If you are looking for leisure or social science research plan, which is to use the main methods of data analysis:
? Chi-square test. This test is denoted by the symbol X2, is used to examine the relationship between two nominal variables, variables that are something like gender or age must be described to show. This test will show whether the relationship is significant or not, and in this case the null hypothesis of no difference are rejected. The test is performed by examiningCounts or percentages in the table cells and comparing the actual census count with the expected what would happen if there were no differences with respect to the null hypothesis, as if there were an equal number of people from two different ethnic groups in a study Participation in two different leisure activities. One would expect that the same number of members of different ethnic groups in any business, if there is no difference, but if a company becoming more popular with a group andthe other activity is so popular with the other group, then there is a difference. The chi-square test is expected the addition of the differences between the counts and the counts or percentages or percentage so that the larger the value, the larger the chi-square value would be. In other words, this value by adding the values ??of the squared differences.
? T-test. This test will identify two ways to determine if the differences are significant compared tobased on the rejection of the null hypothesis of no difference and accept the alternative hypothesis that there is a difference. For example, the test at the average income of people who participate in various recreational activities like golf to the bowling alley to see, see if there is a difference between them, which might be expected, since golf is a sport is quite expensive, during a sport bowling is relatively inexpensive. The test can be used as a test or paired samplesindependent samples. In tests, paired samples, the means of two variables, such as two different activities compared to everyone in the whole sample, as the time spent on the Internet and the time watching television. In contrast, in independent test samples, the means of two subgroups of the sample can be seen in terms of a single variable, whether there are differences between them, than spend the time, young people and their parents are comparedInternet.
? One-way analysis of variance or ANOVA l'analisi. This test is used to go more than two vehicles in a single test, how to compare participation opportunities for men and women in a number of activities such as dining out, television time on the Internet, shopping, active participation in sports, or go to sporting events. The test checks whether the mean difference for each variable in the test from the general average, what is the alternativeHypothesis, or is the same as the overall average, the null hypothesis. The test takes into account not only the differences between the average of the general population and for various subgroups, but differences occur between the means, the "variance". This variant is determined by the sum of the individual differences between the means and the overall mean results that are obtained interpreted in this way. The higher the variance between the groups,likely that there was a significant difference between the groups, while the larger variance within the groups, the less likely that there is a significant difference between the groups. The F-score is the analysis of these two different measures of variance, the relationship between the two species show of variance ? the variance between groups and the variance within the groups. In addition, one must consider the number of groups and sample size, the degreeFreedom for each test. The result of this calculation results in a score F and F, the lower the score, the more likely it is that it represents a significant difference between groups.
? The factor analysis of variance. This is another ANOVA test, based on an analysis of means more than one variable, such as examining the relationship between activity and is involved in the sex and age of participants is based. In fact, this is testCrossing the help of various groups to determine whether they are significant by comparing the averages of the two groups and the degree of spread between the groups. So, in this test, the degrees of freedom considered in addition to the sum of the squares are taken to a square and then make an average F. Again, the lower the score, the greater the likelihood of a significant difference between the middle group.
? Correlation coefficient (usually "r" by means).This coefficient varies from 0 when there is no correlation to +1 if the correlation between two variables is perfect, or -1 if the positive correlation between the variables into good and bad. The numbers between 0 and +1 or -1 indicate the degree of positive or negative correlation between the variables. The size of r is examined by calculating the mean for each variable and how each data point the average x-and y-axis in a positive or negative identifiedConnection. Then multiply the two differences, and takes into account the sample size to determine how significant r at a given significance level (usually 95% or 5% level).
? Linear regression. This approach is used when there is a sufficient degree of correlation between two variables, so that researchers can predict one variable knowing the other. (Veal, p. 358). For this purpose, a researcher, a model of this relationship by developing aEquation, which says that this relationship is. This equation is generally referred to as y = a + bx., In the "a" is a constant and "b" refers to the slope of the line that best shows the extent or the correlation between two variables measured.
? Non-linear regression. This is a situation where two variables that are not connected in a linear fashion, so that a single straight line can not be used to bring their relationship to the expression occurs. This non-linear regression occur if weRelationship is curved, as if there was a gradual growth of interest in activities that are pursued by a burst of enthusiasm, and then a plateau of interest. Another example would be a bimodal distribution, or duty cycle, as if there is public interest in an activity twice a year, or up and down the growth of interest, such as whether there was a spike in interest following the introduction of a new program several times in year, followed by a decline in interest until a newProgram will be reinstated.
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