When conducting research, analysts attempt to measure cause and effect to draw conclusions among variables. For example, in order to test whether a drug can reduce appetite, researchers give participants a dose of the drug before each meal. The independent variable (or predictor) is the taking of the drug and appetite is the dependent variable (or outcome). The independent variable is the variable you manipulate in the study. The dependent variable is the variable you measure (appetite, for example).

One group takes the drug before each meal and a control group does not take drugs at all. After several days, the researchers note that the drug-takers have reduced their caloric intake voluntarily by 30%. Researchers now know that regular consumption of the drug reduces appetite. This type of study is called a univariate study because it examines the effect of the independent variable (drug use) on a single dependent variable (appetite).

Bivariate studies are different from univariate studies because it allows the researcher to analyze the relationship between two variables (often denoted as X, Y) ins order to test simple hypotheses of association and causality. For example, if you wanted to know whether there is a relationship between the number of students in an engineering classroom (independent variable) and their grades in that subject (dependent variable), you would use bivariate analysis since it measures two elements based on the observation of data.

There are essentially four steps to conducting bivariate analysis as follows:

For example, if you were testing the relationship of class size and grades in an engineering class, then you would report the following: “The data show a relationship between class size and grades. Smaller class sizes (20 or less students) have a grade point average of 4,4 whereas larger class sizes (21-100 students) have a grade point average of 3,1. This demonstrates that students in smaller classes earn grades that are 30% higher than those in large classes.”

In order to determine the type and direction of the relationship you must determine which of the four levels of measurement you will use for your data:

- Nominal, which is non-numerical and places an object within a category (ex. male or female)
- Ordinal, which ranks data from lowest to highest, 3) interval, which indicates the distance of one object to the next and
- Ratio, which contains all of the above, but also has an absolute zero point. In the example above, the variable number of students is ordinal and the grade point average is also ordinal, so it is a correlative relationship.

Correlation describes the relationship or degree of association that exists between variables. We can conclude that small class size has had a positive effect on grades. The decrease in number of students in a class attributed to an increase in grades. This is a negative correlation. If an increase in number of students led to an increase in grades, then that would have been a positive correlation.

Statistical significance is used to determine whether the results are significant enough to truly make a connection. In other words, do we think the results occurred by chance, or do we truly expect to see the same results with another similar study population? In many types of studies, a relationship is considered significant (the association seen in this sample is not occurring randomly or by chance) if it has a significance level of .05. This means that in only 5/100 times will the pattern of observations for these two variables that we have measured occur by chance.

To determine whether a bivariate correlation is significant researchers choose a standard formula depending upon the type of data used. For example, Pearson's correlation coefficient measures the strength of linear relationship between X and Y. The relationship between two ordinal variables can be measured by using a formula entitled Spearman’s rho. Spearman’s rho calculates a correlation coefficient on rankings rather than on the actual data. In our example, we looked at how smaller class sizes led to higher grade point averages. Both the number of students in a class and the grades can be ranked.

Spearman’s rho will vary between –1 and +1, with –1 being a perfect negative correlation (if you rank high on X, you will rank low on Y), +1 being a perfect positive correlation (if you rank high on X, you will rank high on Y), and 0 being no relationship between the two (rank on X tells us nothing about rank on Y).

There are several other formulas that can be used to measure significance based on type of data used including Kendall’s Tau, Kendall’s Tau-B, Tau-c, Goodman-Kruskal Gamma, Chi-square(x2), Lambda A, Mann Whitney U-test, Wilcoxon Signed-Rank Test.

Multivariate studies are similar to bivariate studies, but multivariate studies have more than one dependent variable. For example, if an advertiser wanted to examine the effectiveness of three different banner ads on a popular website, the advertiser could measure the ads click rate for both men and women. Researchers could then use multivariate statistical analysis to examine the relationships between all of the variables.

Multivariate analytical techniques represent a variety of mathematical models used to measure and quantify outcomes, taking into account important factors that can influence this relationship. There are several multivariate analytical techniques that one can use to examine the relationship among variables. The most popular is multiple regression analysis which helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. Other techniques include factor analysis, path analysis and multiple analyses of variance (MANOVA).

When conducting research, analysts will choose among the univariate, bivariate or multivariate analytical techniques based on their particular study purpose and proposed hypothesis. Each method has its own advantages and uses specific statistical tools to draw conclusions and identify relationships between variables.

The primary benefit of multivariate and bivariate techniques is that it gives researchers a vital tool to examine relationships between variables and to quantify the relationship between those variables. For example researchers can study the impact of a new drug on depressed patients. If the results show that 70% of the patients who take that drug feel happier, then researchers can conclude that the relationship between the two variables (drug and depression) exist and they can quantify that relationship by alluding to the fact that 70% felt better.

Via the use of multivariate techniques researchers can introduce a variety of other variables and manipulate the association between those variables to understand the connection between independent and dependent variables. For example, in the above study, researchers can introduce the variable of 60-minutes of exercise, three-days-a week to a control group. This way they can measure the impact of exercise vs. the impact of taking the drug.

Another benefit of multivariate techniques is that researchers can also manipulate the conditions under which the association takes place. For example, if the subjects exercise in the morning, does this have a greater impact on their overall well-being as opposed to exercising in the evening?

Some people might say that there are also disadvantages to multivariate techniques in that the process can be complex and involve high-level mathematics capabilities. For the results to be truly significant, the number of subjects in a study should be quite large so that the results would expected to repeat themselves if administered to another sample group.

Despite these minor disadvantages, it is clear that multivariate techniques deliver great benefits to scientific studies. Multivariate techniques give researchers a much broader and more accurate picture than looking at just one variable.

When conducting research, analysts attempt to measure cause and effect to draw conclusions among variables. For example, in order to test whether a TV food commercial can increase caloric intake in women, researchers have participants watch a TV food commercial each evening before they go to sleep. The independent variable (or predictor) is the watching of the TV food commercial and caloric intake is the dependent variable (or outcome). The independent variable is the variable one manipulates in the study. The dependent variable is the variable one measures (participant-recorded caloric intake, for example).

One group watches the TV food commercial each evening and a control group does not. After a week, researchers discover that those who have watched the TV food commercial have increased their caloric intake by 35% over the group that did not see the commercial. Researchers now know that regular viewing of this particular TV food commercial increases caloric intake. This type of study is called a univariate study because it examines the effect of the independent variable (drug use) on a single dependent variable (appetite).

Bivariate studies are different from univariate studies because it allows the researcher to analyze the relationship between two variables (often denoted as X, Y) in order to test simple hypotheses of association and causality. For example, if we take the example above one step further, we could evaluate the relationship between the number of times the women watched the TV food commercial (independent variable) and their caloric intake over the week (dependent variable). You could use bivariate analysis in this example since it measures two elements based on the observation of data.

Multivariate studies are similar to bivariate studies, but multivariate studies have more than one dependent variable. For example, if in the above scenario one wanted to examine the effectiveness of three complete different TV food commercials on two groups of women (ages 18-30 and 31-55), the advertiser could measure the three ads effectiveness on the two populations. Researchers could then use multivariate statistical analysis to examine the relationships between all of the variables.

When conducting research, analysts can choose among the univariate, bivariate or multivariate analytical techniques based on their particular study purpose and proposed hypothesis. Each method has its own advantages and uses specific statistical tools to draw conclusions and identify relationships between variables.

Researchers use bivariate and multivariate analysis whenever a study requires the examination of the relationship among multiple variables at the same time. Through the use of statistical methods, researchers seek to quantify the relationship in order to predict likely outcomes. Bivariate analysis focuses on two variables, whereas multivariate analysis focuses on more than one dependent variable (also known as the outcome), more than one independent variable (also known as predictor) or both.

Researchers use multivariate and bivariate analysis when they want to predict or explain outcomes between variables. For example, if a university wants to predict the success of their future students based on their high school average (GPA) and SAT scores, they could create independent variables (High School GPA and SAT scores) and dependent variables (First Semester College GPA and College Satisfaction Rating). In this case we have two independent variables (predictors) and two dependent variables (outcomes).

Regression analysis is a statistical method that is used to quantify this relationship. Regression research involves multiple inferential statistical tests. In order to develop a regression equation, you compile scores for both variables from a group of similar individuals. If we use the example above, we can include all the subjects’ GPAs in High School and their SAT scores, as well as their first semester GPA at college and their self-rated satisfaction score.

Regression statistics are used to create an equation that can determine whether good High School GPAs and good SAT scores can predict satisfaction in college and a good first semester GPA. Regression calculates a coefficient for each independent variable to estimate the effect of each predictor on the dependent variable. Furthermore, it calculates its statistical significance which tells the researchers how likely these results would be duplicated if the study was performed on a similar population under the same circumstances. In other words, by understanding and quantifying the relationships between the predictors and the outcome, researchers can determine what types of students will succeed best at their university. This effects their admission criteria for years to come.

Regression procedures can also be used to explain why individuals are different on some particular variable. For example, a researcher may want to know why students from lower income families score lower on SAT scores. The research may hypothesize that the reasons for the differences could be parent’s level of education, school attendance, quality of education in lower income areas, etc. To test the hypothesis, researchers can use regression analysis on this set of data.

As you can see, the use of multivariate and bivariate analysis is useful in predicting outcomes. As a result it is used regularly in a variety of fields such as economics, social sciences, medicine, psychology and even advertising.

Multivariate and bivariate analysis is extremely useful for researchers because it can help measure cause and effect among variables and draw conclusions among those variables. Multivariate and bivariate analyses are terms used to describe how many variables are being analyzed. Multivariate means more than two variables are being examined and bivariate means only two variables are being analyzed. Univariate means that just one variable is being examined.

For example, in order to test whether listening to high-energy music can enhance an athlete’s running time, researchers might play the music right before the athlete’s competition. The independent variable (or predictor) is listening to the music and their running time under the competition is the dependent variable (or outcome). The independent variable (music) is the variable you manipulate in the study. The dependent variable (running time) is the variable you measure.

To test the music theory on runners, there should be a preliminary test of all subjects to record their average running time under a race. Once these are recorded, the researchers form two separate groups, one which listens to music before the race and the second group would consist of athletes who don’t listen to the music at all. After the competitions, the researchers could note that 70% of the athletes that heard the music had increased their running times, whereas only 30% of the non-music group increased their running times. Researchers now know that listening to high-energy positive music can increase running times for athletes.

As you can see, multivariate and bivariate analysis is critical in determining cause and effect and relationships between variables. These types of studies are represented in a variety of fields such as psychology, sociology, medicine and even advertising.

Market research, which includes social and opinion research, as well as information about the size and needs of the marketplace, provides information for companies to make smart business decisions. Market research surveys include qualitative market research such as focus groups, individual interviews and observations and it can include quantitative market research involving online surveys, face-to-face interviews, telephone interviews, longitudinal studies and systemic behavioral observations.

The results collected from market research surveys are recorded and analyzed using bivariate and multivariate techniques. Multivariate and bivariate analysis allows researchers to examine the relationships between variables and to quantify the relationship between those variables.

Via the use of multivariate techniques researchers can introduce a variety of other variables and manipulate the association between those variables to understand the connection between independent and dependent variables. Another benefit of multivariate techniques is that researchers can also manipulate the conditions under which the association takes place. It is a quick and cost-effective statistical method.

For example, the company can analyze the information they receive from their target market and cross reference it with their target markets’ behavior. This way they can see whether certain independent values (such as attitude about a competitor) will impact dependent variables (such as trying out a new product on the market).

Any company that wants to succeed in this global marketplace will focus on obtaining and analyzing market research surveys using the statistical methods of bivariate and multivariate analysis to gain a competitive advantage over its peers.

Competitive companies are always looking to improve their profits by reacting faster to customer needs and by offering better products and services. A company’s brand, or reputation, is built upon whether it meets those customer needs and wants. As a result companies spend quite a bit of money and resources to promote, enhance and monitor its brand loyalty against competitors.

Multivariate and bivariate analysis is an array of advanced statistical tests used to observe many variables or perceptions that interact with one another. The decision to be a loyal customer at an establishment usually involves a variety of factors. People cling to a brand due to a variety of perceptions such as price, image, product quality, environment, staff attitude, and even proximity could all affect brand loyalty.

Researchers will use multivariate analysis methods to investigate the large amount of data to create actionable information and hence improve a retailer’s decision-making about what factors most drive brand loyalty.

Here are three ways that Multivariate and Bivariate Analysis can be used in Brand Research

**Advertising:**McDonald’s can use multivariate analysis to find out which advertisements were the most effective in getting customers to come in and use their coupon. Three advertisement coupons could be sent via direct mail to 10,000 local residents. Multivariate analysis could then provide information about which of the three advertisement coupons were the most effective in drawing a particular segment of that market into the restaurant.**Store Design:**Multivariate analysis could also measure the impact of three different store signs in a huge department store. After a week, researchers can see which purchases increased in relation to the three new store signs.**Price:**Multivariate analysis could also measure the impact of three different price tags for the Big Mac in three different stores. After a week, researchers can see which purchases increased in relation to the price increase, decrease or unchanged.

In summary, multivariate and bivariate analysis can be used in brand research to understand how customer perceptions affect brand loyalty and purchase behavior. Zeroing in on what influences a customer to visit and spend, enables retailers to craft their company image so that they can attract more people to their establishment.

Business market research is the process of gathering relevant data that will help your business understand its market and capitalize on that information. Business market research typically involves answering the following questions. Who makes up my target market? What does my target market want? When do they need it? How do they usually go about fulfilling that need? What does my target market think of my competitors?

To conduct business market research, you might want to send out a survey to 10,000 prospects in your area and ask them a series of questions to get insight into their needs and behaviors. Once you get back all the answers, you will have quite a lot of data to analyze. Rresearchers will therefore often use bivariate and multivariate analysis to examine the relationship among multiple variables at the same time. Through the use of statistical methods, researchers can quantify the relationship in order to predict likely outcomes. Bivariate analysis focuses on two variables, whereas multivariate analysis focuses on more than one dependent variable (also known as the outcome), more than one independent variable (also known as predictor) or both.

Let’s say in this scenario you want to isolate two independent variables (age, sex) to see if customers will purchase your product (outcome). You could see which sex and age had recently purchased your competitor’s similar product. Then you could perhaps draw the conclusion based on the results that 75% of females aged 25-35 purchased the competitor’s product, so marketing to this segment might result in a high purchase rate for you.

As you can see, the use of bivariate and multivariate analysis to examine the relationship among multiple variables in business market research is cost-effective and efficient way to form predictors of behavior. The significant of the results will determine whether the results will be duplicated if conducted again with a similar population.

Data analysis techniques include bivariate analysis (two variables), and multivariate analysis (three or more variables). Multivariate statistical analysis can be divided into two main strategies:

**Exploratory or descriptive**Exploratory or descriptive analysis involves uncovering patterns and exploring relationships among variables to recognize and solve problems.**Confirmatory or inferential**Researchers tests statistical hypotheses by using statistical models to see whether they can identify predictors and outcomes for their hypotheses.

For example, if you were testing the relationship between the number of city after-school programs and the number of juvenile arrests in a given year, then you would create a hypothesis, such as: “We believe that the existence of one or more after-school programs in a city will lead to reduced juvenile arrests than cities that have no such programs. Perhaps after the research is conducted, the hypothesis will be confirmed. “Cities that have 1 or more after-school centers had 50% less juvenile arrests than cities without such a program.”

In order to determine the type and direction of the relationship you must determine which of the four levels of measurement you will use for your data: 1) nominal, which is non-numerical and places an object within a category (ex. male or female), 2) ordinal, which ranks data from lowest to highest, 3) interval, which indicates the distance of one object to the next and 4) ratio, which contains all of the above, but also has an absolute zero point.

In the example above, the number of centers is ordinal and the number of juvenile arrests is also ordinal, so it is a correlative relationship. We can conclude that having an after-school center has had a positive effect on juveniles. This is a positive correlation. If the number of arrests went up when the number of centers went down, then the direction of the relationship would have been a negative correlation.

Statistical significance is used to determine whether the results are significant enough to truly make a connection. In other words, do we think that if we built after-school centers in cities that don’t have any yet, that the number of juvenile arrests would go down? A relationship is considered statistically significant (the association seen in this sample is not occurring randomly or by chance) if it has a significance level of .05. This means that in only 5/100 times will the pattern of observations for these two variables that we have measured occur by chance.

For common multivariate procedures, one or more of the following statistical models will typically be conducted: multiple linear regression, factor analysis, logistic regression, and loglinear modeling. To determine whether a correlation is significant researchers choose a standard formula depending upon the type of data used. For example, Pearson's correlation coefficient measures the strength of linear relationship between X and Y. The relationship between two ordinal variables can be measured by using a formula entitled Spearman’s rho. Spearman’s rho calculates a correlation coefficient on rankings rather than on the actual data.

The data analysis that one conducts to explain correlations or to predict outcomes can be either extremely simple or extremely complex in terms of the number and nature of the parameters estimated. They are, however, much easier to perform that trying to recreate real-world replications of these processes.

International market research is conducted by investment managers who want a good understanding of investment opportunities and risks in a particular country or region. International analysts will regularly compile macroeconomic data such as GDP, inflation rates, growth rates, and other indicators for each country that they follow. This data will also be compiled with industry and/or sector performance and valuations.

International market research analysts will often use bivariate and multivariate analysis to examine the relationship among several variables. Bivariate analysis focuses on two variables, whereas multivariate analysis focuses on more than one dependent variable (also known as the outcome), more than one independent variable (also known as predictor) or both.

For example, investment analysts may want to see whether historical events can predict future events. By analyzing two independent data sets like growth rate and inflation rate, researchers can see whether they impact the dependent variable of industry-average stock performance. Through the use of statistical methods, researchers can attempt to quantify the relationship in order to predict likely outcomes. For example, they could say that when country growth rates go up and inflation goes down, then there is a 75% chance that the sector’s average stock price will go up at least 10%.

Via the use of multivariate techniques, researchers can introduce a variety of other variables and manipulate the association between those variables to understand the connection between independent and dependent variables. For example, researchers could look at the impact of a presidential election in the region. This way they could measure whether a president’s election in an international country typically impacts average sector price (and in what direction).

The advantage of doing bivariate and multivariate techniques in international investment research is that it is quick and cost-effective, and if the results are significant enough, the data can be used to predict the direction of the stock market.

Online companies are very obsessed with driving traffic to their websites. As a result a huge amount of money is spent on creating good-quality content with relevant keywords to attract customers searching for vital information. The focus on search engine optimization (SEO) may indeed be driving customers to their website, but it may not be helping with conversion (the rate at which clients actually make a purchase). There’s no benefit to having lots of people visit your site and then leave without buying anything.

As a result, smart companies are using bivariate and multivariate analysis to research their customer’s online experience and capitalize on that research. For instance, if a company knows that customers only stay on their landing page for one second before abandoning, then there is clear evidence that the landing page is not delivering what the customer wants. As a result, the company can conduct multivariate research to explore what types of landing pages are most effective in getting customers to 1) stay longer on that page and 2) move them to a purchase decision.

Rather than just create a new landing page at random, the company could test three mock landing pages with three different designs. They could send an email to 10,000 of their customers encouraging them to click a link to learn more about a new product. One third would be randomly sent to mock landing page #1, one third to mock landing page #2 and one third to mock landing page #3.

The results of this analysis via multivariate analysis could show which of the three landing pages had the best conversion rates (more products purchased), plus the research could show which groups of people were the most active. For example, was it middle aged women who made the purchase or young and single men? This information helps with future targeting.

Smart companies will regularly use bivariate and multivariate analysis methods and online research to improve a retailer’s decision-making about what factors most drive purchase decisions. Zeroing in on what influences a customer to visit a website and buy, enables retailers to craft their online offers more effectively.

Primary research is proprietary, new research that can be collected in either quantitative or qualitative format. Quantitative research is numerically-based, such as online surveys and telephone surveys. Qualitative research is non-numerical and focuses on getting the consumer’s feelings and opinions. This may include focus groups or in-depth interviews with participants.

Since compiling and analyzing this collected data can be complex and cumbersome, researchers often turn to bivariate and multivariate analysis methods to examine relationships among multiple variables at the same time. By using statistical methods, researchers seek to quantify relationships to predict likely outcomes. Bivariate analysis focuses on two variables, whereas multivariate analysis focuses on three or more variables.

If a company wanted to see whether men with incomes over $100,000 a year will like product A, they could create two independent variables (sex and income) and a dependent variable (likes product A). Analyst can choose to conduct regression analysis which involves multiple inferential statistical tests. In order to develop a regression equation, you compile the variables which include all the subjects’ sex and income, as well as their liking product A.

Regression calculates a coefficient for each independent variable to estimate the effect of each predictor on the dependent variable. Furthermore, it calculates its statistical significance which tells the researchers how likely these results would be duplicated if the study was performed on a similar population under the same circumstances. By understanding and quantifying the relationships between the predictors and the outcome, researchers can determine what types of people will like product A.

As you can see, the use of multivariate and bivariate analysis is useful in predicting outcomes when analyzing primary research.

Qualitative market research focuses on things you can qualify in words or pictures, instead of numbers. Research methods include focus groups, individual interviews and observations. Even though qualitative market research contains non-numerical data (like opinions), it is still very helpful for analysts to use multivariate and bivariate techniques in qualitative studies. Multivariate and bivariate analysis gives researchers a vital tool to examine relationships between variables and to quantify the relationship between those variables.

For example researchers can study the impact of a new form of self-therapy on depressed patients in which patients are trained via video. If the results show that 75% of the patients who watch the video and perform self-therapy are happier, then researchers can conclude that the relationship between the two variables (the self-therapy program and happiness) exist and they can quantify that relationship by alluding to the fact that 75% of the participants felt better.

The downside to using bivariate or multivariate studies in qualitative market research is that the participant size is typically smaller than in a quantitative study. That’s because in qualitative studies, there is more personalized in-depth questioning and close observation. Qualitative market research is said to be exploratory because one doesn’t have a preconceived vision of what the study will deliver. It is fantastic for providing insight into an individual’s or group’s underlying reasons, opinions, and motivations. It can uncover trends and expose problems.

For example, if 25% did not feel better after using the self-therapy techniques, then the moderator could ask them why. If all of them say that it was difficult to master, then they have uncovered valuable information. If the moderator were to ask them which aspects were most difficult, then that information could be used to perfect the self-therapy program.

Via the use of multivariate techniques researchers can introduce a variety of other variables and manipulate the association between those variables to understand the connection between independent and dependent variables. For example, in the above study, researchers can introduce the variable of a brief 10-minute introductory session with a trainer prior to watching the self-therapy video. This way they could measure whether the added assistance increased the number of participant who reported to be happier.

Another benefit of multivariate techniques in qualitative market research is that researchers can manipulate the conditions under which the association takes place. For example, if the subjects receive training in a relaxing spa setting, does this have a greater impact on their overall well-being as opposed to receiving the training in a white-walled hospital room?

In summary, qualitative research is great for exploratory purposes to get a depth and richness of information not possible with a quantitative study alone. The downside is that there are not so many participants to measure, so a quantitative study may be needed to confirm results.

Quantitative market research is based on numerical analysis and statistics. Quantitative research can be used to quantify attitudes, opinions, behaviors, and other defined variables. Quantitative research is also used to uncover patterns or correlations. As a result, multivariate and bivariate analysis is often used to examine relationships between variables and to quantify the relationship between those variables.

Quantitative market research methods typically involve online surveys, face-to-face interviews, telephone interviews, longitudinal studies and systemic behavioral observations. The data is then recorded and analyzed.

For example, let’s say you wanted to create a quantitative study to see whether women who take prenatal vitamins had a reduced chance of developing a child with autism. You could send out a survey to over 10,000 participants asking them to record what vitamins and health supplements they took before and during pregnancy. Let’s say results showed that women who took prenatal vitamins had a 40% less chance of giving birth to an autistic child. Researchers could then assume a direct correlation between prenatal vitamins and autism. Via the use of multivariate techniques researchers can introduce a variety of other variables and manipulate the association between those variables to understand the connection between independent and dependent variables. For example, researchers could introduce a new variable, such as looking at the impact of taking Omega 3 in tablet form during pregnancy. This way they could measure whether taking Omega 3 during pregnancy and pre-natal vitamins prior to pregnancy further reduced the chances of giving birth to an autistic child.

The advantage of doing bivariate and multivariate techniques in quantitative market research is that it is quick and cost-effective to execute. Quantitative analysis allows researchers to test specific hypotheses, and its statistical nature allows for generalization for the wider population.

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