding since one could hypothesize that those who were not working for pay could actually have a stronger motivation to participate and acquire incentives.

Lastly, the relatively low economic status of the AHS students may have made the incentive (possible $45.00 total) an attractive income-generating option while this level of incentive may not have the same effect in a traditional public school population. To our knowledge, this is the first study to objectively measure physical activity in students attending alternative high schools.

The study sample of urban youth was racially/ethnically and economically diverse and inclusive of males and females. Our data also indicate that collecting accelerometry data in AHS students is feasible and productive. Almost all students provided four or more days of quality, usable data. The generalizability of these data is limited by the small sample size drawn from four urban alternative high schools within one Midwestern metro region. Further study will be needed to identify differences in physical activity by urbanization level and geographic region. The loss of ActiGraph data from the suburban schools is a limitation.

However, Nelson et al. Indicate that simple urban/suburban/rural classifications may mask important differences in the associations between the environment and physical activity (). For example, adolescents living in older suburban and inner city neighborhoods were similarly active and more likely to be active than those in new suburban or rural neighborhoods. Therefore, a more complex understanding of the physical environment appears important but beyond the scope of this study. Conclusions In conclusion, the AHS students in the present study were relatively inactive compared to studies assessing activity among students attending traditional middle and high schools; this was especially true for females.

AHS students represent a segment of the adolescent population that is rarely studied but more vulnerable to engaging in risky health behaviours, which include low levels of physical activity and high inactivity. The low levels of physical activity put these AHS students at greater risk for overweight and early onset of type II diabetes or heart disease that can carry forward to adulthood. The AHS setting provides an often overlooked venue to access at risk youth and intervene to support and promote healthy lifestyle practices that include more physical activity and less sedentary behavior.

Formative data from AHS students and staff suggest interest in school-based programming that supports students to be more active (, ) and data from the current study demonstrate a clear need. Authors' contributions: JRS generated the accelerometer measurement protocol and data reduction procedures, collected data, analyzed and interpreted the data and lead the writing and revising of the manuscript.

MYK conceptualized the study, collected data, and assisted with data interpretation, writing and critical revisions of the manuscript. JAF assisted with the conceptualization of the study, collected data, assisted with data interpretation, writing and critical revisions of the manuscript. CA collected data and critically edited the manuscript. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript.

The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Psychological correlates of academic performance have always been of high relevance to psychological research. The relation between psychometric intelligence and academic performance is one of the most consistent and well-established findings in psychology. It is hypothesized that intelligence puts a limit on what an individual can learn or achieve. Moreover, a growing body of literature indicates a relationship between personality traits and academic performance. This relationship helps us to better understand how an individual will learn or achieve their goals. The aim of this study is to further investigate the relationship between psychological correlates of academic performance by exploring the potentially moderating role of prior education.

The participants in this study differed in the type of high school they attended. They went either to gymnasium, a general education type of high school that prepares students specifically for university studies, or to vocational school, which prepares students both for the labour market and for further studies. In this study, we used archival data of psychological testing during career guidance in the final year of high school, and information about the university graduation of those who received guidance. The psychological measures included intelligence, personality and general knowledge. The results show that gymnasium students had greater chances of performing well at university, and that this relationship exceeds the contribution of intelligence and personality traits to university graduation. Moreover, psychological measures did not interact with type of high school, which indicates that students from different school types do not profit from certain individual characteristics. Introduction Since the early days of psychology, prediction of academic performance (AP) has been of high relevance to psychologists [, ].

This is due to the importance of AP in the life of every individual–it confines the range of possible job opportunities, as well as career choices. Higher educational level is often a prerequisite for more demanding jobs, which can also lead to greater financial outcomes []. Moreover, a measure of academic success can also play an important role in the job application process, as a source of information about the candidate’s prior performance []. Overall, it could be stated that the educational level achieved plays a role in the quality of life [] and well-being [] of an individual. Literature on psychological correlates of AP in post-secondary education indicates that there is a well-established relationship between intellectual abilities and AP [,, ], that individual differences in personality can explain additional variance in AP [,, ], and that general knowledge (GK) might be a valuable predictor of AP as well [, ]. Besides psychological individual differences, high-school success has traditionally been used in research to predict AP [–], mostly operationalized as grade-point average (GPA) or as a result in standardized tests (such as SAT in the USA). High-school GPAs has also been widely used as university admission criteria.

However, if a certain education system does not have standardized tests at the end of high school, GPA scores obtained by students from different high schools are hardly comparable. In Croatia this type of school system was present prior to the year 2010, when the national state exam was introduced. Until 2010, all high-school students that were enrolled in 4-year programmes were eligible university candidates. We can make a broad distinction between two types of 4-year high-school programmes: gymnasium, which offers a general education aimed to prepare students for higher education; and vocational schools, aimed to prepare students for the labour market. However, successful leavers of both types of high school are allowed to apply for admission to universities. Therefore, in this paper, besides psychological predictors (intelligence, personality, GK), we explored the moderating role of high-school type in predicting AP.

Intelligence Intelligence is a general mental ability that reflects a broad capability for comprehending our surroundings, solving problems, planning, or learning from experience, while it does not reflect a set of narrow academic skills []. AP has been the validity criterion for psychometric measures of intellectual ability [], since their main objective in the early days was prediction of academic success or failure []. Ever since, measures of intellectual abilities have become some of the most frequently-used psychological instruments, often used in employee selection [], career guidance [] and clinical practice []. Robust findings show that intellectual abilities are positive predictors of success in a variety of scholastic tasks, in level of education, and in work performance [,, –]. Association between intelligence and AP differs slightly with respect to different measurement methods, and varies across the level of education in different studies. Correlation between intellectual abilities and AP declines with age, being highest at primary school (.60–.70), and lowest at graduate level (.30–.40) [, ].

This decline is usually explained by the restriction of range in the university population, since fewer students continue education after high school []. Moreover, intellectual abilities are associated with continuing to higher levels of education []. Studies show that intelligence has high longitudinal stability in the period from childhood to early adulthood [, ], which makes it suitable for long-term predictions of AP at university. For example, a longitudinal study showed that psychometric intelligence measured at age 11 makes a large contribution to scores in national examinations in 25 academic subjects at age 16 []. Verbal and numeric aptitudes measured in 7 th grade were moderate-to-high predictors of total grade and grades in four academic subjects in 10 th grade []. Moreover, intellectual ability measured in the first year of university was in low correlation with academic success at the end of the first and third years of study []. Personality traits Even though some studies showed that intelligence explained more variance in academic achievement than did personality factors [], a role for personality traits should not be excluded from the prediction of AP.

It has been shown that both intelligence and personality can be related to successful learning []. In addition, different conceptualizations of intelligence and personality may also explain different aspects of academic performance. Whereas intelligence represents a set of specific abilities and puts a limit on what an individual can do, personality traits might indicate how an individual will do it [].

Traditionally, academic performance has been considered to be more closely related to intellectual abilities than personality traits []. However, abundant literature indicates that personality traits contribute to AP as well [, –]. In this introduction, we further present relations between AP and Eysenck’s Gigantic Three personality factors: Psychoticism, Extraversion and Neuroticism (used in this study). Neuroticism has generally been shown to negatively predict AP [, ] and this effect decreases with academic level []. It is argued that stress, impulsiveness and anxiety may influence AP (e.g. During the taking of an exam) in the same way they negatively relate to psychometric intelligence score [], since both AP and IQ are measured with maximum-performance tests. It could also be possible that Neuroticism influences AP in other ways.

Neurotic students are more often ill during examinations, which might lower their AP []. In addition, Neuroticism might direct a student’s attention towards anxious emotions, and away from academic homework []. Correlations between Extraversion and AP are ambiguous across studies.

It is expected that extraverts have higher levels of energy and generally more positive attitudes towards studying, which could be reflected in a desire on their part to acquire knowledge []. However, extraverts benefit from this trait only at lower levels of education, where there is more interaction with teachers, and where visibility in class is appreciated. It is more likely that extraverts will favour socialization over studying, which might lower their AP at higher levels of education []. In line with this, it has been shown that introverts outperform extraverts in secondary and tertiary education []. However, findings about the relationship between Extraversion and AP are inconsistent. For example, Petrides, Chamorro-Premuzic, Frederickson and Furnham [] reported negative correlation among a high-school sample, and Chamorro-Premuzic and Furnham [] reported positive correlation among a university sample, while Heaven, Mak, Barry and Ciarrochi [] reported no significant correlation among a high-school sample.

Correlation size and direction vary relative to method of AP estimation [], and relative to age and level of education []. Psychoticism, the last of the Gigantic Three personality traits, has been systematically negatively related to AP in previous studies [,, –]. Psychoticism is also negatively related to some other behaviours significant to academic excellence. For example, individuals high on Psychoticism found nothing wrong with school truancy [], and had lower levels of responsibility and interest in studies [], and lower involvement in coursework []. General knowledge GK represents one’s ability to acquire knowledge in general []. It is not a clear measure of an individual’s cognitive abilities, and it is not explicitly related to a formal education. The theoretical background of this construct may be inconclusive, since some researchers consider GK a first-order factor of crystallized intelligence [], while others consider it a first-order factor of semantic memory [].

GK is positively correlated with general intelligence [], and it could indicate how an individual uses mental abilities. Someone who is intellectually bright will never acquire broad knowledge if not devoted to learning, and if not in an environment that values education.

On the other hand, an individual with moderate intellectual abilities, but with high motivation for learning, and in a supportive environment, will acquire more knowledge. Furnham and Monsen [] showed that a measure of general cognitive abilities based on general-knowledge questions was in low-to-moderate relation with academic grades. Furthermore, Furnham, Monsen and Ahmetoglu [] showed that a GK measure was in weak positive relation with English and Maths grades.

Since it has been shown that GK relates to both intellectual abilities and personality traits [], it can be assumed that it may serve as a predictor of AP as well. Academic performance AP has been measured in numerous studies, but researchers do not agree about its definition []. Furnham and Chamorro-Premuzic [] proposed that this was due to familiarity with the concept. The simplest definition would be that AP is the success of individuals in formal education (elementary, secondary or tertiary education). As with inconsistency in the definition of AP, researchers have conceptualized it differently in different studies: for example, as GPA [], first-year examination scores [], final-year examination scores [], course performance [] or standardized PISA testing scores []. However, researchers often investigate the relation of aptitudes or personality-trait scores to students’ scores in different tests, but rarely have information on students’ broader academic achievement, such as university graduation, which is a cumulative result of all the academic tasks that students have to fulfil prior to earning a degree. Type of high school In most of the European Union countries, students may choose between general and vocational programs after finishing their primary education.

In some countries, continuing to higher educational levels is limited after vocational-school graduation (e.g. In Germany vocational-school students have to take additional courses prior to higher-education admissions) [], and in some countries they are allowed to enrol in tertiary educational programs. In Croatia, there are two types of high school–gymnasiums and vocational school–and students are selected for these schools based on their primary-school grades. The gymnasium programme offers a general education that qualifies students for university studies. Gymnasium graduates are not qualified for any profession, and it is assumed that they will continue their education at tertiary educational level. Vocational high schools offer programmes of 3 years (craftsmanship and industrial professions) and 4 years (medical, economic, agricultural professions etc.) that qualify graduates for specific professions.

Four-year vocational programmes offer a mix of broad basic knowledge, as well as profession-specific knowledge, and graduates may apply for tertiary education studies (while 3-year programme graduates may not apply). About 60% of 4-year Croatian vocational school students continue to university education []. According to the Student Integration Model [, ] a key factor in successful university studies is the student’s integration within academic and social systems at the university.

Individual characteristics that contribute to successful integration include the individual’s goal and commitment to achieving that goal, individual attributes regarding the importance of graduation, pre-college experience (usually GPA and academic and social attainments) and socioeconomic factors such as family background. However, if some country does not have standardized final high-school exams, the GPAs obtained can hardly be compared across different high schools. From that perspective, differences in high-school programmes (such as differences in gymnasium vs.

Vocational programs) can lead to a student’s integration with higher educational systems being easier or more difficult. Overview of present study The aim of the present study was to further investigate predictors of AP in tertiary education using longitudinal research design. AP was operationalized as a binary variable indicating whether the participant graduated at university level or not. While psychological correlates of AP are a widely-researched topic, we wanted to examine whether type of high school moderates the relationship of individual differences in intelligence, GK and personality to AP. Since the Croatian education system allows students from both gymnasium and vocational school to enter universities, the present study investigates whether individual differences or prior education contribute more to success at university. Intelligence and personality traits are constructs that represent qualitatively distinct individual differences, and it could be expected that there will be small or insignificant correlations between them [, ].

However, some studies indicate that a small negative correlation between intelligence and Neuroticism [][] might be found, due to mediational effects of test anxiety. Moderate correlation is expected to be found between intelligence and GK [].

Furthermore, it can be expected that intelligence [, ] and GK [, ] will be positive predictors of AP, while Psychoticism, Extraversion and Neuroticism [] will be negative predictors of AP. From the perspective of the Student Integration Model, we can expect that gymnasium leavers would perform better at university than those from vocational schools, merely due to the differences in primary goals between the two high-school programmes. A better scholastic background may lead to higher chances of better performance at higher educational levels.

Method This is an archival study, in which we have cross-referenced two archives of the Zadar Regional Office of the Croatian Employment Service (CES). Usage of the archive data was authorized by the Assistant Director of the CES.

A psychologist, working as a career guidance counsellor, was in charge of data collection. No personal information about any participant was ever released outside the premises of the CES Zadar archives. The first registry used consists of the results of psychological assessment of high-school students. The CES offers a service of career guidance to all high-school students who are university candidates (leaving gymnasium or 4-year vocational school) and who seek advice in career choice. Students can voluntarily schedule a counselling session during their final high-school semester. A psychologist gives career guidance, which consists of psychological assessment, semi-structured interview and counselling session.

Typically, the psychological assessment consists of measures of intellectual abilities, general knowledge and personality. Four measures of intellectual abilities are used–the Problem Test (serving as a measure of reasoning ability) and three measures from the Multifactor Test Battery (serving as narrower measures of numeric, spatial and verbal abilities)–and a measure of general knowledge, while personality is assessed by Eysenck's Gigantic Three personality dimensions. Primarily, results of psychological assessment are used for counselling purposes; but they are also kept for a long-term psychometric evaluation of the counselling process. Information about AP was retrieved from the CES job-seeker database, the second registry used in this study. When an individual registers as a job seeker at CES, a counsellor collects their information about formal and informal education and work experience. If a participant who enrolled for career guidance during the years 2000–2005 could be found in the job-seeker database, information on their university graduation was collected (either that they graduated, or that they did not graduate). If the information presented in the job-seeker database was inconsistent, or if there was no information for a certain participant, their test results were discarded.

The dataset is available in the. Any personal information has been removed from the dataset, and only raw scores are presented.

Participants The participants were final-year high-school students from Zadar County, Croatia, who enrolled in career guidance counselling at the CES, Zadar, between the years 2000 and 2005. During that period, a total of 1389 students enrolled in counselling. If the participants’ data could be matched with the job-seeker registry, the data was recorded, and 826 participants (average age of 18.07 ( SD =.70)) were included in analyses.

A total of 239 (28.9%) were men, and 578 (71.1%) were women. Of these, 538 (65.1%) had enrolled in a gymnasium programme, while 291 (34.9%) had enrolled in a 4-year vocational school programme. Problem test The Problem Test [] is a maximum-performance test that measures reasoning ability through problem-solving tasks. It consists of 70 tasks (mostly verbal, and some numeric), and the participant is supposed to identify the problem that lies underneath the task, and report a solution for a given problem. Cronbach alpha internal reliability has been reported to lie between.85 and.95 in various studies [].

Problem identification and solving is considered a valid cognitive-ability measure []. Multifactor test battery (MFTB) The MFTB is a Croatian adaptation [] of the General Aptitude Test Battery (GATB) [], a test widely used for purposes of professional orientation and selection. In the current study, three sub-tests were used, and all of them were maximum-performance tests. MFTB 2 is a numeric test, and the participant’s task is to perform simple maths operations as quickly as possible.

It consists of 50 items. MFTB 3 is a spatial-representation test made up of 40 items, which are pictures of flat, two-dimensional objects with foldable edges.

The participant’s task is to mentally ‘fold’ the planes of each object, and to select the correct three-dimensional object from among four choices. MFTB 4 is a vocabulary test. It consists of 60 items, and each of them has one pair of distractors and one pair of either synonyms or antonyms. The participant’s task is to mark the two words that are either synonyms or antonyms.

Split-half reliability coefficients of the tests were:.92 (MFTB 2),.88 (MFTB 3) and.92 (MFTB 4). Results Descriptive statistics and bivariate correlations among intelligence measures (PT and three subtests of MFTB), personality measures (Eysenck's PEN personality model), GK, AP and type of high school are presented in. Prior to data analyses, assumptions for inferential statistical tests were examined. Indices of asymmetry (AI) and kurtosis (KI) indicated normal distribution of continuous variables (all AI. Descriptive statistics and intercorrelations for measures of intelligence, GK and personality.

Weak-to-moderate positive correlations were found among all measures of intelligence and GK, which is in line with the results of previous studies []. Correlations between intelligence measures and personality traits are less systematic, with most of them being non-significant. Significant correlations are negative and weak. On the other hand, all measures of intelligence and GK were in weak positive correlation with AP (coded as: 0—did not graduate; 1—graduated), indicating better test scores among students who managed to graduate.

In contrast, Psychoticism and Extraversion were in weak negative correlation with AP, indicating higher values among students who did not graduate. Furthermore, measures of intelligence and GK were also related to type of high school (coded as 0—vocational high school; 1—gymnasium). Results showed moderate positive correlations between high-school type and PT and MFTB 4, while small positive correlations were found with MFTB 2 and MFTB 3. A moderate positive correlation between high school and GK was also found. All correlations indicate higher test scores for gymnasium students. In order to examine the relationships the relationships between high-school type, intelligence, personality and GK, on one hand, and AP, on the other, binary logistic regression was conducted. Four models were tested: in Model 1, the sole contribution of high-school type was examined; in Model 2, intelligence measures and GK were added as predictors; in Model 3, personality traits were added; and lastly we added the interaction terms between the type of high school and psychometric measures of intelligence, personality and GK in Model 4.

Model fit was assessed by using two pseudo-R 2 measures–Cox and Snell R 2, and Nagelkerke R 2 –and increase in fit of successive models was tested with the likelihood-ratio test []. Prior to the analysis, the presence of multicollinearity issues among predictors was tested for.

Variance inflation factors indicated no multicollinearity (all VIF. Results of binary logistic regression with high-school type, intelligence, personality and general knowledge as predictors, and academic performance as criterion. In Model 1, the contribution of the type of high school in predicting the AP was examined. It was found that high-school type was a significant predictor (χ 2 = 79.59, DF = 1, p.05). In the further discussion, we will interpret the results of Model 3. Discussion In this study, we examined the longitudinal contributions of intelligence, personality, GK and high-school type in predicting AP, operationalized as university graduation.

The results presented in indicate that students from gymnasium high school (compared to those from vocational schools) have greater scores on all measures of intellectual abilities and in GK, while students from the different schools did not differ in personality traits. Moreover, students who managed to graduate (compared to those who did not) had greater scores on all intellectual-ability measures, and in GK. In addition, they had lower levels of Psychoticism and Extraversion.

However, when all predictors of AP were introduced in the regression model (, Model 3) it was shown that only high-school type, numeric ability, Psychoticism and Extraversion were related to AP. Moreover, high-school type was shown to be the best predictor of graduation. Interpretation of exponentiated unstandardized b coefficients shows that gymnasium students have 172% greater chances of university graduation, that an increase of one point in the numeric-ability test leads to 6% higher chances of university graduation, and that an increase of one point on the Psychoticism and Extraversion scales leads to a lower probability of graduation by 11% and 7%, respectively. Interaction terms between high-school type and all psychometric measures (Model 4, ) were insignificant. It seems that intelligence is a weak predictor of AP, and that gymnasium students do not profit from being ‘more intelligent’. It also seems that what students learn in high school contributes more to their success at tertiary educational level than their individual differences in intelligence, GK and personality.

Previous studies have also shown that intelligence is not a good predictor of AP at post-secondary educational level [, ]. In line with previous studies, introduction of intelligence measures led to a small increase (0.03) in pseudo-R 2 indices.

For example, Kappe and van der Flier [] reported that intelligence accounted for 5% of the variance in students’ GPAs, while Farsides and Woodfield [] reported 4% of final grade being explained by intelligence. The relation between intelligence and scholastic achievement is expected to be lower at university level than at lower levels of education [, ], because of the restricted range of the population at university level []. Furthermore, it is interesting that GK was not a significant predictor of AP. Lack of this association might be due to the construction of TOIM, the GK measure used in this study. As already described in the Method section, TOIM is constructed to reflect knowledge different from that acquired in school. The lack of association between GK and AP in this study could mean that knowledge acquired in a general academic high-school programme, as offered by gymnasiums, could be far more relevant for performance at university. Download Buku Komputer Sd. Moreover, the introduction of personality traits increased the predictive and incremental validity of AP prediction, which was in line with previous findings [,,,, ].

Psychoticism was found to be negatively related to AP. This is among the most consistent findings regarding the Gigantic Three personality traits and AP [,, ].

Individuals high on Psychoticism often show behaviour such as poor cooperation with the group, have weak organizational skills, and exhibit low achievement motivation. It is also argued that Psychoticism can serve as a proxy measure for low conscientiousness [], a Big Five trait that usually accounts for a substantial amount of variance in AP [, ]. Extraversion was also shown to be negatively related to AP, which is in line with previous findings [, ]. While pupils may benefit from a higher Extraversion level, it seems that this trait can obstruct performance in academic tasks. Extraverts might tend to spend more time socializing than studying, and could be more easily distracted from studying, thus having lower probability of university graduation. The results did not reveal a significant relation between Neuroticism and AP in this study.

In a number of studies, a negative relationship between Neuroticism and AP has been demonstrated [, ]. One possible explanation for the absence of this relation in the present study might be related to the method of measuring AP. AP is often measured through maximum-performance tasks (e.g.

Test scores). In that context, the negative emotionality that comes with high Neuroticism (e.g.

Test anxiety) may negatively influence performance. However, university graduation is not a maximum-performance measure, and the effects of Neuroticism may be reduced when scholastic tasks are spread over several years. Neurotic students with high levels of motivation may demonstrate several behaviours for overcoming difficulties caused by anxiety: they can learn more, make an efficient plan for attending exams, or take more than one exam for the same course and improve their performance. Researchers have usually used a narrower operationalization of AP. University graduation is a result of several (usually four or five) years of studying, during which students must accomplish various academic tasks and pass numerous exams.

Many factors (both internal and external) in that process may influence one’s graduation and lower the correlation between intelligence and graduation. It should be noted that the Big Five personality dimensions might better predict differences in AP than Eysenck’s Gigantic Three model, but they could not be used in this study, since that data was not available in the present records. For a review of research on Big Five personality traits and AP, see [,,, ]. Two of those traits that could be particularly important in the prediction of AP are conscientiousness (representing students who are more motivated to perform well and more persistent when faced with difficulties []) and openness (representing students that are more imaginative, and might better manage new learning []). Therefore, it might be relevant for future study to explore the relationships of the Big Five traits to AP regarding students’ high-school background.

Besides intelligence and personality measures, the results of this study revealed that high-school type is a significant predictor of AP. Unsurprisingly, the results revealed that students from gymnasium high school, compared to vocational school, have a greater chance of graduating at university. It is worth noting that gymnasium students did not benefit from having greater intelligence levels than vocational-school students. (All interactions between high-school type and intelligence were non-significant.) This may imply that the high-school programme has a bigger impact on post-secondary education than do students’ abilities. In that respect, gymnasium schools fulfil their purpose of preparing students for university studies.

As previously mentioned, intelligence might put a limit on what an individual can achieve, while personality might indicate how an individual will achieve it. Here, we put emphasis on the context in which an individual is making his achievement.

These findings are in line with Tinto’s Student Integration Model [, ]. Gymnasium leavers might be better prepared for university, since their high-school programme is designed for that purpose. That might lead to better adjustment to the new study system, and greater goal commitment, leading to more persistence and, finally, greater achievement. On the other hand, vocational school programmes are divided between general education and skills that are required in the labour market. While vocational-school students can start work immediately after leaving, it seems that they might have difficulties in adapting to university studies. Although they are eligible for university admission, it seems that the current education system fails to prepare them sufficiently for post-secondary education.

The results presented have two practical implications. First, the type of prior education can be a valuable source of information in future research on AP correlates. It could be a simple measure of prior educational context in school systems similar to that in Croatia.

Moreover, future studies should focus on more detailed explanation as to why gymnasium students perform better at university level. Other contextual factors that could be considered include parental support (Do more aspirant parents send their children to better schools and provide better support?), school social groupings (Do high-scoring students at the end of elementary school regress to different means in different high schools?) and university context (Do graduates from different high schools choose different universities with different standards for awarding degrees?). The second implication relates to equal possibilities for the higher education of those leaving different types of high school. The European Union is striving to increase inclusion in higher-education programmes [].

Although they are eligible university candidates, it seems that students from vocational schools in Croatia perform more poorly at university level. Therefore, they might benefit from some sort of additional institutional help prior to higher education. For instance, some countries (e.g.

The Czech Republic, France and the Netherlands) offer additional counselling for high-school students. On the other hand, some countries offer additional preparation programmes (e.g. The Czech Republic, Luxembourg and Iceland) prior to university entry []. Limitations of the present study There are several limitations of this study that should be addressed.

First, the generalizability of the data presented is limited due to the sample used in this study. The participants in this study were final-year high-school students who decided to take career guidance counselling, and who, later in their lives, registered as job seekers with the CES.

It should be noted that the entire sample showed a considerably high level of achievement motivation, which manifested itself in seeking career advice, and in actively searching for a job. However, although the sample size is reasonably large, the sample is not representative of the population of students. There are final-year high-school students that did not engage in career guidance counselling (for it was not mandatory), and there are students that did engage in career guidance counselling, but later in life did not register as job seekers (which was not mandatory, either).

Not engaging in career guidance can imply both high and low scholastic motivation. For example, an excellent student who achieves high grades and knows exactly which university to attend does not need career guidance. On the other hand, a student with low grades, and without any motivation to continue studies at university level, might find career guidance unattractive.

Not registering as a job seeker with the CES can also imply both a successful career and lack of motivation to look for a job. Despite some limitations regarding the unrepresentative sample, the relationships between intelligence, personality and AP presented here are in line with the literature in this field, but these findings should be confirmed on a more representative sample. The final regression model shows small pseudo-R 2 measures. It should be taken into account that this might be due to the operationalization of AP.

A continuous measure of university graduation (e.g. GPA) might serve as a better dependent variable. Moreover, on the basis of the available data, it was not possible to account for differences among universities and among different departments. It could be assumed that different departments have different standards that students must meet prior to graduation. This type of distinction at university level might provide better distinction of AP.