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Learn everything about Likert Scale with corresponding example for each question and survey demonstrations. When represented in a graph, a normal distribution looks like a bell shape where most of the scores are clustered around the mean and taper towards the ends.
Common parametric inferential statistics include:. In contrast, non-parametric tests do not make the same assumptions about normality as parametric tests.
When graphed, a non-parametric distribution does not look like a bell shape and typically has most of the scores closer to one end of the distribution. To address non-normality, non-parametric tests work by assigning each datapoint ranks and then analysing those.
Common non-parametric inferential statistics include:. Sign Up. Get Started. Sign in Sign in with Facebook. Sign in Sign in with Google. Quantitative Data. In this article What is Quantitative Data? Author: Dr Jessica G. For the former factor, several study designs can be applied to quantitative research, and can include: Type Definition Longitudinal Examination of variables in a group of individuals over the course of a specified time frame, which allows for the quantification of your variables over time to see how they change.
Cross-Sectional Examination of variables in a group of individuals at a specific point in time, which allows for the quantification of your variables at a fixed time point. Case Study In-depth investigations of variables in a small number of individuals. This allows for an in depth and more focused quantification of your variables. This allows you to quantify your variables in their natural state. Experimental The systematic, and tailored, manipulation and measurement of two or more variables to investigate how the change in one variable influences the other s.
Correlational Examination and quantification of a potential relationship between two variables, which demonstrates how one variable might relate to another. How to measure quantitative data How you measure quantitative data depends on your research question and associated variables of interest. Several methods can be used to quantify data, and can include: Type Definition Questionnaires A series of written and structured questions where participants are asked to choose from a series of answers.
The nominal scale of measurement defines the identity property of data. Examples of nominal data include eye colour and country of birth.
Nominal data can be broken down again into three categories:. Nominal without order: Nominal data can also be sub-categorised as nominal without order, such as male and female. The ordinal scale defines data that is placed in a specific order. The interval scale contains properties of nominal and ordered data, but the difference between data points can be quantified.
This type of data shows both the order of the variables and the exact differences between the variables. They can be added to or subtracted from each other, but not multiplied or divided. For example, 40 degrees is not 20 degrees multiplied by two. This scale is also characterised by the fact that the number zero is an existing variable. In the ordinal scale, zero means that the data does not exist.
In the interval scale, zero has meaning — for example, if you measure degrees, zero has a temperature. Data points on the interval scale have the same difference between them. The difference on the scale between 10 and 20 degrees is the same between 20 and 30 degrees. This scale is used to quantify the difference between variables, whereas the other two scales are used to describe qualitative values only.
Other examples of interval scales include the year a car was made or the months of the year. Ratio scales of measurement include properties from all four scales of measurement. The data is nominal and defined by an identity, can be classified in order, contains intervals and can be broken down into exact value. Weight, height and distance are all examples of ratio variables. Data in the ratio scale can be added, subtracted, divided and multiplied. The number zero means that the data has no value point.
An example of this is height or weight, as someone cannot be zero centimetres tall or weigh zero kilos — or be negative centimetres or negative kilos. Examples of the use of this scale are calculating shares or sales.
Of all types of data on the scales of measurement, data scientists can do the most with ratio data points. To summarise, nominal scales are used to label or describe values.
Ordinal scales are used to provide information about the specific order of the data points, mostly seen in the use of satisfaction surveys.
The interval scale is used to understand the order and differences between them. The ratio scales gives more information about identity, order and difference, plus a breakdown of the numerical detail within each data point. Once data scientists have a conclusive data set from their sample, they can start to use the information to draw descriptions and conclusions.
To do this, they can use both descriptive and inferential statistics. Descriptive statistics help demonstrate, represent, analyse and summarise the findings contained in a sample. They present data in an easy-to-understand and presentable form, such as a table or graph.
Without description, the data would be in its raw form with no explanation. One way data scientists can describe statistics is using frequency counts , or frequency statistics, which describe the number of times a variable exists in a data set. Other examples include qualifications of education, such as high school diploma, a university degree or doctorate, and categories of marital status, such as single, married or divorced.
Formplus gives you a day free trial to test all features and start collecting quantitative data from online surveys. We will be creating a sample quantitative data collection form that inputs student's scores First test score and final test score , then output the total grade in percentage.
You can also customise your forms, share to respondents and view response analytics. Quantitative data is perhaps the most widely used data type in research. This is partly due to its ease of computation and compatibility with most statistical analysis methods. In quantitative data analysis, we take a sample population, classify its features, and even construct more complex statistical models in an attempt to explain what is observed. Our findings may be extended to a larger population, and comparisons may be made between two sample populations.
Quantitative data is collected through a standard procedure, making it easy for researchers to replicate past research or build on current ones. Data interpretation and analysis are fast becoming more valuable with the prominence of digital communication, which is responsible for a Researchers often have issues choosing which research method to go with: quantitative or qualitative research methods?
Many incorrectly Data types are an important aspect of statistical analysis, which needs to be understood to correctly apply statistical methods to your Research and statistics are two important things that are not mutually exclusive as they go hand in hand in most cases. The role of Pricing Templates Features Login Sign up. What is Quantitative Data? Discrete Data Discrete data is a type of data that consists of counting numbers only, and as such cannot be measured.
Continuous Data Continuous data is a data type that takes on numeric value s that can be meaningfully broken down into smaller units. What are Quantitative Data Examples? Census The Government carry out census to acquire and record information about the members of a given population. Annual income When setting the selling price of a product, businesses use quantitative data of the annual income of a person or household to determine their purchasing power.
Counter Many online businesses use this to determine the number of website visits they get daily, number of product downloads on the app store, the number of users etc.
Customer Satisfaction Survey This is a case of quantification of qualitative entities used by businesses to improve their customer service. Collect Quantitative Data with Online Surveys [ Sign Up] Quantitative Data Examples in Statistics Mean height in a class The mean height of the students in a class will be calculated by recording the height of each student, adding it up and dividing it by the number of students in the class. Measurement of physical objects It may be used to record the length or width of an object.
Number generator A computer program may be written to generate a particular set of numbers with a uniform 0,1 distribution. Probability The probability of an event occurring is calculated using the quantitative data of the ratio between the ways of achieving success and the total number of outcomes.
Characteristics of Quantitative Data Types Quantitative data is of two types, namely; discrete and continuous data. Numerical Representation Quantitative data takes up numeric values with numeric properties. Order There is a scale or order to quantitative data. Arithmetic Operation One can perform arithmetic operations like addition and subtraction on quantitative data.
Standardised Scale Quantitative data has a standardised measurement scale. Analysis Quantitative data can be analysed using descriptive and inferential statistical methods, depending on the aim of the research.
Data Visualisation Some of the data visualisation techniques adopted by quantitative data include; scatter plot, dot plot, stacked dot plot, histograms, etc. There are four steps of data preparation: Step 1: Data Validation This is done so as to find out whether the data collection was done without any bias. The process includes: Testing for fraud by checking whether all the respondents were truly interviewed or not.
Screening the respondents to know whether they really met the research criteria. Checking whether the right procedure was followed. Checking whether the investigation is complete. Step 2: Data Editing Large data sets may inevitably include errors, and that is why they need to be edited.
Step 3: Coding and Data Entry This is the process of quantifying qualitative data for easy analysis. Step 4: Data Transformation This is the process of changing data into new format. Descriptive Statistical Method Researchers make use of descriptive statistics to summarise quantitative data. Some common descriptive statistical methods include: Mean: the average of a set of numbers. Median: the middle number of a set of numbers. Mode: most occuring number from a set of numbers.
Range: the difference between the highest and lowest numbers from a set of numbers. Inferential Statistical Method This method measures the relationship similarities and differences between multiple variables to generate results and infer conclusions.
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