We've reached the end of our trial of Little Acorns, click here to read our Ease of use: When you sign up, you receive a page PDF which. tion of the likely profit growth of a lay-betting system called. Little Acorns? Little Acorns arrives as a PDF manual of twenty-four pages. The system is based on the. PDF | On Jun 1, , Stefan Dyck and others published Great oaks from little acorns grow: Tracing the scientific evolution of experience.
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The role of self-assessment in educational transitions: If you break even on the day but still have losses outstanding from previous days then you move up the Fibonacci staking sequence by one unit for all selections. C is a vector representing individual controls like gender, year of birth, number of siblings, birth order, whether the respondent is Roma, plus type of settlement and county of residence. Because the focus of the analysis is not on the impact of these variables, tables containing the results do not summarise the estimated parameters for these variables. In that sense, self-assessment moderates the parental educational gap if the impact of self- assessment on the track choice difers among the ofspring of diferently educated parents. Below is a table to really show the power of compounding.
Even though there is ample research to demonstrate that self-perception — and more particularly academic self-concept — does contribute to educational outcomes, much less evidence has been gath- ered about its status proile. In particular, more information is needed about why parental background diferences might prevail in self-assessment.
Why status diferences in perceived ability might exist Breen argues that social diferences in subjective probability might exist because various status groups have diferent information about the educational system; moreover, in determining success at school, they estimate the role of efort and ability diferently.
Working-class pupils ascribe lower belief to the role of efort than do their peers in the middle class, and therefore they are more pessimistic about the prospects of success Breen , On the other hand, empirical research reinforces the idea that families in diferent social strata employ diferent parenting styles, which could inluence the transmission of personality traits and, through personality, educational outcomes Kaiser and Diewald Poorly educated mothers, on the other hand, have less interest in the schooling progress of their children.
Other research reinforces the notion that, unlike children in working-class and poor families, middle-class children are deliberately stimulated by their parents in order to foster their cognitive and social skills Lareau If parents of diferent social status employ diferent parenting styles and have diferent information about the role of ability and efort in education, it could be assumed that these diferences modify the way in which pupils in diferent families interpret their own abilities.
Since low-status pupils and their parents overstate the importance of ability and downgrade the role of efort in education Breen , lower self-assessment is hypothesised among pupils in lower strata.
A comparison of the estimates with the actual results showed that pupils from lower classes systematically underestimated their ability, compared to their more advantaged peers. Since only a month or so elapsed between the measurement of self-assessed and real performance, reverse causation working-class pupils making more rapid progress could be excluded.
The purpose of the analysis his paper broadens understanding of educational transitions by distinguishing actual ability school grade or test points from perceived ability. Even though these two concepts are naturally correlated, they could both contribute individually to future school track choice.
While the consequences of status-related diferences in ability are well known in educational decision-making, much less atten- tion has been devoted to the same diferences in perceived ability, especially in connection with later educational outcomes. Our knowledge in terms of status-related educational transitions will be expanded by providing empirical evidence on the following three questions: Material and methods 2.
Currently the survey has six completed waves with fairly large response rates 2nd wave in Pupils with special educational needs are also excluded, because they are oversampled in HLCS and have only reading test scores available. Keller 2. More detailed information is available in Bukodi et al.
At the beginning of the second semester, they draw up an order of preference for the secondary school they would like to attend Figure A2 in Appendix 1 shows how these events are connected to the timing of HLCS. With the choice of a particular secondary school pupils also opted for a particular track. As mentioned above, there are three tracks available at the secondary level: In Hungary, there are no general tuition fees for tertiary education: But the vast majority of students study free of charge.
Deinitions 2. Educational tracks at secondary level he secondary track was deined on the basis of the path that pupils are following at the beginning of year 9 three possible categories , using HLCS data and as reported by parents.
In the empirical analysis, two dummy variables will be employed. Fee-paying university places usually have lower requirements: Pupils could be enrolled in tertiary education in the year of their school-leaving certiicate, or one year ater.
Please also note, that the tracks both at secondary and tertiary level are those where pupils had been admitted, and not those which had been applied. It could be that pupils applied schools within a given track but they had been not admitted to see Section 2.
However, in recognition of the fact that parental education is not equivalent to social-class position, the diferences to be found according to parental background will be referred to as status-related not simply status diferences, indicating that the variation is not necessarily attributable to social status. Two categories are distinguished. Table 1 contains the mean, standard deviation and the number of observations N for the three dependent variables in the analysis.
Explaining these gaps more carefully is the aim of the empirical analysis below. Self-assessment Self-assessment is measured using the following question: Contrary to prior research Alicke and Govorun ; Williams and Gilovich , the above-described question gives pupils information about the class average performance. Keller Table 1. Mean, standard deviation and number of cases of the three dependent variables in analysis, by parental background.
Answering the self-assessment question pupils had to indicate a number ranging from 0 to It is likely, however, that giving pupils clear reference about the average performance explains this underestimation, since in prior studies Dunning, Meyerowitz, and Holzberg the average performance was not given.
In the analysis, however, the self-assessment measure is standardised with a 0 mean and one unit standard deviation. Note, that this question refers to performance in year 8, and so is a kind of retrospective question it was asked in the irst wave of HLCS, when students had already begun year 9. Moreover, it is worth mentioning that the wording of the question does not suggest the type of test.
One can only guess that the test mentioned in the question probably measures some cognitive ability rather than talent in sport, art, etc. A more detailed and domain-speciic self-concept measure used, for example, by Musu-Gillette et al. Obviously the question measures self-assessment with noise, but this is the only available proxy for that in the data-set. In an ideal situation, self-assessment should be measured before pupils transitioned from primary to secondary education.
Since the wording of the self-assessment refers to year 8 performance, if the question were not asked retrospectively, the causality assumption would hold — simply because of the temporal ordering between cause and efect. As things stand, however, reverse causality might be a factor: Note that pupils enter secondary education on the basis of the order of preference they indicate on their application form, and on the basis of their results in the admission test.
Usually pupils rank better schools higher on their order of preference. It should also be noted that usually more competitive schools prescribe an ability test, and schools without a good reputation cannot select pupils.
It is quite reasonable to assume that those who did not get into their irst-preference school down- graded their year 8 performance retrospectively. Moreover, those who were admitted to a competitive secondary school could have upgraded their self-assessment retrospectively.
Even introducing secondary-school ixed efects as is done would be a classiication based on the dependent variable and therefore would not capture the substantive choice process.
Nonetheless, it is hard to ind an instrumental variable which inluences only self-assessment and not track choice. However, in the case of the choice to go on to tertiary education, the retrospective nature of self-assessment probably will not bias the estimations. Test scores might be a more objective measure of ability, since it is assessed by means of a centralised test which is corrected by external examiners who do not know the pupils personally.
Note that even test points can be regarded as an outcome of the school system, and therefore they are not necessarily a perfect measure of ability. School marks are the GPA based on the mid-term report card in year 8, taking all school subjects into account. It is assumed that GPA is the major information source for pupils about their school achievement.
School marks are readily available. Pupils know what grade they have, and moreover pupils and sometimes also teachers discuss grades in their classrooms.
In order to have similar relative to classmates measures of self-assessment, the GPA measure appears as a classroom average and the individual deviation from that. Both variables individual and classroom-average GPA are standardised with 0 mean and one unit standard deviation.
Competence scores, on the other hand, are used in the analysis to control for unobserved ability which teachers cannot observe, but pupils might have — see Terrier It is completed by pupils in their usual classroom environment. In maths, pupils have to think about everyday life problems, to which they are required to apply their maths knowledge. In reading, they have to read between six and eight 1—2 page long texts, ater which they are asked questions on what they have read.
Since the aim of including competence measures alongside school grades in the analysis is to control for unobserved ability as well as possible, in order not to lose variance, both maths and reading comprehension test scores are introduced into the analysis rather than some form of composite measure.
Both maths and reading comprehension test scores are standardised to 0 mean and one unit standard deviation. Descriptive statistics about self-assessment, competence scores and school marks the major intendant variables are summarised in Table 2. Since every measure is standardised and have 0 mean and 1 standard deviation Panel C , one unit corresponds to one standard deviation. It means for example that the diference in self-assessment between the ofspring of low- and high-educated parents is 0.
Keller Table 2. Mean, standard deviation and number of cases of self-assessment and ability measures in year 8, by parental background. Similar diferences can be found in school marks and competence test scores. Psychological variables he way in which pupils rate their own ability could be inluenced by some psychological traits.
While internal control means that a person believes he or she has some control over life, external control is deined as when someone considers that environmental factors or fate inluence life outcomes.
Moreover, a depression scale was constructed from questions about anxiety and suicidal thoughts. Control variables he following control variables were used, though the estimated efects of the variables do not appear in the tables: Because the focus of the analysis is not on the impact of these variables, tables containing the results do not summarise the estimated parameters for these variables. School ixed efects If there is a sorting of students across schools, endogeneity might occur, especially since the heteroge- neity of schools is considered quite remarkable in Hungary Horn However, employing school ixed efects also means that only within-school variance is used, and the estimated parameters should be interpreted relative to the school average in year 9.
Descriptive he empirical analysis to be done later is driven by the assumption that, at the same level of GPA, those who perceive their abilities to be higher will be more successful in their educational transitions than their peers with the same grades but a lower level of self-assessment. Since NABC has full information about the classroom attended in year 8 grades and competence scores are recorded for each class member , and since the wording of the self-assessment question indicates the hypothetical classroom average, it is possible to anchor self-assessment to relative school achievement.
One possible way of doing this is to check whether grades in year 8 fall below or above the class- room average two categories and compare whether the corresponding self-assessment is lower or higher than the average indicated in the question i. Combining these two sets of two categories, a four-category classiication is produced, showing the match and mismatch between school grade and self-assessment. However, a corresponding classiication based on competence scores is also made, and shows the same results.
In general, for every outcome variable analysed in this paper, a higher mean is found among those who have below-average school achievement and above-average self-assessment than among those with below-average grades and below-average self-assessment.
Hence, self-assessment may well increase the probability of transition. But the reverse argument holds, too: Models 2. Parental background diferences in self-assessment When calculating the diferences in self-assessment according to parental background, raw diferences are shown irst. Equation 1 shows the estimated model, where SA stands for self-assessment, PB for parental back- ground, and A is a vector for ability containing school marks and competence scores.
P is the vector of psychological variables, containing psychological traits like internal control, social competence, self-esteem and inclination to depression.
C is a vector representing individual controls like gender, year of birth, number of siblings, birth order, whether the respondent is Roma, plus type of settlement and county of residence.
S stands for school ixed efects; separate models are itted using primary and secondary school unobserved school-level heterogeneity. General Tertiary Figure 1. Self-assessment and educational choices, and how self-assessment mediates parental education gap in track choices Mediational analyses try to identify the process whereby a particular independent variable leads to a speciic outcome for a dependent variable, decomposing the total efect between the independent and the dependent variable into direct and indirect efect, which is transmitted through a third independent variable Alwin and Hauser ; Hou In the case of transition from primary to secondary education, the dependent variable consists of those who were admitted to college-bound secondary tracks secondary general and secondary vocational track vs.
In a second set of models, the diference between secondary general and secondary vocational track is analysed. In the last set of models, the transition to state- inanced tertiary education provides the focus of attention. Here, the population contains those who completed secondary education secondary general or secondary vocational track within ive years of commencing it. Among the right-hand variables appear self-assessment SA , parental background PB , ability A , psychological controls P , school ixed efects S and other individual-level control variables C.
Unobserved school heterogeneity deals with the unknown selection of pupils into schools, which could also inluence self-reported ability self-assessment.
Only secondary-school unobserved school heterogeneity is controlled for, since unobserved school heterogeneity might inluence the way in which pupils answered the self-assessment question. In the case of entry to tertiary education, an additional variable appears in the regression, indicating the year in which pupils graduated from secondary school U.
In that sense, self-assessment moderates the parental educational gap if the impact of self- assessment on the track choice difers among the ofspring of diferently educated parents. Statistically, the moderation efect is assessed by the interaction between parental educational level and self-assess- ment. Equation 4 therefore difers from Equation 2 in that it contains the interaction term as well. Results 3. What kind of factors mediate parental education diferences in self-assessment Table 3 shows the results of estimations explaining self-assessment.
Note that the numbers in the table are expressed relative to the standard deviation of self-assessment which is ixed in one unit. Another explanation for this status-related gap in self-assessment is that pupils with various parental backgrounds probably attend quite diferent classrooms.
Including classroom-level characteristics - like average school marks - see Column 3 , the parental education diference in self-assessment decreases further the diference in the efect of parental education between the two parameters in Columns 2 and 3 is signiicant: Parental background diferences in self-assessment might also be connected to unobserved school-level heterogeneity: However, the drop in the efect of parental education between the two parameters in Columns 3 and 4 is not signiicant: Psychological traits as well as other control variables also mediate the initial parental educa- tion gap in self-assessment.
How track choices are inluenced by self-assessment, and how self-assessment mediates parental education diferences Table 4 summarises the results for the role of self-assessment in educational transitions. In the three panels of the table, three diferent dependent variables appear: Self-assessment has a signiicant positive efect in every model presented. Its impact ranges from 0.
Making predictions from the irst model column 1 in Panel A this means that if we have two pupils with low-educated parents the one with average self-assessment has At the same time, if we have two pupils with average self-assessment the one who is the ofspring of high-educated parents have Altogether the impact of self- assessment on educational track-choices is small. In the given example, it is approximately one third of the efect of parental background. One should, however, also be aware that the meaning of one standard deviation increase is 20 points diference on the original 0— scale the not standardised self-assessment variable.
It equals with the change if somebody believes that his performance is above 80 points and not bellow 60 points the classroom average. If we compare the estimated parameters for parental schooling across the two models within each panel, it is possible to gain some clue about the importance of self-assessment in mediating this gap.
If pupils with low- and well-qualiied parents assessed themselves as equally performing, the gap in their educational transitions would decrease by an additional 2.
So the ofspring of parents with a diferent educational level do not follow diferent school tracks because of their self-assessment. It should be highlighted that individual-level school marks have a higher impact on the choice of whether to embark on further education than does the probably more objective ability measure — the competence scores. All in all, both maths and reading comprehension test scores play an independent role in the explanations of the outcome variables.
Keller Table 4. How self-assessment moderates the parental education gap in educational transitions Table 5 contains the estimations for the interaction between parental background and self-assessment. Column 1 shows the choice between college-bound secondary tracks and the vocational track. Since it is oten diicult to imagine the interaction efect solely on the basis of estimated coei- cients Brambor, Clark, and Golder , Figure 2 helps to visualise the predicted probabilities of being admitted to a college-bound secondary rather than to the vocational track.
In the case of the ofspring of those with high-educated parents, self-assessment seems to make no diference. Note that these tracks instead of the vocational track ofer the school-leaving certiicate that serves as an admission ticket to tertiary education.
Self-assessment has less of a role to play in moderating the parental education gap in more qualitative educational choices at the secondary level choosing a secondary general instead of a secondary vocational track.
At the tertiary level as Column 3 of Table 5 indicates , however, self-assessment does not have a signiicant moderating efect on the parental education gap, most likely since this stage of transition is a conse- quence of prior educational transitions. Summary and discussion hroughout the research it was assumed self-assessment might reinforce the investment in efort. Azmat and Iriberri At the same time, even small investments little acorns could make a signiicant change mighty oaks among pupils with low-educated parents.
Note that if pupils aim to reach the educational level of their parents, this means the ofspring of low-educated couples tend to choose secondary tracks that do not lead directly to tertiary education. Consequently, depending on self-assessment, the gap decreases in college-bound secondary track choice between the ofspring of low-and high-educated parents.
Limitations here are some limitations to the results, which should invite careful reading, above all because of the retrospective character of self-assessment. However, a promising feature of the results is that self-assessment inluences the choice of tertiary education, which is clearly unafected by the retrospective nature of self-assessment.
It should also be mentioned that ability and school achievement could both be products of self- assessment; therefore an early-childhood measure of self-assessment would be more appropriate. Since self-assessment is measured on a scale of 0—, and the hypothetical class average is ixed Conclusion he choice of secondary education at age 14 is probably the irst educational decision where pupils have a say.
As has been shown elsewhere, inducing pupils to think that they are highly able is more of an ego-centred than a task-centred exercise, and is therefore perceived to be a less efective way of developing ability Nicholls , Our knowledge of how efort is inluenced by parental background remains very limited.
Even though there is a growing body of literature pointing out the importance of personality traits though personality traits are not necessarily equivalent to efort in educational outcomes Borghans et al.
Making an investment in education is costly, since investment now is only rewarded in the uncertain future. As with any investment, there is always the possibility of failure.
Hence, having something which modiies the willingness of pupils to make the efort is of signiicant importance. If somebody believes that he is able or more able than his peers , that belief might help him to consider the efort to be worthwhile. Because of the conidence in own ability, people with high self-assessment might worry less about fruitless invest- ment and might endure current eforts in order to earn later rewards.
Notes 1. Pupils also have the opportunity to enter secondary general school ater year 4 or year 6. Usually talented high-status pupils choose the early track. Results are consistent using multinomial logit; results are available from the author on request.
All remaining errors are solely mine. Disclosure statement No potential conlict of interest was reported by the author. His major research interests include education, social stratiication and the role of attitudes and values.
Keller References Alicke, Mark D. Alicke, David A. Dunning, and Joachim Krueger, 85— New York: Psychology Press. Alwin, Duane F. Azmat, Ghazala, and Nagore Iriberri.
However, this staking sequence is designed to protect profits already made. We are then in a great position to take advantage when we hit the inevitable series of winning lays, streaks as I call them. Remember, unlike many systems that adopt the use progressive staking, Little Acorns has the long term stats on its side that will bring about future profits. More importantly, incorporating the above staking sequence ensures a very efficient way of pulling back any losses that may occur en-route.
So lets look at the amount of starting bank required depending on your chosen lay stake. As a guide, we have also given your expected winnings per month which reflect that starting stake and betting bank.
These figures are based on a monthly profits ratio of between 7 — 35 points which the formula has historically shown. This will then give you confidence to move up the staking ladder. Remember the title of the book? The betting bank we recommend above can take over 2. Also, in time you will have built up sufficient profits for any losing sequence to be easily absorbed. When I first started operating the formula I did experience a losing sequence of 9 only once in an 18 month period, but even then I did not come close to losing my starting bank.
Let me explain; The average Lay price for Little Acorns is 1. Put simply, you are not losing your full unit Lay stake on most of your bets. On the other hand, if we were to lay at the maximum price allowed for Little Acorns of 2.
However, as stated above, the average liability payout for each winning selection is 1. So the betting bank could take well over 2. More than enough to protect our bank! There are simply too many complex variables in horse racing to form any consistency for the backer of horses at these odds. To prove this, have a go at this little experiment. We have no doubts the conclusion of your experiment will validate why Little Acorns works, and works well!
Remember, we have the maths on our side. If you do feel uncomfortable when you reach 6 or 7 losing bets in the sequence you are staking too high! Using both of the above progressive staking plans will bring about profit. In fact, using progressive staking alongside any statistically sound formula is a powerful weapon for making profits.
There is a lot myths and scare stories that surround progressive staking, yet if used wisely, its one of the single most powerful staking tools that can be used to make good gains, especially when you are laying equal too, or below your actual unit stake. Ask any professional, they will tell you making money from level stake betting is hard. Therefore, I feel compelled to include as an option. Quite simply when you experience a series of losers, instead of going back to the beginning of the staking sequence like with the Fibonacci method, with this you just go back 2 steps.
Here it is below; Extended Fibonacci Sequence: It will certainly beat any bank rates to savers and with compounding can turn into worthwhile sums in as little as 3 years. Little Acorns can quite effortlessly obtain well over 2 points every month on average no matter what staking plan you choose therefore is ideal for compounding. Below is a table to really show the power of compounding. You may suffer a very slight loss in some months that can delay you reaching this target by month Working Out Your New Unit Stake Monthly To work out your new unit stake on a monthly basis, simply divide your new betting bank total at the end of each month by your initial starting bank in units.
Although amounts are small to start with I know of several Little Acorns followers who are compounding their way up to some very amounts from small starting pots. The message is clear, even when starting with small stakes which most of us do, Little Acorns can help you build up a very nice lump sum profit in the medium to long term.
Even take out half and carry on compounding with the other half — The choice is yours. Then over a couple of months see how you fair and evaluate which one suits you best in terms of risk versus reward.
This way you will always have some profits trickling in on monthly basis as using both staking methods will deliver steady profits almost every month.
So no matter what staking plan you choose, the excitement you get as your profits and stakes build in a safe and secure manner will be immense. Also, make sure you keep notes and log all bets as you go along. Operating down this route will give you a higher win strike rate and more qualifying selections as more liquidity flows into the market. However if you are unable to do this due to work commitments etc, then you can take one of the following 3 routes.
Either operate the method on one qualifying selection a day and this can be the first one that qualifies on price at your time of viewing in the morning. Then on your return home check the result and adjust your stake up or down depending on the result for your next qualifying bet the next day or whenever you choose to use it again.
You can simply use it at the weekend, evenings, or on your days off. If you choose to use Staking plan A , you can place your bets before work by following the guidelines below. Then consult your Betfair account when you get home to see if you have made a profit or loss overall. If you break even on any day and have NO losses to claw back from the previous days then you stay on the same stake for the next day.
If you break even on the day but still have losses outstanding from previous days then you move up the Fibonacci staking sequence by one unit for all selections. If you win on any day from your chosen selections and have recouped most, or all of your previous losses, then you go back to the start of the staking sequence. The way we win with this method is when we get a few days of consecutive winning lays, maybe if we get 2 or even 3 winning lays each day for a few days in succession.
Log onto www. Note down all the races on the Win market where the favourite has current Lay odds of between 1. For example; 1. You can include 2. Also, make sure the Second Favourite has lay odds of 6. Also, if there are just 3 runners in total for the race then its still valid so long as all 3 meet with the price criteria above.
Optional 5. For improved strike rate use on All Weather Racing Only! You will soon find out how profitable this formula is without the worry of high liabilities, but as they say, the proof is in the pudding. We have provided the pudding, and YOU will see the proof!
See it as a slow burner and re-invest profits. In time when your bank doubles, you can double your Lay stake. Now you may be wondering how many selections on average you will get daily. Well, this is a strange one.
The amount can vary greatly, some days you will have several, then you may have a few blank days. Selection wise Little Acorns can be a bit like a dripping tap at times, then all of a sudden you will get a gush of qualifiers and the profit will trigger upwards. Therefore, we do urge patience and the formula will not let you down. It all depends on the races on offer on any given day and this is something we have no control over. It can also depend on when you decide to analyse potential selections.
If you are looking in the morning many races may not qualify then, but may well qualify later. Mainly due to speculators requesting silly lay prices to see if they get matched, this would then put the race outside the system selection boundaries.
The secret here is in the staking plan to give us our edge, and sticking to the rules. Little Acorns takes advantage of this uncertainty and turns it to your advantage to drive you into long term profit - Enjoy! You will also receive a nice reward after 3 months depending on the amount you bet! Go to; www. Also on the Internet; www.
Here is a fun bet I use every Saturday that has given me some sizeable wins, and some nail biting moments in my quest for the big payout! This bet is offered as an option by most online bookmakers once you enter 4 selections from whatever sport. Or you can simply place your bets at the betting office on a pre-printed slip. What we are doing here is predicting the home side will be leading at half time but will end up drawing at Full Time. So here goes; I scan the football coupon of bookies or Betfair on any given day I wish to use the method and choose 4 HOME sides that are favourites priced around the 1.
As stated above you usually get odds of Also, watch out for Bookmaker bonuses that give double the odds for getting one correct. Some bookies have already cut the odds for this bet due to many bettors drawing out some nice wins so shop around! You can do more than one Lucky 15 if you get more matches qualifying, but remember you need at least 4 matches for the Lucky 15 bet. Please do let me know if you hit the big one as its nice to hear the stories! Also, a nice testimonial received in October says it all; Subject: Football Bankbuilder To: I've attached a screenshot of my Lucky 15 ticket with B Congratulations and many thanks!
I will buy you a beer if it comes up.