Crowdfunding

Disparate impact, also known as adverse impact, focuses on the effect of employment practices, rather than on the motive or intent underlying them. Accordingly, the emphasis here is on the need for direct evidence that as a result of protected characteristic, people are being adversely affected by practice (Heneman 2015). Disparate impact statistics describe existing analytical patterns, but they do not automatically describe the reasons these patterns occur. The main reasons for the patterns may, or may not, be caused by discrimination. Statistical evidence must be presented to support a claim of adverse impact throughout my paper I will discuss the three statistical types, limitations, and disparate impact.

(3) Justice: “Attention ought to be paid to the equitable distribution within human society of the benefits and burdens of research involving human subjects”. This is basically to act or treat people equitably and fairly.

The article “Payday Loans”, written by Scott Gilmore express his opinion on payday loan interest rates. His beliefs are that the interest rate is extremely high for people in society. After considering all Gilmore’s points placed in his opinionated article, I can grasp that I share the same opinion as him. The interest rate is too high for people and there should be a cap put in place by the government. With regulations put in by the government, it is believed to benefit society in which is considered still high. The government believes that an annual percentage rate of 540 is reasonable for everyday working people. In fact the newly in placed interest rate is double the amount charged from loan sharks.

An analysis was performed to see if the effect of a National Supported Work (NSW) Demonstration study was effective in increasing the earnings of a group of individuals. The analysis showed that, on average, those treated earned roughly $800 more than their counterparts three years later. Because of the randomization method used when selecting the participants, we can be confident that this estimate is causal. This randomization removes all biases from omitted variables as well as any selection bias, because the groups were systematically alike as shown in the analysis. Another analysis was performed using data from both the NSW study as well as the Panel Study of Income Dynamics (PSID). This analysis showed that using non-experimental data creates biased results because the two groups are not comparable. Furthermore, even the use of regressions in an attempt to correct this bias will not be successful and will still not result in causal results.

The Propensity Score Matching (PSM) technique will be used to generate a matched comparison group for analysis that is designed to eliminate the effects of self-selection bias. The levels of recidivism outcome measure for those who completed prison-based education programs and those who did not participate in the programs before and after the application of the PSM method will be compared. This will enable us to determine whether research that does not control for self-selection bias produces inflated estimates of the treatment effect. The effect of prison-based education will be investigated by using a fixed-effects logistic regression to measure the likelihood of re-arrest when a prisoner completes the education program. Lastly, the length of time to re-arrest will be taken into consideration and we will employ a proportional hazards regression to compare hazard rates between those who completed prison-based education programs and their matched comparison group. Conditional logistic regression models will be used. Male subjects will be chosen based on eligibility factors that include completion of the prison-based education program and impending release. Recidivism rates will be monitored 2 years after release, as research indicates this is when rates tend to be highest. The independent variable will be a prison-base

As you can see, it might not be obvious that these seemingly similar measures of absolute fairness would give rise to such different methods to

However, the researcher may fail to reject the null hypothesis that is potentially false. In this case, we are putting into question the inferential power of the test, specifically, the measures ability to accurately identify the effect. Essentially, police officers participating in the CAN program were expected to make at least 2 unannounced contacts with his or her youth on parole per month ( p.117). However, the program was only partially implemented as more than half (52.8%) of the participants received 0-1 visits during the entire 7 months of the study. As such, we can presume that the treatment was implemented inconsistently from person to person which would reduce the researcher’s capacity to detect any relationship between the program and criminal offences. As such, the control group and the experimental group may have received the same amount of contact with a regular probation officer or police

Different matching methods all seeking to match as many treatment households with control households as possible have been advanced. Several matching methods are tested and overall measures of imbalance in the covariates - both before matching and after matching are checked.

he was obviously looking for fame when he created the video. But did not think of the backlash that it could of created.

Which of the individuals made the best use of their credit card? Which made the worst? Why?

Variables that at the same time influence both the participation in MGNREGA and the outcome which in turn, are unaffected by treatment have been included in the analysis. Propensity score matching analysis provides better estimates when one can retain maximum number of covariates which will affect the treatment, but treatment should not affect the selected

The propensity score matching model is a method used to evaluate the average effect of a programme on participants’ outcome, conditional on the pre-participation characteristics of such participants (Bryson, Dorsett, & Purdon, 2002). The PSM technique has been applied widely in a variety of fields in the program evaluation (Heinrich et al., 2010).The model is appropriate for addressing the problem of selection bias (Wooldridge, 2002) in determining the difference between the participant’s outcome with (in this case adoption of weather index insurance) and without (non-adoption of the weather index insurance) programme (Pufahl & Weiss, 2008). Pufahl & Weiss also note that participants and non-participants

Another assumption in this approach is the overlap assumption that each firm have a positive probability of having a dissention. There is no difference in the chance of dissention between a firm in the treatment group and a firm in the control group. Statistically, it can be tested in a statistical software. Please see the Stata results using teffects ipw and teffects overlap. Thank you very much for the points of missing important assumptions and justifications of my matching method. Although it is commonly used in finance area, I need to understand and consider

The marginal mean weighting through stratification (MMWS) is a more recent approach developed to handle multivalued treatment analysis. It works by combining elements of both propensity score stratification and IPTW (Linden, 2014; Hong, 2012; Linden et al., 2010). It works by first stratifying the analytic sample into quantiles of the generalized propensity score, and secondly, generating a weight for each individual based on their corresponding stratum and treatment assignment (Linden, 2015; 2014). The stratification process is meant to reduce bias in the observed covariates used to create the propensity score (Rosenbaum & Rubin, 1984), whilst the weighting processes standardizes each treatment group to the target population (Linden, 2014). In the multivalued treatment situation (Linden, 2014; 2015), the MMWS approach is initiated as follows: first, the GPS is estimated either by an ordered or multinomial response models. Next, each GPS is then stratified into equal sized quantile categories. It is worth noting that stratification is based on the estimated probability of the base

Crowdfunding creates funds for new projects by using internet and social media. This can benefit small business projects to obtain their required funds. A project receives small investments from wide range of individuals through web advertising and social media. The individuals (investors) who have invested in the project may receive incentives such as discounts on the products, early opportunity to purchase their products, inclusion of their name in the list of contributing founders etc., so, they are not purchasing the share of the company. Crowdfunding avoids going to the banks, friends and family to get funds. It also avoids giving up partial ownership of their company. The websites like www.rockethub.com, www.peerfunding.com,