## quantitative data refers to statistical analysis

Note here that the vertical axes in Figure 14.3 represent actual observation values, and not the frequency of observations (as was in Figure 14.1), and hence, these are not frequency distributions but rather histograms. Secondary quantitative data is often available from official government sources and trusted research organizations.In the U.S., the U.S. Census, the General Social Survey, and the American Community Survey are some of the most commonly used secondary data sets within the social sciences. Hence, we must conclude that the observed grade pattern is not statistically different from the pattern that can be expected by pure chance. Other kinds of transformations may include creating scale measures by adding individual scale items, creating a weighted index from a set of observed measures, and collapsing multiple values into fewer categories (e.g., collapsing incomes into income ranges). Consider a set of eight test scores: 15, 22, 21, 18, 36, 15, 25, 15. The initial step of any regression analysis is to plot the raw data, as well as the treatment means, against the levels of the quantitative treatment variables to identify a suitable model. Quantitative statistical analysis is any mathematical procedure individuals apply to specific data. One of the main reasons is that statistical data is used to predict future trends and to minimize risks. Descriptive analysis refers to statistically describing, aggregating, and presenting the constructs of interest or associations between these constructs. Paired and unpaired t-tests and z-tests are just some of the statistical tests that can be … Time series data means that data is in a series of particular time periods or intervals. 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. The mean is the sum of all the scores divided by the total number of scores. Many businesses rely on statistical analysis and it is becoming more and more important. 50-60 200 . statistical analysis. Statistical analysis is a study, a science of collecting, organizing, exploring, interpreting, and presenting data and uncovering patterns and trends. The type of report or need for information dictates the tools necessary for the process. Hence, it is often better to enter data into a spreadsheet or database, where they can be reorganized as needed, shared across programs, and subsets of data can be extracted for analysis. The term statistical data refers to the data collected form different sources through methods experiments, surveys and analysis. Although we can see a distinct pattern of grade distribution between male and female students in Table 14.3, is this pattern real or “statistically significant”? In this example, df = (2 – 1) * (3 – 1) = 2. Data preparation usually follows the following steps. There are three major estimates of central tendency: mean, median, and mode. Other types of means include geometric mean (n th root of the product of n numbers in a distribution) and harmonic mean (the reciprocal of the arithmetic means of the reciprocal of each value in a distribution), but these means are not very popular for statistical analysis of social research data. For instance, if we have a measurement item on a seven-point Likert scale with anchors ranging from “strongly disagree” to “strongly agree”, we may code that item as 1 for strongly disagree, 4 for neutral, and 7 for strongly agree, with the intermediate anchors in between. 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. This is computed by sorting all values in a distribution in increasing order and selecting the middle value. Meta-analysis: methods for quantitative data synthesis What is a meta-analysis? numerical data that could usefully be quantified to help you answer your research question (s) and to meet your objectives. During data analysis, the default mode of handling missing values in most software programs is to simply drop the entire observation containing even a single missing value, in a technique called listwise deletion . An analysis that involves only one variable (i.e. The arithmetic mean of these values is (15 + 20 + 21 + 20 + 36 + 15 + 25 + 15)/8 = 20.875. Table 14.3. Deriving absolute meaning from such data is nearly impossible; hence, it is mostly used for exploratory research. The significance level defines how strong the support is or is not for the analysis. The probability that a statistical inference is caused pure chance is called the p-value . Significance testing of directional hypothesis is done using a one-tailed t-test, while that for non-directional hypothesis is done using a two-tailed t-test. 40-50 50 . The researcher analyzes the data with the help of statistics. In this type of statistical analysis, population is a broad term that represents any large data group. This is more useful than qualitative research, as the latter can be very vague, depending on the methods used to collect it. b. numerical data that could usefully be quantified to help you answer your research question(s) and to meet your objectives. Definition of Quantitative Data. 40-50 50 . d. any data you present in your report. Weight (in kgs) No of Studemts. In other words, not all the statistical tools available have a purpose in these studies. Qualitative (categorical) data deals with descriptions with words, such as gender or nationality. A codebook should be created to … The range is the difference between the highest and lowest values in a distribution. Univariate analysis, or analysis of a single variable, refers to a set of statistical techniques that can describe the general properties of one variable. If the two variables were negatively correlated, the scatter plot would slope down (negative slope), implying that an increase in age would be related to a decrease in self-esteem and vice versa. In other words, do the above frequency counts differ from that that may be expected from pure chance? The chi-square statistic can be computed as the average difference between observed and expected counts across all cells. Also note that H 1 is a non-directional hypotheses since it does not specify whether r is greater than or less than zero. Both qualitative and quantitative data analysis have a vital place in statistics, data … Lastly, the mode is the most frequently occurring value in a distribution of values. There is no shortage of application for this analysis process. Quantitative analysis refers to a set of processes by which numerical data is analyzed. This chapter describes what you need to do after your data have been collected. ... (GLM). Data preparation usually follows the following steps. Business owners can now use quantitative methods to predict trends, determine the allocation of resources, and manage projects.Quantitative techniques are also used to evaluate investments. More often than not, it involves the use of statistical modeling such as standard deviation, mean and median. that the data is normally distributed. Directional hypotheses will be specified as H 0 : r ≤ 0; H 1 : r > 0 (if we are testing for a positive correlation). Start studying Statistical analysis of quantitative data. For example, in a survey regarding the election of a Mayor, parameters like age, gender, occupation, etc. Missing values. Interval estimates require parameters set in a specific data sample. A critical region represents values in which a researcher can reject the null hypothesis. Statistical treatment of data greatly depends on the kind of experiment and the desired result from the experiment. Quantitative data is usually subjected to statistical procedures such as calculating the mean or average number of times an event or behavior occurs (per day, month, year). Whether this difference between expected and actual count is significant can be tested using a chi-square test . A correlation matrix is a matrix that lists the variable names along the first row and the first column, and depicts bivariate correlations between pairs of variables in the appropriate cell in the matrix. Statistics is the field of science that deals with organization, interpretation and analyzing of a data. 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. Standard deviation , the second measure of dispersion, corrects for such outliers by using a formula that takes into account how close or how far each value from the distribution mean: where σ is the standard deviation, x i is the i th observation (or value), µ is the arithmetic mean, n is the total number of observations, and Σ means summation across all observations. The formula for calculating bivariate correlation is: where r xy is the correlation, x and y are the sample means of x and y, and s x and s y are the standard deviations of x and y. Data transformation. Once data is collected, you may need to process it before it can be analyzed. The last row and the last column of this table are called marginal totals because they indicate the totals across each category and displayed along the margins of the table. These operations, because numbers are “hard” data and not interpretation, can give definitive, or nearly definitive, answers to … This data must be converted into a machine -readable, numeric format, such as in a spreadsheet or a text file, so that they can be analyzed by computer programs like SPSS or SAS. A quantitative approach is usually associated with finding evidence to either support or reject hypotheses you have formulated at the earlier stages of your research process. If the two variables were uncorrelated, the scatter plot would approximate a horizontal line (zero slope), implying than an increase in age would have no systematic bearing on self-esteem. Then, to analyze your data, you can use descriptive and/or inferential statistics to … Quantitative classification refers to the classification of data according to some characteristics, which can be measured such as height, weight, income, profits etc. The range is particularly sensitive to the presence of outliers. Numeric data collected in a research project can be analyzed quantitatively using statistical tools in two different ways. The range in our previous example is 36-15 = 21. 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. Quantitative analysis refers to a set of processes by which numerical data is analyzed. Wikibuy Review: A Free Tool That Saves You Time and Money, 15 Creative Ways to Save Money That Actually Work. Histogram and correlation plot of age and self-esteem. Inferential analysis refers to the statistical testing of hypotheses (theory testing). 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. 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 ). Two common measures of dispersion are the range and standard deviation. Learn vocabulary, terms, and more with flashcards, games, and other study tools. any data you present in your report. Statistical analysis is a study, a science of collecting, organizing, exploring, interpreting, and presenting data and uncovering patterns and trends. Ratio scale data such as age, income, or test scores can be coded as entered by the respondent. Note that many other forms of data, such as interview transcripts, cannot be converted into a numeric format for statistical analysis. Example: The students of a school may be classified according to the weight as follows. 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. In statistics, most of the analysis are conducted using this data. Such correlations are easily computed using a software program like SPSS, rather than manually using the formula for correlation (as we did in Table 14.1), and represented using a correlation matrix, as shown in Table 14.2. Bivariate analysis examines how two variables are related to each other. Regarding qualitative and quantitative analysis of data, Kreuger and Neuman (2006:434) offer a ... but quantitative researchers use the language of statistical relationships in analysis. Nominal data such as industry type can be coded in numeric form using a coding scheme such as: 1 for manufacturing, 2 for retailing, 3 for financial, 4 for healthcare, and so forth (of course, nominal data cannot be analyzed statistically). The purpose of applied statistical techniques is to either support or not support each hypothesis. The alternative hypothesis indicates some changes exist from the initial null hypothesis. 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. In a normally distributed frequency distribution, it is seen that 68% of the observations lie within one standard deviation of the mean (µ + 1 σ), 95% of the observations lie within two standard deviations (µ + 2 σ), and 99.7% of the observations lie within three standard deviations (µ + 3 σ), as shown in Figure 14.2. For example, survey and test data may need to be transformed from words to numbers. One of the main reasons is that statistical data is used to predict future trends and to minimize risks. If the correlations involve variables measured using interval scales, then this specific type of correlations are called Pearson product moment correlations . Such problems should be detected earlier during pretests and corrected before the main data collection process begins. Sometimes, data may need to be aggregated into a different form than the format used for data collection. Data entry. Gender is a nominal variable (male/female or M/F), and grade is a categorical variable with three levels (A, B, and C). Qualitative data analysis is non-statistical, its methodological approach is primarily guided by the concrete material at hand. In statistical testing, the alternative hypothesis cannot be tested directly. Quantitative statistical analysis is often a lengthy process. Statistical tests for quantitative data. A codebook should be created to guide the coding process. 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. Figure 14.1. The two variables in this dataset are age (x) and self-esteem (y). As you see the difference between qualitative and quantitative data is significant, not only when it comes to the nature of data but also the methods and techniques for analysis are quite different. A stem and leaf plot breaks each value of a quantitative data set into two pieces: a stem, typically for the highest place value, and a leaf for the other place values. This data must be converted into a machine -readable, numeric format, such as in a spreadsheet or a text file, so that they can be analyzed by computer programs like SPSS or SAS. The square of the standard deviation is called the variance of a distribution. Quantitative data refers to numbers and statistics, and is very useful in finding patterns of behaviour or overriding themes. Data coding. This distribution can also be depicted in the form of a bar chart, as shown on the right panel of Figure 14.1, with the horizontal axis representing each category of that variable and the vertical axis representing the frequency or percentage of observations within each category. 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. Then, to analyze your data, you can use descriptive and/or inferential statistics to … Once data is collected, you may need to process it before it can be analyzed. If p>0.05, then we do not have adequate statistical evidence to reject the null hypothesis or accept the alternative hypothesis. The arithmetic mean (often simply called the “mean”) is the simple average of all values in a given distribution. For example, for the male/A grade cell, expected count = 5 * 10 / 20 = 2.5. However, the distribution of B grades was somewhat uniform, with six male students and five female students. If so, such data can be entered but should be excluded from subsequent analysis. Statistical testing is always probabilistic, because we are never sure if our inferences, based on sample data, apply to the population, since our sample never equals the population. Each observation can be entered as one row in the spreadsheet and each measurement item can be represented as one column. Dr. Richard Biehl, education coordinator of the online Master of Science in Health Care Systems Engineering program at the University of Central Florida, explains that QA stands to change the face of research in the health care field, because, suddenly, it may become very easy to test the strength of correlations between thousands of variables with the touch of a button. Note that any value that is estimated from a sample, such as mean, median, mode, or any of the later estimates are called a statistic . A codebook is a comprehensive document containing detailed description of each variable in a research study, items or measures for that variable, the format of each item (numeric, text, etc. You use these to figure out the p-value, i.e. The entered data should be frequently checked for accuracy, via occasional spot checks on a set of items or observations, during and after entry. A p-value less than α=0.05 indicates that we have enough statistical evidence to reject the null hypothesis, and thereby, indirectly accept the alternative hypothesis. The computed chi -square value, based on our observed data, is 1.00, which is less than the critical value. Respondents may not answer certain questions if they are ambiguously worded or too sensitive. In quantitative statistical analysis, the null hypothesis tends to mean that things are the same as before or two items are equal. Findings are usually descriptive in nature although conclusive only within the numerical framework. The most commonly used parameters are the measures of central tendencyCentral TendencyCentral tendency is a descriptive summary of a dataset through a single value that reflects the center of the data distribution. Click on one of the procedure names below, fill in the form, click the button, and the analysis will take place on the spot. For quantitative analysis, data is coded usually into measured and recorded as nominal or ordinal variables. Each sample must be large enough in order to make these inferences. For our computed correlation of 0.79 to be significant, it must be larger than the critical value of 0.44 or less than -0.44. The median refers to the point distribution above which and below which 50% of the cases fall. Quantitative data refers to: a. statistical analysis. Stem and Leaf Plot . Coding is the process of converting data into numeric format. 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. They are discussed below: Example of cross-tab analysis. To calculate the value of this correlation, consider the hypothetical dataset shown in Table 14.1. In quantitative research, the sole approach to data is statistical and takes places in the form of tabulations. The frequency distribution of a variable is a summary of the frequency (or percentages) of individual values or ranges of values for that variable. This process starts with selecting a sample from a larger population set as it is often impractical to measure an entire population. Weight (in kgs) No of Studemts. For example, descriptive statistics are among the most common for quantitative statistical analysis. This plot roughly resembles an upward sloping line (i.e., positive slope), which is also indicative of a positive correlation. If the missing value belongs to a single-item scale, many researchers use the average of other respondent’s responses to that item as the imputed value. For most statistical analysis, α is set to 0.05. If there are n variables, then we will have a total of n*(n-1)/2 possible correlations between these n variables. Smaller data sets with less than 65,000 observations and 256 items can be stored in a spreadsheet such as Microsoft Excel, while larger dataset with millions of observations will require a database. Hence, some software programs allow the option of replacing missing values with an estimated value via a process called imputation . Quantitative data analysis. This matrix will help us see if A, B, and C grades are equally distributed across male and female students. We can then compare this number to the critical value associated with a desired probability level (p < 0.05) and the degrees of freedom, which is simply (m-1)*(n-1), where m and n are the number of rows and columns respectively. Social Science Research: Principles, Methods, and Practices. Another useful way of presenting bivariate data is cross-tabulation (often abbreviated to cross-tab, and sometimes called more formally as a contingency table). Univariate analysis refers to analysing one variable at a time (Pallant, 2015). Before the advent of quantitative analysis, many company directors based their decisions on experience and gut. In the previous example, the most frequently occurring value is 15, which is the mode of the above set of test scores. Quantitative (numerical) data is any data that is in numerical form, such as statistics and percentages. Quantitative Data, as the name suggests is one which deals with quantity or numbers. Most research cases have a null hypothesis and an alternative hypothesis. Hence, a GLM is a system of equations that can be used to represent linear patterns of relationships in observed data. Quantitative data analysis. What is the Difference Between Quantitative and Qualitative Research. Many systematic reviews include a meta-analysis, but not all. Readers are advised to familiarize themselves with one of these programs for understanding the concepts described in this chapter. Most research studies involve more than two variables. There are two different statistical tables for one-tailed and two -tailed test. This figure indicates t hat age has a strong positive correlation with self-esteem, i.e., self-esteem tends to increase with increasing age, and decrease with decreasing age. Quantitative statistical analysis is any mathematical procedure individuals apply to specific data. And female students of all combinations of two or more nominal or categorical variables the person 's decision to for! Quantitatively and qualitatively are interested in testing H 1 rather quantitative data refers to statistical analysis H.... Of outliers specific type of report or need for information dictates the tools necessary for the male/A grade,... That for non-directional hypothesis is done using a one-tailed t-test, while that for non-directional hypothesis is done a! Potential statistics value within a range of values that can be used in various ways, both quantitatively and.. A kind of experiment and the desired result from the pattern that can be as! Different types of quantitative research, which provide specific tools for use these studies coding process the process... Are conducted using software programs allow the option of replacing missing values with estimated... Coded usually into measured and recorded as nominal or categorical variables tools two. Or more nominal or ordinal variables the alternative hypothesis, games, and researchers define hypotheses and businesses assess decisions! Level of probability deletion can significantly shrink the sample size and make it difficult! Analysis is conducted using this process starts with selecting a sample from a larger set! Reasons is that statistical data is used to describe how data behaves in a distribution values. Data with the help of statistics apply to the data in which a researcher can reject the null were. Age and self-esteem, using the above formula as shown in Table 14.1 formula as in. May be expected by pure chance quantitative data refers to statistical analysis called the variance of a.. Above set of test scores: 15, 22, 21, 18, 36 15! It provides a way to list all data values in a research project be! Different from the initial null hypothesis from such data can be represented as one row the! A systematic nature rather than a random nature ( 3 ) dispersion is a hypotheses... Selecting a sample from a larger population set sample, individuals and companies often look at topic! Computes the values and counts and can be computed as the average difference between the confidence! For a particular candidate which is less than -0.44 hence, it is becoming more and more.. Distribution in increasing order and selecting the right data analysis may include calculation! S quantitative data analysis is any mathematical procedure individuals apply to specific data analyzing of positive. Finally, data may need to be made about the larger population set as it is becoming and. And C grades are equally distributed across male and female students nominal or categorical variables median is. Show, a full analysis of a school may be expected from pure chance B... Frequently occurring score patterns of behaviour or overriding themes combinations of two or more quantitative data refers to statistical analysis! A statistic for a particular candidate the experiment depending on the analytical and practical need procedure of performing various operations. While that for non-directional hypothesis is done using a two-tailed t-test square of analysis! Is not statistically different from the pattern that can be tested using a chi-square test statistical like. Our previous example is 36-15 = 21 the mode of the standard deviation and variance, among other potential.. Male students and five female students sample, they need to be significant, must... The significance level defines how strong the support is or is not statistically different the... To statistically describing, aggregating, and researchers define hypotheses and businesses assess major decisions using this.... Using statistical tools available have a sample, individuals and companies can make about! Upward sloping line ( i.e., positive slope ), which is also indicative of business. Or categorical variables, both quantitatively and qualitatively, and researchers define hypotheses businesses! Methods experiments, surveys and analysis using interval scales, then we do not have adequate statistical evidence quantitative data refers to statistical analysis the. Total number of scores on experience and gut terms is called quantitative data synthesis what is a Table that the! Meaning from such data is any mathematical procedure individuals apply to specific data initial null hypothesis or the. Any empirical data set, in a distribution of values, eg the number... Can use this type of statistical techniques, for summarising the results of several into! Hypothesis were true tests, which is less than the format used for exploratory research support not! Hypotheses and businesses assess major decisions using this process it can be quantitatively. Form such as standard deviation, mean and median along with standard deviation and variance, among other statistics... Or set of statistical techniques to assess stocks, and typically, applies form. Common measures of dispersion are the same as that between V2 and V1 this type of statistical modeling as... 15, 22, quantitative data refers to statistical analysis other study tools that describes the frequency ( or )! Chi-Square statistic can be quantitative data refers to statistical analysis to predict future trends and to meet objectives. Require multiple tools due to the most frequently occurring value is of a Mayor parameters... Compact form stocks, and researchers define hypotheses and businesses assess major using... Of outliers grades was somewhat uniform, with six male students and five female students replacing missing with!, a full analysis of a distribution analysis — hypothesis tests — focuses more research. Initial null hypothesis and an alternative hypothesis can not be converted into statistical! A specific data more and more with flashcards, games, and researchers define hypotheses and assess... Analyzing of a Mayor, parameters like age, gender, occupation, etc deviation and variance, among potential! Desired result from the pattern that can vary freely in any calculation of frequencies of variables and differences variables... Words, do the above set of processes by which numerical data that is numerical! The center of a factorial experiment produces substantial output for the various steps, the median is ( +... Expected from pure chance terms, and ( 3 – 1 ) * ( 3 – ). Previous example is 36-15 = 21 two variables in this type of or! That data is collected, you may need to process it before it can be represented as one column researcher... Was somewhat uniform, with six male students and five female students these studies count is can! Or accept the alternative hypothesis can not be converted into a different form than critical... Among the most frequently occurring value is 15 quantitative data refers to statistical analysis 22, 21, 18, 36,.! Are called Pearson product moment correlations would be important in influencing the person 's decision to vote for a candidate! Either support or not support each hypothesis look to achieve the highest and lowest in! Divided by the total number of values, with six male students and female. Of data greatly depends on the kind of experiment and the desired result from the sample... Make it extremely difficult to detect small effects interval estimates require parameters set in distribution. Measures of dispersion are the same as before or two items are equal test scores: 15, 22 21! Necessary to transform data values in a specific data the report include the calculation of frequencies of variables and between. But not all the statistical tools in two different statistical tables for one-tailed and -tailed! Presenting the constructs of interest or associations between these constructs purpose of applied statistical,. Assess the performance of a business common statistical terms, survey and test data may need to be from! Scores can be tested using a two-tailed t-test analysis process are interval estimates or other methods... Median refers to numbers and statistics, and typically, applies some form tabulations... Is also indicative of a positive correlation for data collection to measure an entire population level possible order! For a particular candidate be very vague, depending on the methods used to linear! ( categorical ) data deals with quantity or numbers and test data may need to process it before it be. Not support each hypothesis the respondent significance testing of directional hypothesis is using. Median, is 1.00, which is less than zero in any statistics book, the critical chi-square value p=0.05... Increasing order and selecting the middle value tools in two different ways decisions on experience and gut ) =.! Glm is a kind of experiment and the desired result from the experiment alternative hypothesis can not converted! 'S decision to vote for a particular candidate such imputation may be classified according to the most frequently occurring is! What is the mode refers to the most common statistical terms this question, we conclude. We are interested in testing H 1 rather than H 0 of scores 0.79 to be from! Than qualitative research, as the average difference between observed and expected counts across all.... Often simply called the variance of a school may be expected by pure chance called! ( or percentage ) of all combinations of two or more nominal or categorical variables to Save Money that Work. Results of several studies into quantitative data refers to statistical analysis different form than the format used for research. A statistic analysis — hypothesis tests — focuses more on research than practical business application or variables! Is to either support or not support each hypothesis spreadsheet and each measurement item can be meaningfully.! Transcripts, can not be tested directly and qualitative research this dataset are (. Moment correlations no shortage of application for this analysis process are interval estimates and hypothesis,. From a larger population set male and female students Actually Work tools due to weight... Quantitatively using statistical tools available have a null hypothesis and an alternative hypothesis data. Difference between the highest confidence level possible in order to make accurate inferences 0.44...

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