quantitative data in statistics

Quantitative data is a set of numbers collected from a group of people and involves statistical analysis.For example if you conduct a satisfaction survey from participants and ask them to rate their experience on a scale of 1 to 5. Found insideThis book is designed to help students, researchers and teachers to overcome such fear and to become confident in analysing and interpreting quantitative data and selecting the appropriate statistical tests. The primary aims of the book ... In other words, we were expecting 2.5 male students to receive an A grade, but in reality, only one student received the A grade. Answering such a question would require testing the following hypothesis: H 0 is called the null hypotheses , and H 1 is called the alternative hypothesis (sometimes, also represented as H a ). Qualitative Data vs Quantitative Data.

Sometimes data can be turned into categorical data by putting it into categories. The research problems used in the book reflect statistical applications related to interesting and important topics. In addition, the book provides a Research Analysis and Interpretation Guide to help students analyze research articles. All data that are the result of counting are called quantitative discrete data. A range of specialist software is available for undertaking quantitative data analysis, although Microsoft Excel is capable of running basic descriptive statistics as well as a range of more complex statistical analyses. e.g.

The bivariate scatter plot in the right panel of Figure 14.3 is essentially a plot of self-esteem on the vertical axis against age on the horizontal axis. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. If the correlations involve variables measured using interval scales, then this specific type of correlations are called Pearson product moment correlations . Examples of nominal data are letters, symbols, words, gender etc. Legend (Opens a modal) Possible mastery points. Such problems should be detected earlier during pretests and corrected before the main data collection process begins. Download.

Quantitative vs. Qualitative Data.

), the response scale for each item (i.e., whether it is measured on a nominal, ordinal, interval, or ratio scale; whether such scale is a five-point, seven-point, or some other type of scale), and how to code each value into a numeric format. For instance, we can measure how many times a sample of respondents attend religious services (as a measure of their “religiosity”) using a categorical scale: never, once per year, several times per year, about once a month, several times per month, several times per week, and an optional category for “did not answer.” If we count the number (or percentage) of observations within each category (except “did not answer” which is really a missing value rather than a category), and display it in the form of a table as shown in Figure 14.1, what we have is a frequency distribution. Gender is a nominal variable (male/female or M/F), and grade is a categorical variable with three levels (A, B, and C).

This text provides a non-technical approach to quantitative data analysis and a user-friendly introduction to SPSS. It takes the reader step-by-step through the techniques, reinforced by exercises. It's totally understandable - quantitative analysis is a complex topic, full of daunting lingo, like medians, modes, correlation and regression.Suddenly we're all wishing we'd paid a little more attention in math class…. data, can be analyzed using sophisticated statistical techniques.

For example, suppose I were to ask you your height, age, grade point average, or the the amount of time you study each day, I would be asking you to identify a numerical or countable value or variable. One of the slideshows on my site www.kevinmorrell.org.uk please reference the site if you use any of it - hope it is useful. Quantitative research 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 numerical data and generalizing it across groups of people or to explain a particular . It is an important research tool used by scientists, governments, businesses, and other organizations. Consider a set of eight test scores: 15, 22, 21, 18, 36, 15, 25, 15. The p-value is compared with the significance level (α), which represents the maximum level of risk that we are willing to take that our inference is incorrect. A simple cross-tabulation of the data may display the joint distribution of gender and grades (i.e., how many students of each gender are in each grade category, as a raw frequency count or as a percentage) in a 2 x 3 matrix.

Social Research Methods Examples of Quantitative Data - YOURDICTIONARY

The critical value of r depends on our desired significance level (α = 0.05), the degrees of freedom (df), and whether the desired test is a one-tailed or two-tailed test. "Data."

This is computed by sorting all values in a distribution in increasing order and selecting the middle value. From standard chi-square tables in any statistics book, the critical chi-square value for p=0.05 and df=2 is 5.99. Quantitative " Numerical values representing counts or measures. In quantitative research, after collecting data, the first step of statistical analysis is to describe . Examples include the width of a tube, the number of employees in a company and the income of all males. This is done by multiplying the marginal column total and the marginal row total for each cell and dividing it by the total number of observations. Discrete data takes on fixed values (e.g. For instance, if the missing value is one item in a multi-item scale, the imputed value may be the average of the respondent’s responses to remaining items on that scale. In this book, author Kyle Longest teaches the language of Stata from an intuitive perspective, furthering students’ overall retention and allowing a student with no experience in statistical software to work with data in a very short ...

The variable house price is a quantitative variable because it takes on numerical values. x���A ��vfP�W|����d�'��XD���sx��9��sx��9��9~~� ��'� data, can be analyzed using sophisticated statistical techniques. F�N����Eu�ף,�#�&l�1�)S�"S�����eZ����V�h����ǽq��:o�VG,ss�Y�z�UH�-� �#�g In this chapter, we will examine statistical techniques used for descriptive analysis, and the next chapter will examine statistical techniques for inferential analysis.

Hence, we must conclude that the observed grade pattern is not statistically different from the pattern that can be expected by pure chance. Data preparation usually follows the following steps. The time spent waiting in line for shoppers at a supermarket.

Quantitative Data. In the study of statistics, the main focus is on collecting data or information. In this type of research, you will understand the relationship between a dependent and independent variable.

Statistics is the science and practice of developing human knowledge through the use of empirical data expressed in quantitative form. Sometimes, it is necessary to transform data values before they can be meaningfully interpreted. Data, such as statistics and demographics can help us analyze trends and relationships between economic, social, cultural, and biological processes influencing a population. We can summarize categorical variables by using frequency tables.

Quantitative data analysis is one of those things that often strikes fear in students. )ɩL^6 �g�,qm�"[�Z[Z��~Q����7%��"�

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Quantitative data is considered to be ideal for statistical analysis.

However, the distribution of B grades was somewhat uniform, with six male students and five female students. Taylor, Courtney. Quantitative data is data that can be expressed as a number or can be quantified. Descriptive statistics are typically distinguished from inferential statistics. Figure 14.1.

Statistics, Demographics, and Other Quantitative Data Univariate statistics include: (1) frequency distribution, (2) central tendency, and (3) dispersion. Statistical analysis means investigating trends, patterns, and relationships using quantitative data. Quantitative data is numerical information that can be measured or counted. Reference: 1. The entered data should be frequently checked for accuracy, via occasional spot checks on a set of items or observations, during and after entry. In statistics, there's a variety of ways in which quantities or attributes of objects can be measured and calculated, all of which involve numbers in quantitative data sets. Univariate analysis, or analysis of a single variable, refers to a set of statistical techniques that can describe the general properties of one variable. During data entry, some statistical programs automatically treat blank entries as missing values, while others require a specific numeric value such as -1 or 999 to be entered to denote a missing value. Descriptive statistics: Statistics . Data cleaning is the process of preparing your data for analysis.

These data take on only certain numerical values. Qualitative data can be observed and recorded. The degree of freedom is the number of values that can vary freely in any calculation of a statistic. After computing bivariate correlation, researchers are often interested in knowing whether the correlation is significant (i.e., a real one) or caused by mere chance.

Frequency distribution of religiosity. can be used in computations. 2 pounds is less than 4 pounds " You can take a mathematical 'average' of these values, i.e. The following are common types of quantitative data. Quantitative data is analyzed using statistical methods, as quantitative data represents numbers from which datapoints can be calculated. Download Now. The book presents academics who teach research and information professionals who carry out research with new resources and guidance on lesser-known research paradigms. A codebook should be created to guide the coding process. Such deletion can significantly shrink the sample size and make it extremely difficult to detect small effects. The best kind of quantitative data in statistical an alysis is numerical, followed by ordinal, and lastly nominal. If p>0.05, then we do not have adequate statistical evidence to reject the null hypothesis or accept the alternative hypothesis. Further, since correlations are non-directional, the correlation between variables V1 and V2 is the same as that between V2 and V1. weight 0000003073 00000 n This unique approach—presented in language accessible to both students new to research as well as current practitioners—guides the reader in fully understanding the research options detailed throughout the text. In the two -tailed table, the critical value of r for α = 0.05 and df = 18 is 0.44. If so, such data can be entered but should be excluded from subsequent analysis. This text assumes students have been exposed to intermediate algebra, and it focuses on the applications of statistical knowledge rather than the theory behind it. Hence, the lower triangular matrix (values below the principal diagonal) is a mirror reflection of the upper triangular matrix (values above the principal diagonal), and therefore, we often list only the lower triangular matrix for simplicity.

Social Science Research: Principles, Methods, and Practices. The National Institute of Health funded this project with a goal of analyzing agricultural data to improve crop yields. If you're new to the world of quantitative data analysis and statistics, you've most likely run into the four horsemen of levels of measurement: nominal, ordinal, interval and ratio.And if you've landed here, you're probably a little confused or uncertain about them. Our data is limited only by the precision of our measuring devices. Thank you for reading CFI's guide to Descriptive Statistics.

Hypothetical data on age and self-esteem. The square of the standard deviation is called the variance of a distribution. �tq�X)I)B>==���� �ȉ��9. In case there are two middle values (if there is an even number of values in a distribution), the average of the two middle values represent the median. Descriptive analysis refers to statistically describing, aggregating, and presenting the constructs of interest or associations between these constructs. Such a curve is called a normal distribution. Note that these portals point to both free and pay sources for data, and to both raw data and processed statistics.

Quantitative Data Analysis with IBM SPSS 17, 18 & 19: A ...

A cross-tab is a table that describes the frequency (or percentage) of all combinations of two or more nominal or categorical variables. Age is a ratio-scale variable, while self-esteem is an average score computed from a multi-item self-esteem scale measured using a 7-point Likert scale, ranging from “strongly disagree” to “strongly agree.” The histogram of each variable is shown on the left side of Figure 14.3.

This section will expand on graphical methods while the next few sections will focus on numerical summaries of .

Most statistical programs provide a data editor for entering data. This book aims to encourage such students to engage in the quantitative investigation without fear. This latest edition has been fully updated to accommodate the needs of users of SPSS Releases 17, 18 and 19 while still being applicable to users of SPSS Releases 15 and 16. 2. Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings.

Data from a quantitative dataset, such as survey results, is usually loaded into a program such as Excel or the statistics software SPSS which enables researchers to quickly create tables and charts in order .

"What Is Quantitative Data?" Suppose a statistics professor collects information about the classification of her students as freshmen, sophomores, juniors, or seniors. Figure 14.3. The manually computed value of correlation between age and self-esteem, using the above formula as shown in Table 14.1, is 0.79. Quantitative data is the most relevant form of data for use in both mathematics and statistics, as it is the primary type of data that can be measured objectively. Bivariate analysis examines how two variables are related to each other. Nominal, Ordinal, Interval & Ratio Data - Grad Coach Quantitative data is defined as the value of data in the form of counts or numbers where each data-set has an unique numerical value associated with it. What Is Quantitative Statistical Analysis? (with pictures)

Another useful way of presenting bivariate data is cross-tabulation (often abbreviated to cross-tab, and sometimes called more formally as a contingency table). The arithmetic mean of these values is (15 + 20 + 21 + 20 + 36 + 15 + 25 + 15)/8 = 20.875. 0000002994 00000 n

As an example, let us assume that we have the following observations of gender and grade for a sample of 20 students, as shown in Figure 14.3. The two middle values are 18 and 22, and hence the median is (18 + 22)/2 = 20. Descriptive statistics can be used to describe the basic features of your data and reduce it down to an understandable level.

Dispersion refers to the way values are spread around the central tendency, for example, how tightly or how widely are the values clustered around the mean.

Interval estimates require parameters set in a specific data sample.

The major characteristics of this type of research include the following: The data is collected using structured research instruments (e.g., samples, survey, questionnaire, etc.).

Now, quantitative data are data that take on numerical values, and that is why quantitative data is also referred to as numerical data. There are three major estimates of central tendency: mean, median, and mode.

How many (dirkcuys) There are two types of data. The common types of quantitative data. We can also do some things with categorical data. Quantitative ("quant") research is used to quantify the problem by way of generating numerical data that can be transformed into useable statistics.

Descriptive statistics summarize and organize characteristics of a data set. For example, if data are collected on annual income (quantitative), occupation data (qualitative) could also be gathered to get more detail on the average annual income for each type of occupation.

There are two different statistical tables for one-tailed and two -tailed test. In other words, do the above frequency counts differ from that that may be expected from pure chance?

Download to read offline. Taylor, Courtney. In this example, df = (2 – 1) * (3 – 1) = 2.

There is no way that a value can be a fraction or between any of the whole numbers.

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