Challenges of quantitative - Research Paper Example

?INTRODUCTION There are two kinds of research styles which are qualitative and quantitative researches. The values and norms of these researches are completely different (Thompson, 68-70). The application of methods which are used for applying these researches requires development of specific understanding. There have been negating views which supplement the importance of both the researches. Quantitative research methods are used by the researchers when they wish to make statements of situations which might take place in a population. This research style is based on probabilistic measures which form the basis of the theory. In this form of study the researcher has access to the data set of the population. Based on this data of the population samples are taken for pursuing the research. The concepts of data collection which form essential components of quantitative researches include sampling error method, random sampling and sampling bias method of data collection (Thompson, 68-70). Quantitative method of research is used specifically when the researchers base their studies on chance (or probability). In this report a discussion will be presented on challenges of conducting quantitative research. Specific application of research strategy based on IT education and its application for conducting a quantitative research will also be discussed. A presentation of analysis of issues on sampling, validity, reliability and biasness of the techniques which are used while conducting the research will be given. Over a period of time researchers have discussed the relative importance and drawbacks of both qualitative and quantitative methods of conducting researches. Both the methods have their own relevant importance in varying situations. Quantitative researches, application and their relevant significance will be elaborated in further segments of this research. QUANTITATIVE RESEARCH The phenomena for collecting data and analyzing it using mathematical and statistical methods for determining results for specific research topic is called as quantitative research. The data for this research is collected using various different forms of surveys, questionnaires. These methods gather the opinion of the respondents and further methods lead to specific conclusions which are the basis of these researches. The analysis aids in measuring the ways in which a large population of people behave in various different situations (Bernard et al., 175-198). The quantitative data is formed on the basis of research techniques and gathering of quantitative data (Mahoney and Goertz, 227-249). The results of this data are measured as expressed in the form of percentages or either it is represented numerically, for example when the companies wish to calculate the overall brand awareness of the customers they use the quantitative style of research. The answer to this question which is a major purpose of this research will give numeric representation let’s say 15% of the respondents are familiar with the brand and its presence in the markets. The advantages of quantitative researches are that all the variable used which includes dependent and independent variables and the associated results of those variables can be analyzed independently. With the use of quantitative researches hypothesis can be tested very effectively (Smith, 6-13). The major drawback of using quantitative method of analysis is that huge sets of data are required for calculation. The collection of such huge sets of data requires a lot of work (Cohen, 155-159). CHALLENGES OF CONDUCTING QUANTITATIVE RESEARCH The challenges which the researchers most commonly have to face while conducting the researches are availability and lack of details, missing variables, relative sampling of large data and methodological limitation (Firestone, 16-21). 1. Availability And Lack Of Details Quantitative researches are criticized for lack of details as the researchers face difficulty in collecting the data. The quantitative research methods require finding public opinion with the use of questionnaires. In certain cases people don’t wish to answer certain questions. Some questions which are essential for research fall outside the ethical barriers and hence the people don’t answer to those questions. For overcoming this problem open ended and close ended questions are used for assessment of varying situations (Howe and Eisenhart, 2-9). 2. Missing Variables With the use of this method it is essential that all the variables are completely assessed. Connectivity o certain variables and the data which is used as an essential component of research may omit the relevance of certain variable completely. 3. Relative Sampling Of Large Data For certain assessment samples have to be taken from large data sets for effective calculation. Deciding that which sample must be selected for the research is a challenge for the researchers. E.g. Users of a particular brand need to be assessed for their preferences. If the brand coverage is huge hen only specific users can be analyzed and not all the users. 4. Methodological Limitation Biasness and the validity factor of the data which has been acquired for calculation of the data is the biggest challenge which the researchers face in their researches. The methodological objectives of the research lead to objective problems in the research (Osorio). All the challenges which have been discussed above elaborate the discrepancies which the researchers have to face while conducting quantitative researches. All the challenges arise during data collection and applying appropriate statistical tools for confirming the validity of the process. Overcoming these challenges and keeping in view the challenges which have to be faced while conducting the research are essential components for completing quantitative researches. QUANTITATIVE RESEARCH STRATEGY APPLIED ON INFORMATION TECHNOLOGY EDUCATION Understanding the instruments which are essential to be applied in the IT industry includes application and validation of the entire process. The researchers need to ensure validation of the instruments which they use for conducting the research (Kaplan and Duchon, 571-586). Over the last few decades the researchers have started emphasizing on measurement related issues and application of appropriate methods for conducting the research. Exploratory, qualitative and non empirical methodologies have been used for research. The researching trend is changing and the researchers are emphasizing on applying quantitative methods of study on IT segment (Dube and Pare, 597-636). The case provided for analysis includes teaching of varying number of courses each semester and results which the students have acquired in different semesters. The courses taught in this study were C++, Java DL, App DL, App, CS 2, C programming and 1401 DL. All these courses are taught to varying number of student each semester. Combination of the courses taught each semester varies. Assessment has been done starting from fall 2011 to summer 2013. The details of grades which the student acquired during this process are shown step by step. Number of total students and the grades which they acquired has been highlighted in the data which has been acquired. For this research the quantitative method of analysis will be used and survey will be provided assessing the success and failure rate of the student in different courses. The analysis will show that in which courses the students have succeeded most. In which courses more number of students has enrolled. Analysis will also pose on relevance of the data and application for acquiring appropriate results which omit the aspect of biasness. Regression analysis, supported by other statistical tests, will be used in order to explore the relationship between the success rate of the students in a particular course and the number of students enrolled in that particular course. The reliability will be ensured by using the Cronbach’s Alpha test. ISSUES ASSOCIATED WITH SAMPLING Quantitative research style is important because it deals with both the issues of determining an appropriate sample size and eliminates the biasness aspect of the research. Quantitative survey design is the ability of using smaller groups for assessment and analyzing the relative sample set. Sample is the data set or the population which is selected for the study. With respect to sample, sampling is selecting a specific portion of the research which will aim at representing the entire portion of the population. There are several techniques and strategies which are used for assessing the sample set collected for research (Bartlett, Kotrlik, and Higgins, 43-50). Sample size determines the appropriateness and adequateness of the data which influences the quality and accuracy of the research material collected. The survey researches are used for collecting the data and represent the specific reasons which are essential for collection of data. The prominent issues which occur during sampling include assessment of error and biasness (Landreneau). While conducting researches it is very important that researchers understand the fact that more accurate results can be acquired with the use of appropriate samples rather than assessing on the basis of entire population. When population data is used for specific researches rather than sampled data then the concluding results are erroneous and biased. Researchers have proposed various methods which determine selection of sample size and make relevant assumptions which contribute in making the data relevant. During sampling assessing determining the relevant sampling procedures is extremely important; for example if an assessment is being conducted about the children who are part of group daycare in U.S. the entire population of 10 million students which are part of preschool segment cannot be assessed. By using the appropriate sampling techniques it can be determined that which families and what number of sample data will be appropriate for completing the analysis. Besides defining the procedures and setting the sample size which is to be used for completing the data it is very important that sampling designs are specifically set for accomplishing the research objectives. Suitable sampling designs for the researches are probabilistic and non probabilistic sampling method. Researchers require that they determine the sampling technique which they intend to use for conducting research. In the data set provided of the IT industry the sample is not continuous. The courses taught in each semesters differ and the numbers of students which pass and fail the course also vary. Some courses are taught in one semester but are not taught in the other. This shows disproportion and lack of continuity of the data set provided. The basic methods which are used for determining the sample size include continuous and categorical data. Besides these methods the tools which are used for determining the sample size include conducting regression analysis, factor analysis, sampling non respondents and using budget, time and other constraints. These methods make determining the sampling size easier and can be applied on data provided for assessment. Application of these testing procedures is essential for determining the sample size which may be used for accomplishing the purpose of this research. ISSUES ASSOCIATED WITH VALIDITY Researchers make continuous claims for understanding the appropriate validity of the data which has been used for conducting the research. The appropriateness of the observation and measurement of the data set which define the validity of the procedures used in this research are significantly important for functioning of the concept (Adcock, 529-546). Advances in the research methods have yet signified little importance to the validity of the measured data. Qualitative researches require effective assessment and valid measurement of the data. While conducting the researches it is very important that distinction is created for appropriateness of the measured data and its relation to the concept. There are four major issues which the researchers face while validating the data which they have used for the research. The first issue which the researchers have to address when they are conducting the research includes the skepticism in the standard methods using which the qualitative and the quantitative researchers have used the data does not appear due to the methodological reconciliation of the data which has been used (Straub, 147-169). Several methods have been used for setting the standards which determine the techniques which are used for assessing the validity. The second issue arises when the data validity is assessed for measuring the disputes which validate the concept. The topics discussed for research aims at clarifying and refining the concept which are used in the study. The conflict which arises in the concept and in the measurement validity is essential components of this study. The third challenge arises when application of research is valid in one context while it is invalid in the other context. The response which is sought from the research is essential for incorporating sensitivity and at the same time establishes equivalence across diverse settings. The last challenge is the use of confusing languages. This contrasts the views and at the same time interrelates to different types of validity (Straub, Boudreau, and Gefen, 380-427). Researchers working in the domain of IT industry require that complex choices are made for linking the concepts to the observations. These connect the ideas to the facts and the choices made lead to the direct measurement of validity and choice. The measuring of the validity is completely linked to the concept of operational significance of the entire case presented. Relating the topic of research to the data which has been used is the validity of the data. The alternate perspective of validity which is used for the measurement is content validity, criterion validity and constructs validity of the research. These labels of the research are very important and they have been emphasis of various discussions which different researchers have proposed (Kim, 1178-1191). Example extracted from IT industry shows that on studying the relevance of validation, at one place convergent validation provide relevant evidence which show that construct validation and at another situation they are treated are distinct. Various concepts have been presented which aim at solving the issues which arise in the research for solving the issues of validity. Validity of the data fails in presenting a relation of the data. The synchronization and relativity of the data shows that the data is appropriately related with the study. Non continuity of the data and the changing number of students in the courses offered make it difficult for assessing the data. For completion of the research it will be very important for the researcher to form a link which validates the data with accomplishing the research objectives (Golafshani, 597-607). ISSUES ASSOCIATED WITH RELIABILITY During the research process it is essential that the research process must include appropriate reliability of the instrument. For this process it is calculated that absence of the same instruments are trailed. Reliability is considered as a statement which measures the overall accuracy of the instrument. In another definition it can also be stated that the overall estimation and the reliability of the data which is used by the researcher and appropriateness of the instruments which are used and contribute in providing error free results contribute to reliability (Boudreau, Gefen, and Straub, 1-16). This produces error free results of the research conducted. Five techniques are used by the researchers for analyzing the reliability. Internal consistency, split halves, test retests, inter-rater reliability test and alternate form test are used for measuring reliability. Recently these techniques have been used for supplementing reliability which is assessed through structural equation modeling process. Other method for measuring reliability is the one-dimensional reliability test. These methods play a very important contributing role for measuring reliability of the instruments. All these methods signify the importance of the processes which are used and this contribute in the techniques the application of which is more or less frequent. Reliability of a research is also tested in qualitative study using Cronbach’s Alpha (Lewis, Templeton, and Byrd, 388-400). The idea behind reliability testing is that whether the instrument is successfully generating same or different results in differing settings. Researchers need to assure that in appropriate settings it is necessary that operational significance of the entire method is tested. Assessment of reliability of tests must be done by appropriately analyzing the research questions, check the units of measures which are part of the research and replicate the reasons and selection of the methods. Selecting the interviewees which are essential for the research is also required to be tested. All these protocols of assessing the research study form the essential components of the researches. Testing the consistency of the results of the researches is extremely important. In the provided data set aspect of consistency and accurate representation are extremely important for conducting the research. Reliability of the research is treated as the methods which can be used if similar techniques of study are applied on entire data set. The data provided on the IT industry is reliable and shows appropriate grades acquired by the students in each semester. When all these components are applied on the research then the study can be assumed to be reliable. Replication of data, repetition of data in related studies can be related to reliability of data which is used. Stable measures taken for analyzing the data and forming a protocol of assessment which secures the reliability of data used is the purpose for applying this method of study. On analyzing the examples of quantitative researches are assessed it can be clearly concluded that validity takes place on successful completion of reliability. If the data used for the research is reliable then the purpose of research can be successfully accomplished. Researcher’s ability and skills which form the basis of qualitative research show that reliability of a document is a measure of validity. Both reliability and validity are interrelated measures which form the building block of authenticity of the research. The data of this research is appropriate as it is both reliable and valid. ISSUES ASSOCIATED WITH BIASNESS Selection bias is one of the topics which have gained significant importance in the relevant topic. The central focus shows the significant importance of selection methods which deliberately aid in analyzing the data which is intended to be used for the study. Selection bias is a terminology used for specifying the non random selection of cases which results from inference of the data and non statistical representation of the data extracted from the population. The selection bias is derived from deliberate selection of data which is selected by the researcher for the study. The common issues which arise in the study due to selection bias include excessive representation and emphasis on certain specific variables. Such a process is also termed as truncation. Selecting an asymmetry of data leads towards deliberate selection of cases which lead to biasness. In the process when the effect of selecting such variables are removed then casual inferences and biasness are eventually removed (Collier, 461-466). The most common caveat which arises in the studies when the data is biased includes underestimating the impact of the causal effect. While conducting the qualitative research it is very important that inductive character of the research is formed. Forming ad hoc hypothesizing will lead to correcting the data which has been selected for the research. The second caveat which is essential is forming the basic asymmetry is linking the variables of the research with each other. The correlation process and constraining the variance lead the researchers towards biasness. The third caveat arises when distinctive problems are associated with selection of variable are not associated with each other. Selection bias is a part of application of the data which has either mild or extreme effect on creating biasness. For this research and data of IT industry the random sampling process will be applied. In various studies criticism has been placed on selection biasness of the data. The perspective of the study will conclude that studies present biasness at all the levels of the research. It is necessary that even the studies which have been made with complete emphasis on the variables may possess biasness. The selection biasness issues in the data can be omitted from the research on the basis of spectrum which relate to the IT industry. The core problem of selection biasness occurs when the data fails in creating relevance with the research objectives. The research objective must aim to address broader sets of questions for various researches which link to causal and descriptive analysis of the research. Other methods include relating to similar contrasting cases which show implication and restrictions applying to the topic. FINDINGS & CONCLUSION This report is an analysis of the research objectives which are accomplished by using the quantitative method of study. The findings from this report show the challenges which arise while conducting the quantitative research. At the same time the processes of research has also been discussed. While conducting quantitative research it is essential that sample which is selected emphasizes on all the variables dependent and independent which are parts of the research. Besides sampling, testing the reliability and validity of the entire process must be carefully accomplished. Researcher must keep in mind that the research is not biased towards any variable. In this research issues related to sampling, reliability, validity and biasness of the data has been carefully assessed. All these components are related to the results which are acquired from the research. If the research fails in addressing any of these issues then the purpose and result derived from the research will fail to be accomplished. The purpose of using and applying quantitative research methods is discussed elaborately in this research. Challenges which the researchers face while applying quantitative methods of research are very important to be addressed while completing the research. Application of the research method in the IT industry and strategies used for this purpose are effectively discussed in this report. The data used for conducting the research must be aligned to the topic. While conducting quantitative research it is very important that all the discussed aspects in this report for aligning the data must be appropriately applied. Works Cited Adcock, Robert. 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