Quantitative analysis, It is defined as a technique, or methodology, that permits investigators to convert data into numerical format that facilitates statistical analysis of research data. It allows the quantification of attributes by constructing statistical models to explain observations and serves as a platform for inferring decisions. Researchers employ the quantitative approach for developing knowledge through strategies of inquiry using predetermined instruments such as surveys and experiments. The purpose of quantitative research involves determining the relationship between one variable [independent variable] and another variable [dependent or outcome] within a population. Quantitative research, initially characterized by defining research questions, identifies variables and designs the research methodology before collecting data. Researchers collect data using structured research instruments extracted from representative population samples. Data analysis, which uses statistical procedures, arranges research data in tables, charts, figures and graphics. There are several statistical software packages that can assist you with you statistical analysis of quantitative data. See below for more details.
Babbie, Earl R. The Practice of Social Research. 12th ed. Wadsworth Cengage, 2010
Creswell, John W. Research Design: Qualitative, Quantitative and Mixed Methods Approach, 2nd ed. Sage Publications. 2003.
Quantitative Approaches. Center for Innovation in Research and Teaching.
|Statistical Software Available at UNLV||Campus Labs with Statistical Software Packages|
|Mathematica. Integrated system for computation, modeling, simulation, visualization, development and deployment.||CBC_B131 CBC_ C323 TBE_ A311|
|MATLAB. High-level language and interactive environment that enables users to perform computationally intensive tasks faster than with traditional programming languages such as C, C++, and Fortran.||ARC_72 CBC_B131 CBC_C309 CBC_C311 CBC_C321 SU_233 TBE_B310 CBC_B348 CBC_B350 CBC_B367 TEC_112 TEC_113|
|Mplus. Statistical modeling program that provides researchers with a flexible tool to analyze their data. It offers researchers a wide choice of models,||CEB_211 CEB_212 CEB_309A|
|R for Windows. Software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.||CBC_B131 CBC_C129 CBC_C309 CBC_C311 CBC_C321 CBC_C323 GUA_3202 SU_233 WRI_C211|
|SAS. Traditional software package for statistical analysis of variance package that uses predictive modeling to exact methods that employs statistical visualization techniques. SAS software designed for both specialized and enterprise level analytical needs.||BHS_131 BEH_102 CBC_B135 GUA_3202 LLB_2141 SU_233|
|SPSS (IBM). comprehensive statistical package for analyzing any type of data from research projects, from almost any type of file, and use to generate tabulated reports, charts, and plots of distributions and trends, descriptive statistics, and complex statistical analysis.||BEH_102 BHS_131 BHS_451 CBC_B131 CBC_B135 CBC_C125 CBC_C129 CBC_C309 CBC_C311 CBC_C321 CBC_C323 CEB_211 CEB_212 CEB_309A GUA_1125 GUA_3202 LLA_2141 RLL_132 SU_ 233 TBE_A311 TBE_B310 TBE_B348 TBE_B367 TEX-112 TEC_113 WRI_C211|
|STATA. Complete integrated statistical software package that provides everything you need for data analysis, data management, and graphics.||ARC_172 CBC_B131 GUA_3202 LLB_2141 SU_233|