All of the potential highs, lows, and sentiments associated with investing can overshadow the ultimate goal: Markowitz used math to quantify diversification, and is cited as an early adopter of the concept that mathematical models could be applied to investing.
Moderate and Advanced Analytical Methods The first thing you should do with your data is tabulate your results for the different variables in your data set. This process will give you a comprehensive picture of what your data looks like and assist you in identifying patterns.
The best ways to do this are by constructing frequency and percent distributions A frequency distribution is an organized tabulation of the number of individuals or scores located in each category see the table below.
This will help you determine: Variable Frequencies for Student Summer Program Survey Data A percent distribution displays the proportion of participants who are represented within each category see below.
The most common descriptives used are: Mean — the numerical average of scores for a particular variable Minimum and maximum values — the highest and lowest value for a particular variable Median — the numerical middle point or score that cuts the distribution in half for a particular variable Calculate by: Listing the scores in order and counting the number of scores If the number of scores is odd, the median is the number that splits the distribution If the number of scores is even, calculate the mean of the middle two scores Mode — the most common number score or value for a particular variable Depending on the level of measurement, you may not be able to run descriptives for all variables in your dataset.
The mode most commonly occurring value is 3, a report of satisfaction. Crosstabs allow you to disaggregate the data across multiple categories.
By looking at the table below, you can clearly see that the demographic makeup of each program city is different. This allows you to take a deeper look at the units that make up that category. In the table below, we explore this subcategory of participants more in-depth.
From this table, you can see that: All of the students who were dissatisfied with the program were students of color. All but one of the students of color in the Boston program were dissatisfied with their experience since there were 6 students of color in the Boston program.
This result is masked when you report the average satisfaction level of all participants in the program is 2. In addition to the basic methods described above there are a variety of more complicated analytical procedures that you can perform with your data.
Correlation Regression Analysis of variance These types of analyses generally require computer software e. We provide basic descriptions of each method but encourage you to seek additional information e.
For more information on quantitative data analysis, see the following sources: Correlation A correlation is a statistical calculation which describes the nature of the relationship between two variables i. An important thing to remember when using correlations is that a correlation does not explain causation.
A correlation merely indicates that a relationship or pattern exists, but it does not mean that one variable is the cause of the other.
Analysis of Variance An analysis of variance ANOVA is used to determine whether the difference in means averages for two groups is statistically significant.
For example, an analysis of variance will help you determine if the high school grades of those students who participated in the summer program are significantly different from the grades of students who did not participate in the program.
Regression Regression is an extension of correlation and is used to determine whether one variable is a predictor of another variable. A regression can be used to determine how strong the relationship is between your intervention and your outcome variables.
More importantly, a regression will tell you whether a variable e. A variable can have a positive or negative influence, and the strength of the effect can be weak or strong. Like correlations, causation can not be inferred from regression.The main differences between quantitative and qualitative research consist in respect to data sample, data collection, data analysis, and last but not least in regard to outcomes.
Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques.
Quantitative research focuses on gathering. Quantitative Research Definition: Quantitative research, is defined as a the systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical or computational techniques.
Quantitative research gathers information from existing and potential customers. What is 'Quantitative Analysis (QA)' Quantitative analysis (QA) is a technique that seeks to understand behavior by using mathematical and statistical modeling, measurement, and research.
Quantitative analysis (QA) is a technique that seeks to understand behavior by using mathematical and statistical modeling, measurement, and research. In quantitative data analysis you are expected to turn raw numbers into meaningful data through the application of rational and critical thinking.
Quantitative data analysis may include the calculation of frequencies of variables and differences between variables. A quantitative approach is usually.