Probability sampling means that every member of the target population has a known chance of being included in the sample. Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling. In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included. Common non-probability sampling methods include convenience sampling, voluntary response sampling, purposive sampling, snowball sampling, and quota sampling.
Determining cause and effect is one of the most important parts of scientific research. You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time.
It must be either the cause or the effect, not both! Yes, but including more than one of either type requires multiple research questions. For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more.
Each of these is its own dependent variable with its own research question. You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined.
Each of these is a separate independent variable. To ensure the internal validity of an experiment , you should only change one independent variable at a time. To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.
A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect. A confounding variable is a third variable that influences both the independent and dependent variables. Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.
There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization. In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.
In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable. In statistical control , you include potential confounders as variables in your regression.
In randomization , you randomly assign the treatment or independent variable in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables. Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.
However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. Operationalization means turning abstract conceptual ideas into measurable observations. Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics.
It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.
There are five common approaches to qualitative research :. There are various approaches to qualitative data analysis , but they all share five steps in common:. The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis.
In scientific research, concepts are the abstract ideas or phenomena that are being studied e. Variables are properties or characteristics of the concept e. The process of turning abstract concepts into measurable variables and indicators is called operationalization. A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined. To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.
An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways. A true experiment a. However, some experiments use a within-subjects design to test treatments without a control group.
Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment. If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.
A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned. Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment.
Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity as they can use real-world interventions instead of artificial laboratory settings. Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population.
Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset. The American Community Survey is an example of simple random sampling. In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3. If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity.
However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,. If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling. Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample. There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering.
In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample. Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.
However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole. In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share e.
Once divided, each subgroup is randomly sampled using another probability sampling method. Using stratified sampling will allow you to obtain more precise with lower variance statistical estimates of whatever you are trying to measure. For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race.
Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions. Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups. Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval — for example, by selecting every 15th person on a list of the population.
If the population is in a random order, this can imitate the benefits of simple random sampling. There are three key steps in systematic sampling :. A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.
A confounder is a third variable that affects variables of interest and makes them seem related when they are not.
In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related. Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world.
They are important to consider when studying complex correlational or causal relationships. Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds. Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity.
Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs. In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.
In contrast, random assignment is a way of sorting the sample into control and experimental groups. Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study. Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.
Random assignment is used in experiments with a between-groups or independent measures design. Random assignment helps ensure that the groups are comparable. In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic. In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.
In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables a factorial design. In a mixed factorial design, one variable is altered between subjects and another is altered within subjects. While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design.
Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful. In a factorial design, multiple independent variables are tested.
If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions. A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.
There are 4 main types of extraneous variables :. By knowing the different levels of data measurement, researchers are able to choose the best method for statistical analysis. The different levels of data measurement are: nominal, ordinal, interval, and ratio scales. The nominal scale is a scale of measurement that is used for identification purposes.
It is the coldest and weakest level of data measurement among the four. Sometimes known as categorical scale, it assigns numbers to attributes for easy identity.
These numbers are however not qualitative in nature and only act as labels. The only statistical analysis that can be performed on a nominal scale is the percentage or frequency count.
It can be analyzed graphically using a bar chart and pie chart. In the example below, the measurement of the popularity of a political party is measured on a nominal scale. Which political party are you affiliated with? Labeling Independent as "1", Republican as "2" and Democrat as "3" does not in any way mean any of the attributes are better than the other.
They are just used as an identity for easy data analysis. Ordinal Scale involves the ranking or ordering of the attributes depending on the variable being scaled. The items in this scale are classified according to the degree of occurrence of the variable in question.
The attributes on an ordinal scale are usually arranged in ascending or descending order. It measures the degree of occurrence of the variable. Ordinal scale can be used in market research, advertising, and customer satisfaction surveys. It uses qualifiers like very, highly, more, less, etc. We can perform statistical analysis like median and mode using the ordinal scale, but not mean. However, there are other statistical alternatives to mean that can be measured using the ordinal scale.
For example: A software company may need to ask its users:. The attributes in this example are listed in descending order. The interval scale of data measurement is a scale in which the levels are ordered and each numerically equal distances on the scale have equal interval difference. If it is an extension of the ordinal scale, with the main difference being the existence of equal intervals. With an interval scale, you not only know that a given attribute A is bigger than another attribute B, but also the extent at which A is larger than B.
Also, unlike ordinal and nominal scale, arithmetic operations can be performed on an interval scale.
It is used in various sectors like in education, medicine, engineering, etc. Some of these uses include calculating a student's CGPA, measuring a patient's temperature, etc. A common example is measuring temperature on the Fahrenheit scale.
It can be used in calculating mean, median, mode, range, and standard deviation. Ratio Scale is the peak level of data measurement. It is an extension of the interval scale, therefore satisfying the four characteristics of the measurement scale; identity, magnitude, equal interval, and the absolute zero property.
This level of data measurement allows the researcher to compare both the differences and the relative magnitude of numbers. Some examples of ratio scales include length, weight, time, etc. With respect to market research, the common ratio scale examples are price, number of customers, competitors, etc. It is extensively used in marketing, advertising, and business sales. The ratio scale of data measurement is compatible with all statistical analysis methods like the measures of central tendency mean, median, mode, etc.
For example: A survey that collects the weights of the respondents. Which of the following category do you fall in? Formplus is the best tool for collecting nominal, ordinal, interval and ratio data. It is an easy to use form builder that allows you to collect data with ease. Follow the following steps to collect data on Formplus. We will be using the radio choice multiple-choice questions to collect data on Formplus form builder.
Note : The interval data options do not have a zero value. Note: that the ratio data example has a zero value, which differentiates it from the interval scale.
There are two main types of measurement scales, namely; comparative scales and non-comparative scales. In comparative scaling, respondents are asked to make a comparison between one object and the other. When used in market research, customers are asked to evaluate one product in direct comparison to the others. Paulhus found that more desirable personality characteristics were reported when people were asked to write their names, addresses and telephone numbers on their questionnaire than when they told not to put identifying information on the questionnaire.
However, like all surveys, the validity of the Likert scale attitude measurement can be compromised due to social desirability. This means that individuals may lie to put themselves in a positive light. For example, if a Likert scale was measuring discrimination, who would admit to being racist? McLeod, S. Likert scale. Simply Psychology. Burns, N. Likert scale data and analysis Researchers use surveys regularly to measure and analyze the quality of products or services.
Researchers and auditors generally group collected data into a hierarchy of four fundamental measurement levels — nominal, ordinal, interval, and ratio measurement levels for further analysis : Nominal data: Data in which the answers classified into variables need not necessarily have a quantitative data or order is called nominal data.
Ordinal data: Data in which it is possible to sort or classify the answers, but it is impossible to measure the distance is called ordinal data. Interval data: Aggregate data in which measurements of orders and distances can be made is called interval data.
Ratio data: Ratio data is similar to interval data. Some of the significant points to keep in mind are: Statistical tests: Researchers sometimes treat ordinal data as interval data because they claim that parametric statistical tests are more powerful than nonparametric alternatives.
Moreover, inferences from parametric tests are easy to interpret and provide more information than non-parametric options. To analyze scalar data more appropriately, researchers prefer to consider ordinal data as interval data and concentrate on Likert scales.
Median or range for inspecting data: A universal guideline suggests that the mean and the standard deviation are baseless parameters for detailed statistics when the data are on ordinal scales , just like any parametric analysis based on the normal distribution. The non-parametric test is done based on the appropriate median or range for inspecting data.
Best practices for analyzing the results of Likert scales Because the Likert element data is discrete, ordinal, and limited in scope, there has been a long dispute over the most logical way to analyze Likert data.
The advantages and disadvantages of each type of analysis are generally described as the following: Parametric tests assume a regular and uninterrupted division. Non-parametric tests do not assume a regular or uninterrupted division. However, there are concerns about a lesser ability to detect a difference when one exists. Over the years, a series of studies that have tried to answer this question. However, they have been inclined to look at a limited number of potential distributions for Likert data, which causes the generalization of the results to suffer.
Thanks to increases in computing power, simulation studies can now thoroughly evaluate a wide range of distributions. The researchers identified a diverse set of 14 distributions that are representative of the actual Likert data.
The computer program extracted self-sufficient pairs of samples to test all possible combinations of the 14 distributions. In total, 10, random samples were generated for each of the 98 distribution combinations. The study also evaluated different sample sizes. The results show that the Type I error rates false positive for all pairs of distributions are very close to the target quantities.
If an organization uses any of the analysis and results are statistically significant, it does not need to be too worried about a false positive. The results also show that for most pairs of distributions, the difference between the power of the two tests is trivial. If there is a difference at the population level, any of the analysis is equally likely to detect it. There are some pairs of specific distributions where there is a power difference between the two tests.
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