Absolutely. SPSS doesn't care how you collect data; it just analyzes that data that you input. Likert scale data is usually treated as continuous, although this practice is not without some controversy from more conservative researchers.
Any kind of data can be collected.
Data that is collected may have been collected previously for some reason, or it might have been collected recently. Data is usually collected to show statistics or information about something specific.
Line graphs are often more clear to analyze, and are used for continuous data. bar graphs are used for just a certain amount of results, ones that don't continue.
The collected data is organized in a fashion so you can determine if the hypothesis is supported.
The definition of statistics is the science of conducting studies to collect, organize, summarize, analyze and draw conclusions from data.
The next step in the scientific method is to analyze the data you collected.
it depends what you researching? what are your hypotheses and how are you going to treat your variables (ordinal, continous)? what scale are u using? 3, 5, 7 or more?in one case the analysis is a bit limited on the other hand there are many choices like Pearsons linear Gronbachs alpha and so on
Analyzing data helps scientists explain their observations and their explanations are based on the evidence they collected.
That depends upon whom you ask, as there is some degree of controversy around Likert scales. Many people, myself included, would consider it interval data, and it is usually interpreted that way. However, there is another school of thought that says that Likert data is ordinal at best. Both sides of the debate have valid points, and this question hasn't been settled.
Scientists also design and perform the experiments in which the data was collected. They also analyze the data and try to explain them.
Thermometer, Spring Scale, and a Balance Scale ;-) Math Homework... Suckie! :P
If you are considering a single ordinal variable, determining the mode or median would be meaningful, but the mean or SD would not. Many researchers do consider likert-scale data to possess only ordinal qualities. However, leading research studies, for example in the marketing area, obtain measures such as means and standard deviations from likert-scale data. Indeed leading textbooks also follow this approach. One concern has been that the 'distances' between points on a likert scale are not equal, for example the 'distance' or 'difference' between a strongly disagree and disagree is not the same as the difference between disagree and neutral. A recent study discusses these issues, as well as demonstrating that data obtained from 5-point, 7-point and 10-point likert scales are approximately comparable in terms of mean score (once re-scaled) and various measures of variation and data shape. The study reference is Dawes, John "Do Data Characteristics Change According to the Number of Scale Points Used ? An Experiment using 5-point, 7-point and 10-point Scales" International Journal of Market Research, Vol 50 2008.
Yes, you can analyze collected data on how colors affect human behavior. Example: In the United States a jail was painted pink and they found the pink seemed to soothe the prisoners much more than if the colors were bright or dull colors such as gray.
Take some time to carefully review all of the data you have collected from your experiment. Use charts and graphs to help you analyze the data and patterns.
Many researchers believe you should only report the results for individual likert items using the proportion of responses for each scale point. For example, 17% strongly agreed, 32% agreed, 10% neither agreed or disagreed and so on. The reason they say this is that likert data is not "equal interval" - the difference between strongly agree and agree is not the same as the difference between neutral and agree, for example. The data is said to be ordinal, not metric. However, this is actually not so much of an issue. Several research studies show have calculated the numerical difference between Likert-type scale points and showed they are very, very close to "equal interval". References for this are given in this paper: Dawes, John. "Do Data Characteristics Change According to the Number of Scale Points Used - an Experiment using 5-point, 7-point and 10-point scales", International Journal of Market Research Vol 50, no 1, 2008. In fact the data used for this experiment is available on the web, go to www.johndawes.com.au and click on "free data".
A design of experiment is a multi-step process that requires you plan, collect data and then analyze the data you have collected to achieve the result you desire.
For the most part, they use math related to statistics. They use it to interpret their data, and to determine trends and significance of data points collected in an experiment