QUANTITATIVE ANALYSIS IN BUSINESS
Today businesses must be more profitable, react quicker, and offer higher-quality products and services, and do it all with fewer people and at lower cost. An essential requirement in this process is effective knowledge creation and management. There is no lack of information, but there is a dearth of knowledge. As Tom Peters said in his book Thriving on Chaos, “We are drowning in information and starved for knowledge”.
The information available for decision making exploded in recent years, and will continue to do so in the future, probably even faster. Until recently, much of that information just disappeared. It was not either collected, or discarded. Today this information is being collected and stored in data warehouses, and it is available to be “mined” for improved decision making. Some of that information can be analyzed and understood with simple statistics, but much of it requires more complex, quantitative analyses to convert these data into knowledge.
Quantitative analyses are popular because they enable organizations to create knowledge and thereby improve their decision making. A number of technological advances help us to apply quantitative analysis. Among of the most important are the developments in computer hardware and software. User-friendly software packages brought data analysis into the point-and-click era, and we can quickly analyze mountains of complex data with relative ease. Indeed, industry, government, and university-related research centers throughout the world are making widespread use of these techniques.
Dear Bloggers, let use the term researcher when referring to a data analyst within either practitioner or academic communities. We feel it inappropriate to make any distinction between these two areas, because research in either relies on both theoretical and quantitative bases.
Measurement Scales
Data can be classified in to one of two categories—nonmetric (qualitative) and metric (quantitative)—based on the type of attributes or characteristics they represent. Nonmetric measurements can be made with either a nominal or an ordinal scale and the two different metric measurement scales are interval and ratio scales.
Validity and Reliability
The researcher’s goal of reducing measurement error can follow several paths. The researcher must address two important characteristics of measure:
o Validity is the degree to which a measure accurately represents what it is supposed to. For example, if we want to measure discretionary income, we should not ask about total household income. Ensuring validity starts with a thorough understanding of what is to be measured and then making the measurement as “correct” and accurate as possible. However, accuracy does not ensure validity.
o Reliability is the degree to which the observed variable measures the “true” value and is “error free”; thus it is the opposite of measurement error. If the same measure is asked repeatedly, for example, more reliable measures will show greater consistency than less reliable measures.
Statistical Significance versus Statistical Power
The researcher must strike a balance between the level of alpha (α) and the resulting power. But why can’t high levels of power always be achieved? Power is not solely a function of alpha (α). It is actually determined by three factors: effect size, sample size, and alpha (when alpha become more restrictive then power decreases).
Multivariate Analysis
Multivariate analysis refers to all statistical techniques that simultaneously analyze multiple measurements on individuals or objects under investigation. Thus, any simultaneous analysis of more than two variables can be loosely considered multivariate analysis. Many multivariate techniques are extensions of univariate analysis (analysis of single-variable distributions) and bivariate analysis (correlation, cross-classification, analysis of variance, and simple regression used to analyze two variables.
Classification of multivariate techniques:
Dependence techniques: multiple regression, multiple discriminant analysis, linear probability model (LPM), structural equation modeling (SEM), canonical correlation, multivariate analysis of variance.
Interdependence techniques: factor analysis, cluster analysis, multidimensional scaling, correspondence analysis.
Today businesses must be more profitable, react quicker, and offer higher-quality products and services, and do it all with fewer people and at lower cost. An essential requirement in this process is effective knowledge creation and management. There is no lack of information, but there is a dearth of knowledge. As Tom Peters said in his book Thriving on Chaos, “We are drowning in information and starved for knowledge”.
The information available for decision making exploded in recent years, and will continue to do so in the future, probably even faster. Until recently, much of that information just disappeared. It was not either collected, or discarded. Today this information is being collected and stored in data warehouses, and it is available to be “mined” for improved decision making. Some of that information can be analyzed and understood with simple statistics, but much of it requires more complex, quantitative analyses to convert these data into knowledge.
Quantitative analyses are popular because they enable organizations to create knowledge and thereby improve their decision making. A number of technological advances help us to apply quantitative analysis. Among of the most important are the developments in computer hardware and software. User-friendly software packages brought data analysis into the point-and-click era, and we can quickly analyze mountains of complex data with relative ease. Indeed, industry, government, and university-related research centers throughout the world are making widespread use of these techniques.
Dear Bloggers, let use the term researcher when referring to a data analyst within either practitioner or academic communities. We feel it inappropriate to make any distinction between these two areas, because research in either relies on both theoretical and quantitative bases.
Measurement Scales
Data can be classified in to one of two categories—nonmetric (qualitative) and metric (quantitative)—based on the type of attributes or characteristics they represent. Nonmetric measurements can be made with either a nominal or an ordinal scale and the two different metric measurement scales are interval and ratio scales.
Validity and Reliability
The researcher’s goal of reducing measurement error can follow several paths. The researcher must address two important characteristics of measure:
o Validity is the degree to which a measure accurately represents what it is supposed to. For example, if we want to measure discretionary income, we should not ask about total household income. Ensuring validity starts with a thorough understanding of what is to be measured and then making the measurement as “correct” and accurate as possible. However, accuracy does not ensure validity.
o Reliability is the degree to which the observed variable measures the “true” value and is “error free”; thus it is the opposite of measurement error. If the same measure is asked repeatedly, for example, more reliable measures will show greater consistency than less reliable measures.
Statistical Significance versus Statistical Power
The researcher must strike a balance between the level of alpha (α) and the resulting power. But why can’t high levels of power always be achieved? Power is not solely a function of alpha (α). It is actually determined by three factors: effect size, sample size, and alpha (when alpha become more restrictive then power decreases).
Multivariate Analysis
Multivariate analysis refers to all statistical techniques that simultaneously analyze multiple measurements on individuals or objects under investigation. Thus, any simultaneous analysis of more than two variables can be loosely considered multivariate analysis. Many multivariate techniques are extensions of univariate analysis (analysis of single-variable distributions) and bivariate analysis (correlation, cross-classification, analysis of variance, and simple regression used to analyze two variables.
Classification of multivariate techniques:
Dependence techniques: multiple regression, multiple discriminant analysis, linear probability model (LPM), structural equation modeling (SEM), canonical correlation, multivariate analysis of variance.
Interdependence techniques: factor analysis, cluster analysis, multidimensional scaling, correspondence analysis.
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