Plagiarism destroys your career life. Long time ago, students cannot seem to know if the paper they submitted contained plagiarized content. All they knew was that they were already facing the crime of plagiarism. Sadly, they do not have access to
free plagiarism checkerlike Plagiarisma. Today, scholars are able to correct and edit similar idioms after scanning their documents with anti-plagiarism software. Teachers also will find the tool lessening the burden of returning research works and filing cases against students for plagiarism. Introducing the originality checker considered as an important tool for schools, colleges, academies, institutions and universities. It works on Windows, Android, BlackBerry, Moodle and Web. It detects copyright infringement in your essays, research paper, coursework or dissertation. It supports Google, Yahoo, Bing, Scholar and Books. It helps avoid
plagerism. It will only take a few minutes to check your document. Are you searching for
plagarismdetector that is accurate enough to help you track duplicate content? Make things a bit easy on you. Use
plagiarism checkerto track all your written content. Save yourself from embarrassment of being accused from accidental plagiarism. Although the paid features of Plagiarisma are better and allow you to customized, using the free subscription for purely
duplicate contentscanning is good enough for you. You should be aware that the result of the scan is not a matter of testing your honesty. It is a practical program designed to help you track down similar sentences. Get a free report with percentage now!
Free online plagiarism checker for teachers and students. Copyscape and Turnitin alternative.
Data were collected from a representative sample of households in the 15 Local Government Areas (LGA) targeted by the VISION Project. These include 5 LGAs in each of 3 states: Bauchi State located in the North-East, which is comprised mainly of Gerawa, Ningawa, Hausa, Fulani, and the Tangale ethnic groups; Eungu State located in the East, which is comprised mainly of the Igbo ethnic group; and Oyo State located in the South-Western part of Nigeria, which is comprised mainly of the Yoruba ethnic group. The selected LGAs in each of the 3 states represent VISION Project areas. LGAs were considered for selection into the VISION Project if: 1) the population was over 100,000, 2) the population contained at least 20,000 women of reproductive age, 3) the LGA contained private and public health facilities at the primary and secondary levels, 4) the LGA has access to media resources such as radio, television and newspapers, and 5) the LGA has support from Nigeria's federal Ministry of Health (MOH) and the relevant state-level Ministry of Health (MOH). VISION partners visited the LGAs and made the selections .
This paper analyses data from the 2002 and 2004 household survey waves of the VISION Project evaluation [10,13]. The surveys were implemented by the Center for Research, Evaluation, and Resource Development (CRERD). The Tulane University Institutional Review Board approved this study. A two-stage cluster design was used to obtain a probability sample of respondents for this study. Sample size calculations were based on National contraception prevalence rates of 16% for females and 27% for males, and a design effect of 2. The target sample size was 1,100 respondents per state (after adding 10% for potential non-response and incomplete questionnaires). This sample size will enable us to detect a change of 10 percentage points in a variety of key indicators in the VISION Project areas in each state with 90% power and a probability of committing a type-I error set at 5% (two-sided test). In each state's VISION project area, 40 enumeration areas (EA) were randomly selected with probability of selection proportional to the population size (PPS) of the selected LGA. The State Office of the National Population Commission (NPC) provided a list of EAs in the project LGAs of each state. Within each selected EA, a household enumeration exercise was completed; between 27 and 28 households were then selected using systematic random sampling. All adults aged 15–49 years old in the household were listed, and one eligible person per household was selected using a table of random numbers. Interviewers obtained verbal informed consent from the selected participants. A total of 3,279 respondents across all three states completed the questionnaire (≅ 1% refusal/non-response rate) in 2004.
Data collection was conducted by trained interviewers. All fieldwork supervisors first participated in a 5-day centralized training. Subsequent to this, supervisors and interviewers jointly participated in 5-day regional trainings. The household questionnaire was field tested prior to the onset of the survey to check for errors, and to evaluate the readiness of the interviewers.
The survey questionnaire was based on the Demographic and Health Survey questionnaire. In addition to standard demographic and fertility questions, questions related to family planning, sexual activity and behavior, and exposure to various media campaigns were also asked. Specifically, respondents were asked if they had listened to the following programs on the radio: Kusaurara, Dunniya J'atau, A New Dawn (Ayedotun), One thing At A Time, Gari Muna Fati, Abule Olokemerin, or Odenjinjin. Respondents were also asked if they had seen the Femi Kuti or Fati Mohammed television campaigns, if they had seen any HIV/AIDS or reproductive health advertisements in the newspaper, or had received any information from clinics or community health workers about HIV/AIDS or reproductive health.
The outcome measures for this analysis are as follows: 1) Have you ever talked with a partner about ways to prevent getting the virus that causes AIDS (yes/no)? 2) Can people reduce their chances of getting the AIDS virus by using a condom every time they have sex (yes/no)? 3) Did you use a condom during your last sexual encounter (yes/no)?
The respondent's age (continuous variable), education (categorized as none, primary, or secondary), religion (categorized as catholic, protestant or other Christian denomination, Muslim, or traditionalist), gender, marital status, place of residence (dichotomized as urban and peri-urban, or rural), knowledge of a condom source (yes/no), state of residence (categorized as Bauchi, Enugu, or Oyo), and whether or not the respondent has had at least 1 partner in the past 12 months, served as control variables in all regression models (described below). Based on the distribution of the data during preliminary analyses, exposure to media was measured by dichotomizing whether a respondent reads the newspaper at least once a week (yes/no), watches television at least once a week (yes/no), or listens to the radio at least once a week (yes/no). Media exposure variables served as control variables in the first-stage Poisson regression models only (see below).
Our indicators of program exposure included a total count of the number of FP/RH radio programs heard, television programs seen, or printed FP/RH advertisements seen over the past 6 months (0 – 10). A separate count of the number of radio programs exposed to (0 – 7), and the number of TV programs exposed to (0 – 2), were also used as indicators of program exposure in separate models. All values, whether part of the cumulative number of programs exposed to or the individual programs exposed to for the respective media types, were then categorized as low (none), medium (one), or high (2 or more). The decision to use the same categories for each program exposure model was based on the distribution of program exposure data for each media type. These analyses showed that the proportion of respondents exposed to each category (i.e. low, medium, high) of program exposure was similar between total program exposure counts, radio program exposure counts only, and TV program exposure counts only. No model was run for the printed advertisement media, as the questionnaire only asked about exposure to any printed advertisement (yes/no).
Data analyses were done using STATA™ 7.0. Comparisons of the outcome variables were made between the baseline 2002 data and the follow-up 2004 data. Chi-square statistics and two-stage logistic regression models were used to analyze the data . Using data from the 2004 follow-on survey, the Durbin-Wu-Hausman test (augmented regression test) was performed to test for endogeneity between media exposure and the respective outcomes . Since the results showed evidence of endogeneity (i.e. value of one independent variable is dependent on the value of other predictor variables), two-stage logistic regressions were performed using instrumental variables of program exposure. At the first stage, Poisson regression was used to estimate the total number of exposures to VISION programs using a set of exogenous variables. Because estimated exposure is a continuous variable, respondents were recoded has having low estimated exposure if the estimated exposure was less than 1, medium if estimated exposure was between 1 and 2, and high if estimated exposure was two or more, and used as indicators of program effectiveness in all logistic regressions. Wald statistics and log-likelihood ratios were used to identify variable significance and model fit, with alpha set at 0.05. To control for the effect of clustering within enumeration areas (EA), the Huber-White-Sandwich estimator of variance was used to obtain empirically estimated standard errors, with the EA defined as the cluster. Standardized state-level probability weights, based on the relative population size of program LGAs in each State (pweight) were applied to regressions, and to all between state estimates.