Työn ja perheen suhteen sosiaalinen rakentuminen eri ikäisten puheessa

Tämän tutkielman tavoite on selvittää, miten eri ikäiset ihmiset puhuvat työ- ja perhe-elämän yhteensovittamisesta. Tarkoituksena on tuoda esille haastattelujen puheessa vakiintuneita diskursseja, jotka luovat sosiaalista todellisuutta. Aineistona käytetään kandeksaa eräässä Etelä-Karjalan Osuuspankin konttorissa tehtyä teemahaastattelua. Näitä haastatteluja analysoidaan diskurssianalyyttisesti. Haastateltujen puheessa toistuu kandeksan eri diskurssityyppiä: ihannediskurssi, "normaali" -diskurssi, organisaatio joustajana -diskurssi, omatuntodiskurssi, stressidiskurssi, minä päätän -diskurssi, ylitehokkuusdiskurssi sekä ikä ja asenteet -diskurssi.

Monte Carlo Hypothesis Testing with The Sharpe Ratio

The purpose of this master thesis was to perform simulations that involve use of random number while testing hypotheses especially on two samples populations being compared weather by their means, variances or Sharpe ratios. Specifically, we simulated some well known distributions by Matlab and check out the accuracy of an hypothesis testing. Furthermore, we went deeper and check what could happen once the bootstrapping method as described by Effrons is applied on the simulated data. In addition to that, one well known RobustSharpe hypothesis testing stated in the paper of Ledoit and Wolf was applied to measure the statistical significance performance between two investment founds basing on testing weather there is a statistically significant difference between their Sharpe Ratios or not. We collected many literatures about our topic and perform by Matlab many simulated random numbers as possible to put out our purpose; As results we come out with a good understanding that testing are not always accurate; for instance while testing weather two normal distributed random vectors come from the same normal distribution. The Jacque-Berra test for normality showed that for the normal random vector r1 and r2, only 94,7% and 95,7% respectively are coming from normal distribution in contrast 5,3% and 4,3% failed to shown the truth already known; but when we introduce the bootstrapping methods by Effrons while estimating pvalues where the hypothesis decision is based, the accuracy of the test was 100% successful. From the above results the reports showed that bootstrapping methods while testing or estimating some statistics should always considered because at most cases the outcome are accurate and errors are minimized in the computation. Also the RobustSharpe test which is known to use one of the bootstrapping methods, studentised one, were applied first on different simulated data including distribution of many kind and different shape secondly, on real data, Hedge and Mutual funds. The test performed quite well to agree with the existence of statistical significance difference between their Sharpe ratios as described in the paper of Ledoit andWolf.