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Causal study is the type of quantitative research. In this kind of study, researcher finds the cause and effect among two variables. Researcher constructs a hypothetical condition which called hypothesis between the variables so that cause and effect will be determined.
Work performance and stress is one of the best examples of the causal studies. Moreover, the researcher mostly collects the data through observation or experimental methods in the causal studies. In the observational method, researchers can closely observe the activities of respondents for the purpose to collect the data.
For instance, use of mystery shoppers is one of the best examples of data collection through observations. On the other hand, researcher can design an experiment for the respondents in order to do data collection. The other kind of data collection method is sampling. It is also used as the method of data collection through which researcher gathers the responses from the participants about a particular issue or problem.
Sampling data collection method is used to collect responses from the large population. Sampling is the most preferable data collection methods for the qualitative studies. In sampling, it is convenience to collect large amount of data in a few time. Furthermore, sampling data collection method has two kinds that include probability sampling and non-probability sampling. Probability sampling data collection method is also known as the random sampling.
In this kind of data sampling technique, researcher is free to choose the elements from the sample on a random basis. It means that all the elements of the sample have an equal chance to select by the researcher. On the other side, non-probability sampling is also the data collection method. In this type of sampling, researchers have the authority to choose or select only those elements that are convenience to select.
In the field of statistics, it is necessary for the researches to first identify the type of data before collecting the data.
The reason is that if the data collection tool is not equivalent with the data type, then it will be interpreted in a wrongful manner. Statistics has different kinds of data categories which should be identified in order to do correct data analysis. Statistics is the field of study or an academic discipline which mainly comprises on data. The fundamental function of statistics is to gather data for the purpose to draw conclusions or solve complex issues with the help of logical reasoning.
It is better to categorize the data first before starting the data collection. In addition, categorization of data also helps the researcher in order to best data collection method. Particularly, there are seven main fundamental kinds of data which is used in the field of statistics. The seven types of data are ordinal, categorical, binary, real valued multiplicative, real valued additive, count and binomial. Each data type has its specific characteristics that should be incorporate with the data collection methods.
The description of all data types are given below:. Ordinal is the data type in the field of statistics. In this type of data, the data represents in the form of real numbers as well as integers till the level of arbitrary. Ordinal scale is the level of measurement which is used to measure ordinal data.
For instance, the salary of a person is considered as the example of ordinal data. Categorical is also the type of data in statistics. Categorical data type is quite similar to the binary data type that is because the permissible statistics as well as the measurement scale of both these data types are same. The categorical data can be written in the form of number that starts from 1 and ends at infinity value. Its values are based on random choices or the judgment of an individual.
The example of categorical data type is the selection of a political party by an individual in order to become a member. The third type of data called binary. Binary data type represents in only two digits that include 0 and 1. Computers only understand the language of 0 and 1. The numbers 0 and 1 function as yes or no. In addition, this data type is only used where the possible outcomes are only two.
In statistics, nominal scale is used as the measurement scale binary data. In addition, the appropriate statistics for binary data type are Chi-squared as well as Mode. Another data type is real valued multiplicative in the discipline of statistics. The values of real valued multiplicative represent in the real numbers. In addition, the real numbers should be in positive. For instance, the prices of the goods as well as the income of an individual come under the real valued multiplicative.
The measurement scale for this data type is ratio scale. All the data of interval scale is also used for the real value multiplicative data type. However, the favorable statistics of this data type are coefficient of variation, harmonic mean as well as geometric mean. Real valued additive is the data type which is used in the field of statistics. It is used to represent the distance of a place as well as the temperature of anything.
It is necessary to use real number in order to represent this data type. In addition, interval scale is the preferable scale of measurement for real valued additive. However, the permissible statistics of binomial data type is also used for this data type. Binomial data type is also used in statistics in order to present the data. It is one of the kinds of data type which is used to highlight the ratio of something that includes satisfaction, votes and many others.
The value of binomial data type represents in numbers which starts from 0 and it goes till the infinity. The statistics which are used in the binomial data type are correlation, standard deviation, median, mode and mean. Another data type is count which is one of the types of data categorization.
The values of this data type represents in the positive numbers. Count data type is used to characterize the number of females, males, casualties, births and many others. Ratio scale is used as the measurement scale for count data type. Harmonic mean, geometric mean as well as coefficient of variation are the appropriate statistics for the count data type. In addition, the statistics of the interval scale is also applied on this data type.
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View Homework Help - Homework 3- Order Statistics from PSTAT B at University of California, Santa Barbara. (a) Derive the pdf of U = X + Y. (b) Suppose Z has the pdf f (z) = 1 √ 2 π e-z 2 2.
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