How to analyze questionnaire data? It must pass through various stages, ranging from data entry into the computer processing through SPSS or Ms. Excel, testing the validity and reliability, descriptive analysis and hypothesis testing. Here is the stages:
1. Validity and Reliability
What distinguishing between questionnaire data processing method with secondary data are validity. When we conducted the study with a questionnaire, so we need to test the validity and reliability of the questionnaire. Why need to do? because the questionnaire was arranged by researcher, meanwhile answering the questionnaire is respondent. The purpose is to minimize interpretation gap between researcher and respondent.
Moreover, Good questionnaire should be well understand by respondents as good as the questionnaire maker. A Questionnaire should has high level of consistency over times.
Otherwise, in secondary data, we do not need to test the validity and reliability.
2. Entry Data
Furthermore, After the questionnaires collected, it needs to input the data into a computer. The most common software for data entry is excel. Surely, spread sheet Excel are familiar among us. How to arrange the data in spread sheet. Stacking down in the spread sheet is the respondents. Meanwhile, the column fill by the item number or questionnaire answer. Likewise, Input data into SPSS is similar with spreadsheet Excel. The data arranges on row as respondent and column as question.
For closed questions, we can give score for each answer option in your question. For example, the answer: strongly agree = 5, agree = 4, neutral = 3, disagree = 2, and strongly disagree = 1. Only the score input into spread sheet.
In certain conditions, negative questions are possible? In such conditions, reversely the score of 5 changes to 1, 4 to 2 and so on.
3. Descriptive analysis
To present the questionnaire results, researcher need to process the data using descriptive analysis. What type of graph is suitable for secondary data? Frequency distribution format is a common to present in descriptive. The display is presented how the number of respondents who answered agree, how that answer did not agree and so on.
In descriptive statistics, common measurement need to provide such as: mean, median, mode and standard deviation. However, when we provide ordinal data as mean and standard deviation, in fact we’re treating these data into numeric data.
4. Hypothesis testing to analyze questionnaire data
Is a questionnaire research able to test a hypothesis? The answer is sure. Actually, Likert scale questionnaire data is ordinal data. It is most appropriate statistical technique is non-parametric techniques. However, due to limitations of statistical tools in non-parametric analysis, somehow data transformation is applied to transform ordinal data into a numerical scale. Even though, transformation method is not a must, as long as the data distribution is normal, then statistical parametric methods can apply.
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Statistical analysis for a quantitative study is often perceived to be the most difficult step by a novice researcher. On the other hand, some researchers tend to over-analyse their research data in search of the illusive “significant” p-value. Some of these problems and pitfalls can be reduced if the researchers give some thoughts to their research objectives.1
Another issue that trouble the novice is how much statistical knowledge one needs to have. There is no straight answer to this question; we feel that the information provided in this article is probably the bare minimum needed by most, if not all, researchers embarking on a research project. What about performing your own statistical analysis using statistical software? Although ability to handle statistical software is desirable, it is not mandatory as it is now possible to outsource to people who can do this properly. The researcher should, however, be able to tell the statistician what analysis is needed and to interpret statistical results. Take note that the statistician cannot undo the errors in the data (e.g. inadequate research design, inappropriate definition of research variables, inaccurate measurement during data collection, or data entry errors) – hence great care must be exercised during these earlier steps of research process.
There are several statistical packages available to assist you in data analysis. SPSS software is applied in the following example. You can download a free trial version from www.spss.com (prior registration necessary)
DESCRIPTIVE STATISTICS
We shall start by this example: You have conducted a survey of 160 diabetic patients in your clinic. The mean HbA_{1c} of these patients was 8.9% (SD=2.2, range 5.2-15.7). The gender breakdown is males 44.4%, and females 55.6%. The ethnicity breakdown is Malays 28.1%, Chinese 41.3%, and Indians 30.6%. Other summarised data are given in Table 1 . You may request the data file from the Editor at [email protected]
Table 1:
Characteristics | HbA1c, % (SD) |
---|---|
Gender | |
Male (n=71) | 8.7 (1.9) |
Female (n=89) | 8.9 (2.3) |
Ethnic group | |
Malays (n=45) | 9.6 (2.5) |
Chinese (n=66) | 8.4 (2.0) |
Indians (n=49) | 8.7 (1.9) |
All patients (n=160) | 8.9 (2.2) |
The SPSS commands for obtaining the above statistics for ‘HbA_{1c}’ are as follows:
From the menus choose:
Select the variable ‘hba1c’
The SPSS commands for obtaining the above statistics for ‘gender’ and ‘race’ are as follows:
From the menus choose:
The SPSS commands for obtaining the statistics ‘HbA1c’ breakdown by ‘Gender’ in Table 1 are as follows:
From the menus choose:
Click on the icon ‘Statistics’ and select ‘Number of Cases’, ‘Mean’, and ‘Standard Deviation’
Uncheck these two boxes ‘display cases’ and ‘Limit cases to first 100’
The SPSS commands for obtaining the statistics ‘HbA_{1c}’ breakdown by ‘Race’ in Table 1 are as follows:
From the menus choose:
Select ‘race’ into the ‘Grouping Variable’
Click on the icon ‘Statistics’ and select ‘Number of Cases’, ‘Mean’, and ‘Standard Deviation’
Uncheck these two boxes ‘display cases’ and ‘Limit cases to first 100’
Type of data
The first thing to take note is the type of data (or variables) you have collected.
Categorical data. There are basically two kinds of data in this groups:
Nominal data (named categories), e.g. gender (male/female), ethnicity (Malay, Chinese, Indian), outcome (dead/alive), etc. The nominal data are summarised by percentages.
Ordinal data (ordered categories), e.g. tumour staging (Stage 1, 2, 3, 4), disease severity (mild, moderate, severe), Likert scale (5-point scale, 1-5), etc. The ordinal data are summarised by median value.
Continuous data. Continuous data is sometime referred to as interval data. These data take the form of a range of number, and may or may not have decimals, e.g. age, HbA1c, weight, height, haemoglobin level, etc. The continuous data are summarised by mean and standard deviation (SD).
Another way of looking at the data is defining the dependent and independent variables:
Dependent variable is the variable of interest
Independent variable is the grouping variable
Let us say, you want to find out if HbA1c differ by gender or ethnicity. Then HbA1c is the dependent variable, and gender and ethnicity are independent variables.
Summarised data
We have seen already earlier different type of data are summarised differently. This summary of data, plus a graphical display of the data (e.g. in graph and scatter plot) is a very useful way of having a sense of your data before you embark on formal statistical analysis (the so-called “eye-balling the data”).
Hypothesis testing, sample and population
One of the reasons for conducting the above study is that you have observed that diabetic patients of certain ethnic group appeared to have poorer diabetic control. Rather than stating that “Malay diabetic patients have poor diabetic control”, we should state that “in the population of all diabetic patients, there is no difference in glycaemic control by ethnicity” (this is the so-called Null Hypothesis). By drawing a representative sample of diabetic patients from the population, you then seek to disprove the Null Hypothesis. This process of drawing conclusion on a population from a sample is called inferential statistics.
INFERENTIAL STATISTICS: PARAMETRIC TESTS
t-test
If you want to find out if HbA1c differ by gender, a statistical output can appear as follow: means HbA1c for males and females are 8.7% (SD=1.9) and 8.9% (SD=2.3) respectively, t= − 0.711, df=158, p=0.478. As HbA1c is a continuous variable (and presumably normally distributed), we use t-test for two groups comparison of means (males vs females). Since the p value is more than 0.05 (the conventional cut-off for statistical significance), we can interpret the result as no statistical significant difference or “no real difference in HbA1c in male and female diabetic patients”.
The SPSS commands for obtaining the above t-test statistics are as follows:
The resources on this website have been specifically designed to help you become a proficient secondary data analyst:
- A series of step-by-step video tutorials, in which John MacInnes shows you exactly how to use the IBM SPSS® software to prepare and analyse secondary data. These videos correspond to the data analysis techniques covered in the book, so it’s really helpful to use the book and website together.
- A Microsoft Excel file containing the confidence interval calculator, a tool you can use with SPSS to find the margin of error of sample data.
- SPSS syntax files and datasets, which will enable you to perform the analytical techniques covered in the book and help you to answer the exercises at the end of each chapter.
- Weblinks to all sorts of other useful SPSS learning resources, such as videos on all the main commands available in SPSS, and guides to a range of useful data sources from national data archives and international organisations such as the UN, the World Bank, the OECD, Eurostat and many others.
Just click on the links to the left.
This website may contain links to both internal and external websites. All links included were active at the time the website was launched. SAGE does not operate these external websites and does not necessarily endorse the views expressed within them. SAGE cannot take responsibility for the changing content or nature of linked sites, as these sites are outside of our control and subject to change without our knowledge. If you do find an inactive link to an external website, please try to locate that website by using a search engine. SAGE will endeavour to update inactive or broken links when possible.
How you choose to deal with questions participants decide not to answer will affect the results of your statistical analysis.
It is common to define ’99’ as a missing value, i.e. a respondent has failed to answer a question. Once you have done this you have two options as to how to deal with missing values:
1) No missing values and,
2) Discrete missing values.
Coding Missing Values
No Missing Values
SPSS will default to this option.
When you generate statistics in SPSS the cases that were coded as a non-response will be treated as a valid response , e.g.
Time taken in minutes
Frequency | Percent | Valid Percent | ||
---|---|---|---|---|
Valid | 1-4mins | 1 | 2.2 | 2.2 |
5-9mins | 9 | 20.0 | 20.0 | |
10-14mins | 6 | 13.3 | 13.3 | |
15-19mins | 11 | 24.4 | 24.4 | |
20-24mins | 4 | 8.9 | 8.9 | |
25-29mins | 3 | 6.7 | 6.7 | |
30-34mins | 2 | 4.4 | 4.4 | |
55-59mins | 1 | 2.2 | 2.2 | |
60-65mins | 2 | 4.4 | 4.4 | |
Missing | 6 | 13.3 | 13.3 | |
Total | 45 | 100.0 | 100.0 |
So, here 6 participants did not answer this question, so the total number of participants was not 45, but 39. However, the frequency is still calculated using 45 participants.
If we look at how many participants searched for between 5-9 minutes we can see that 9 people searched for this length of time. If we want to look at that as a percentage of the sample, SPSS has calculated it as 9 ÷ 45 x 100 = 20%.
Compare this to how SPSS calculates this when Missing Values are declared as below.
Discrete Missing Values
If you declare that a value is missing SPSS will omit the values from analysis, presenting you with Valid Percentages, e.g.
Time taken in minutes
Frequency | Percent | Valid Percent | ||
---|---|---|---|---|
Valid | 1-4mins | 1 | 2.2 | 2.6 |
5-9mins | 9 | 20.0 | 23.1 | |
10-14mins | 6 | 13.3 | 15.4 | |
15-19mins | 11 | 24.4 | 28.2 | |
20-24mins | 4 | 8.9 | 10.3 | |
25-29mins | 3 | 6.7 | 7.7 | |
30-34mins | 2 | 4.4 | 5.1 | |
55-59mins | 1 | 2.2 | 2.6 | |
60-65mins | 2 | 4.4 | 5.1 | |
Total | 39 | 86.7 | 100.0 | |
Missing | missing | 6 | 13.3 | |
Total | 45 | 100.0 |
So, 6 participants did not answer this question, so the total number of participants was not 45, but 39. Now the frequency is calculated using 39 participants.
If we look at how many participants searched for between 5-9 minutes we can see that 9 people searched for this length of time. If we want to look at that as a percentage of the sample, SPSS has calculated it as 9 ÷ 39 x 100 = 23.1%
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The course takes you from absolute beginner of SPSS and statistics, all the way to advanced topics that you will be frequently using in your research projects
Data entry, data importing and preparation
Summarizing data using descriptive statistics
Exploring relationships between different types of variables
Choosing appropriate charts and developing them
Transforming variables and managing the data to suit your analyses
Choosing the appropriate inferential tests such as chi-square, t-tests and regression and running them
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Best Way to Analyze Quantitative Data Using Excel
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Guidance on How to Analyze Data Using SPSS Software
Quantitative data can be meaningless to the readers if you fail to analyze it properly. In current times, analyzing data has become easier. You can analyze quantitative data using an excel sheet or use the statistical package for social sciences (SPSS). Many scholars like analyzing data using excel because it is easier to use it and make a comparison between the available data. However, using Microsoft Excel is time consuming especially if you are analyzing a lot of data. The problem with analyzing data using Microsoft Excel comes in when the data that you are analyzing grows over time.
Load your data in the SPSS software: Scholars can import data from an Excel sheet or enter the data using the variable view tab. You should type the names of your variables and then enter your values for each variable. You should then repeat the procedure for all the variables in your data.
Command SPSS using the different tools: Having made entries in the SPSS, you should then select the data that you want to analyze. You should then click on the data view and select a command, for example, the mean, median, standard deviation, and correlation.
Analyze and interpret the data in the tables: The readers’ will not understand your results if you fail to provide a detailed explanation. The explanation that you provide should not add information that is not on tables. Hire our experts when you are stuck, and you will not get disappointed.
Make conclusions based on your results: People should always use their results to come up with meaningful conclusions. You can also use your results to predict future trends. Contact our experts when you need to analyze data using SPSS, and we will offer the best solutions to your problems.
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Data Science is the most popular and revolutionary technology in the current scenario. The first step in every data science project is to describe, visualize and summarize the data. Those people who examine the features and attributes of data by descriptive statistics always create the best models. Descriptive statistics provide insights and numerical summaries of data that help anyone handle and understand data more efficiently. SPSS is very popular software that most statisticians use to analyze a big data set and run multiple tests. Therefore, beginners generally search for “how to do descriptive statistics on SPSS“. And, this blog will describe the steps to perform descriptive statistics on SPSS. Moreover, you can also hire our experts for SPSS Assignments at an affordable price.
First of all, it is necessary to understand what descriptive statistics and SPSS are.
What is Descriptive Statistics?
Table of Contents
Descriptive statistics are the descriptive coefficients that are used to summarize the given data set. This can be the representation of entire data or a sample of a population. In short, Descriptive statistics describes the features of a particular data by providing short summaries about data.
The measures of the center are the most recognized types of descriptive statistics. These measures, i.e., mean, median, and mode, are used mostly in every level of math and statistics.
Descriptive statistics is mainly used for two purposes.
- Firstly, For providing the basic information of the variables in the dataset.
- Secondly, For highlighting the possible relationships between variables.
Descriptive Statistics can represent difficult to understand insights of large data sets into small size descriptions. For instance, the GPA (Grade Point Average) of a student is the small number representing the overall performance of a student.
What is SPSS Software?
SPSS stands for Statistical Package for Social Science. It is a software package that is very useful in complex statistics data analysis. SPSS Inc. launched SPSS in 1968, which IBM later acquired in 2009.
Due to its English-like commands and simple user manual, it is widely used by marketing organizations, education researchers, government, data miners, and many others for processing and analyzing data. Most of the successful research agencies use SPSS to mine text data and analyze survey data for best results. Therefore, it is preferable to perform descriptive statistics on SPSS.
Steps to Perform Descriptive statistics on SPSS
The various Steps to calculate Descriptive statistics on SPSS are given below-
Input Data in SPSS
SPSS allows manual data insertion as well as data import from various sources. The steps to import data from excel are as follows-
- Firstly, Select ‘File’ from the menu.
- Secondly, Choose Import Data>Excel.
- Thirdly, Look for data in the file manager and click ‘Open’.
- Lastly, Select the range of cells and click ‘OK’.
After importing the data in SPSS, click on the ‘Variable View’, and this sheet will open.
SPSS allows anyone to perform changes here according to his convenience to set up the variables correctly. After making the necessary changes click on the ‘Data View’ option in the bottom corner of the screen, and this screen will appear.
Descriptive Analysis SPSS
After inserting the data in SPSS software, the next step is to perform descriptive statistics using SPSS. To calculate descriptive statistics the various steps are given below-
- Firstly, Go to the ‘Analyze’ in the top menu and select ‘Descriptive Statistics’ > ‘Explore’.
2. Now a pop-up window will appear. Click on the variable and select the blue arrow to insert the targeted variables in the ‘Dependent List’ box.
3. Thirdly, click on ‘Statistics’, tick the ‘Descriptives‘, and press ‘Continue‘.
4. Lastly, click on ‘OK’, and SPSS will produce the final results.
Why is SPSS best for Descriptive Statistics?
There are several software options available that can produce descriptive statistics. Now, the question is why SPSS is a good choice for performing descriptive statistics.
Some of the reasons supporting this argument are as follows-
Simplicity
SPSS is very easy to use for every community. Even beginners who don’t have any knowledge of coding or statistics can comfortably use SPSS.
Complete Numerical Analysis
SPSS provides the whole descriptive statistics analysis in numerical form.
Anyone can easily produce a measure of dispersion, i.e., standard error, variance, range, standard variance, kurtosis, and skewness. Moreover, anyone can also measure central tendency that consists of mean, median, and mode as the necessary and most popular analysis.
Furthermore, Quartile, minimum, maximum, and percentile are also possible as a measure of position.
Customization
SPSS provides full control over the descriptive statistic. Therefore, It is completely the choice of the user about what to display and whatnot. Anyone can easily customize it with few clicks.
Hi, can someone please help me with by explaining how I can easily analyse a yes/no variable in spss.
Oh also, if someone could give advice relating to how to run normality checks for likert data that would also be appreciated!
how to run normality checks for likert data
Likert data obviously cannot be normal. Why would it matter, though? What requires a variable to be normal, and how much does it matter if it’s not?
It’s a 7 point likert scale so can’t it be treated as scale variable as opposed to an ordinal one? And if that’s the case would I not need to run a normality check?
If anyone is going to help you here you need to provide more context. There is no one test that you use of yes/no variables, just like there is no one test for continuous variables or anythings else.
It all depends on what you want to know, what your other variables are, etc.
Hi sorry, posted this in a bit of frenzied panic.
I’m analysing data from a questionnaire between two conditions so I am comparing means between conditions. I mainly have likert scale data with a few yes/no questions. Basically wanting to see if there is a significant difference between conditions. For the likert data I have run independent t-tests as well as Mann-Whitney tests. Different sample sizes between conditions so different variances as well.
Descriptive statistics can be used to summarize the data. If your data is categorical, try the frequencies or crosstabs procedures. If your data is scale level, try summaries or descriptives. If you have multiple response questions, use multiple response sets.
Report : Case Summaries
The Summarize procedure can be used to get descriptive information about data. You will probably want to turn off the “Display Cases”. Place the variables you want summarize in the “Variables” section and any grouping variables (if needed) in the “Grouping Variables” section.
In this example, I will get summary statistics on the height broken down by gender and favorite grocery store.
You can specify which statistics you want for the “Variables”. In this case, I have selected N (number of cases), the mean, median, minimum, maximum, and standard deviation. Click “continue” when done selecting.
You may see the output of the Summarize procedure.
Descriptive Statistics : Frequencies
The Frequencies procedure works better with categorical data than with scale data. It will tell you how many times each value appears in the data. You can include means, medians, etc, but that really doesn’t make sense with nominal data.
In this example, I will be getting frequency counts for two variables, gender and favorite season. You can request bar charts, pie charts, or histograms by clicking on the “Charts” button although I have not in this example.
You can see the output from the Frequencies procedure.
Descriptive Statistics : Descriptives
The Descriptives procedure gives descriptive statistics for the variables. It is geared more towards scale data rather than nominal or ordinal data, although you can get descriptive statistics for that level of measurement, also.
Click the “Options” button to specify which statistics you want computed.
You can view the output from the Descriptives procedure.
Descriptive Statistics : Crosstabs
The Crosstabs procedure is useful for generating the joint frequencies between different variables. Like the frequencies procedure, this works best with categorical data.
In this example, I want to see the how the favorite season compares to the favorite grocery store.
One use of “Statistics” is to perform a chi-square test for independence. This would test if the season and grocery store are independent of each other. Of course, our sample size is really too small to tell anything, but this may be useful in the future.
See the output from the Crosstabs procedure.
Custom Tables : Multiple Response Tables
This is similar to the frequencies or crosstabs procedure except that you can define sets containing multiple responses.
In this example, I have created a set called “food” that includes all eight of the food choices.
You may see the output from the Tables procedure.
Compare Means : Means
This can be used to generate summary statistics, but requires a dependent and independent variable.
SPSS stands for Statistical package for the Social Science and it is utilised for the in-depth research in complex statistical data analysis. It is useful for the organisations and entrepreneurs as well as scientists to research huge range of data and information for better analysis and evaluation. Most of the top research agencies across the globe use SPSS to analyse survey the data and mine text data so that they can get the most out of their research and survey projects. It is a software package used for interactive or batched, statistical analysis. Current versions of the software have the brand name, IBM SPSS Statistics. SPSS Help is useful for the data analysts to utilise diverse data sets and conduct different statistical analysis such as ANOVA, T test or MANOVA as well as regression analysis for analysing diverse data set and inter-relating the relationship between the dependent and independent variables. Hence, through the SPSS analysis, it would be possible for the researchers to critically analyse the relationship and interdependent between the variables under data sets and develop in depth critical evaluation.
Advantage of SPSS
In survey data analysis
The advantages of analysing data with the help of SPSS Data Analysis are such as it is not much effort that is needed for the researcher to use this software. The time required for analysing the data with the help of SPSS is comparatively less than any other statistical tool, which is further helpful for the researchers to conduct in-depth critical analysis by including huge numbers of numeric data and information successfully. The core functions of SPSS are such as statistics programming, modeler programming, text analysis for the survey programs and visualizing designers. Through these activities, it is possible for the researchers to conduct the data analysis in a systematic process. The basic application of this program is to analyse the scientific data, related with the social science and other research purposes, where the data analysis can be conducted with in-depth statistical analysis and evaluation. This data can be used for surveys, market research, data mining, etc. SPSS firstly store the data and organise it in a synchronised way so that statistical performance can be done efficiently. In order to produce suitable output, the data set can be conducted efficiently and SPSS is hereby designed in such a sway that it can handle a large data set with different formats.
Help In Data Transformation
SPSS is a very useful software system that is utilised by the researchers, scientists and others to process critical data in a simple way. Data analysis and evaluation is very time consuming process and it is also complex to evaluate and interpret the data systematically. But, SPSS software can easily handle large volume of data and information in order to progress in the research and draw final evaluation critically. This software is hereby useful for the data analysis and survey as it helps to analyse, transform and product the data set between different data variables, the output can be obtained through graphical representation so that the users can understand and easily interpret the results. Data Transformation is the first stage of the software, where the technique of converting the format of the data is suitable for progressing in the statistical analysis. As per the data type, the software is able to handle the large volume of data and information systematically and insert different kinds of SPSS. It will further change the structure and specification as per the requirement of the users. Through such process, SPSS will provides a scope to the researchers and others for conducting critical evaluation and thus it is considered that SPSS is helpful for the survey data analysis in order to draw final conclusion.
Regression analysis is the second stage, where it is effective to understand the relation between dependent and interdependent variables after storing the data in a synchronised way. It also helps to analyse interdependent between the variables in the data set. The third way that SPSS supports survey data analysis is ANOVA (Analysis of variance), in which comparing the events, groups or process can be possible. This method is more suitable for executing a task, where feasibility and effectiveness of the particular method can be ensured. MANOVA (Multivariate analysis of variance) is also utilised for comparing the data with random variables whose value is unknown. It is hereby critical analysis, which can be conducted through SPSS and it is also utilised for analysing different types of population and factors in the data set as per the requirements of the researchers. T test is utilised for analysing different sample types where the researchers can apply the method to find out the difference in the interest of two kinds of groups. It is also helpful for the researchers to conduct in-depth data analysis by considering diverse data set.
Summary
Hence, it can be concluded that, SPSS is beneficial for the survey data analysis, where the researchers can conduct survey by inclusion of the participants to collect data and develop good data set. After collecting the data and information through the survey process, the researchers can also organise the data through SPSS Data Analysis Software and perform the functions under SPSS such as ANOVA, T test or MANOVA as well as regression analysis, so that the relationship and data trend can be identified effectively. Hence, the survey data analysis through SPSS software will provide a scope to the researchers to analyse and evaluate the gathered data and information efficiently and improve the quality of the researcher for exploring final output. It would be beneficial for the researchers to utilise SPSS software in order to improve authenticity and clarity of the data analysis and evaluation for evaluating recent trend and interpreting the data by understanding the inter relationship between the dependent and independent data and information for meeting the survey hypothesis.
I’m so lost and confused and I have no help or support from my tutor who doesn’t even reply to emails.
I’ve collected quantitative data and got it all in SPSS but I’m confused as to what tests I should be using to discuss my data
I’m looking at consumer attitudes towards advertising on Facebook, Twitter and Instagram and comparing them.
So I looked at five variables (such as entertainment and pleasure) and asked respondents if they agreed with the various statement representing each variable. The same questions were then asked in terms of advertisements on Twitter and the Instagram.
Can anyone help?
If your tutor isn’t responding to emails, I’d show up at their office either during office hours or after sending an email to say “I’m coming to see you”. If there’s no one there, I’d email them again with all the previous emails they didn’t respond to and cc in the head of your year, your class/ school rep and the class administrator. Either that or email your head of year directly saying something like “I just wanted to check Prof So-and-So was okay. I’ve sent x number of emails and I haven’t had a response. I went during office hours and they weren’t there. I haven’t heard from them at all in a while and I wanted to make sure they were okay because they are my supervisor and I really desperately need some help analysing my data for my dissertation.”
As for your data, I’m not really sure what your research question is and I’ve never analysed a questionnaire, but if you’re comparing each social media to the others I’m sure that should be simple enough to figure out how to do with some googling.
Off the top of my head, If you used Likert scales (I think you did? strongly agree – strongly disagree?) I’m sure you could add up the participants scores for your 5 areas of interest for each social media and get a mean score for each, then you’d just need to compare those means. If your data is normally distributed (I use R so I’m not 100% up on SPSS, I don’t know what functions you have available to do this) and then I guess you’d just use a one way ANOVA to compare the 3 groups with each other.
Get on at your supervisor though. This is their job and your grade depends on them doing it right.
IBM SPSS Statistics (or “SPSS” for short) is super easy software for editing and analyzing data.
This tutorial presents a quick overview of what SPSS looks like and how it basically works.
SPSS Data Editor Window
SPSS’ main window is the data editor. It shows our data so we can visually inspect it.
This tutorial explains how the data editor works: we’ll walk you through its main parts and point out some handy tips & tricks.
SPSS Syntax Introduction
SPSS syntax is computer code used by SPSS for analyzing data, editing data, running statistical tests and more.
Using SPSS syntax is super easy and saves tons of time and effort. This tutorial quickly gets you started!
SPSS Output – Basics, Tips & Tricks
SPSS’ output window shows the tables, charts and statistical tests you run while analyzing your data.
This tutorial walks you through some basics such as exporting tables and charts to WORD or Excel. We’ll also point out some important tricks such as batch editing and styling tables and charts.
Normality Tests in SPSS
SPSS Shapiro-Wilk Test – Quick Tutorial with Example
The Shapiro-Wilk test examines if a variable is normally distributed in a population. This assumption is required by some statistical tests such as t-tests and ANOVA.
The SW-test is an alternative for the Kolmogorov-Smirnov test. This tutorial shows how to run and interpret it in SPSS.
SPSS Kolmogorov-Smirnov Test for Normality
The Kolmogorov-Smirnov test examines if a variable is normally distributed in some population.
This “normality assumption” is required for t-tests, ANOVA and many other tests. This tutorial shows how to run and interpret a Kolmogorov-Smirnov test in SPSS with some simple examples.
Must-Know Statistics
Effect Size – A Quick Guide
Effect size is an interpretable number that quantifies the difference between data and some hypothesis.
Effect size measures are useful for comparing effects across and within studies. This tutorial helps you to choose, obtain and interpret an effect size for each major statistical procedure.
Cohen’s D – Effect Size for T-Test
Cohen’s D is the effect size measure of choice for t-tests.
This simple tutorial quickly walks you through
- rules of thumb for small, medium and large effects;
- formulas for computing Cohen’s D and;
- software options for obtaining it.
What Does “Statistical Significance” Mean?
Statistical significance is roughly the probability of finding your data under some null hypothesis.
If this probability (or “p”) is low -usually p < 0.05- then your data contradict your null hypothesis. In this case, you conclude that the hypothesis is not true.
Pearson Correlations – Quick Introduction
A Pearson correlation is a number between -1 and +1 that indicates how strongly two variables are linearly related.
This simple tutorial quickly explains the basics with outstanding illustrations and examples.
SPSS – Popular Tutorials
SPSS Missing Values Tutorial
In SPSS, missing values refer to
- system missing values: values that are absent from the data;
- user missing values: values that are present in the data but must be excluded from analyses.
We’ll quickly walk you through both types. We’ll also show how to detect, set and deal with missing values in SPSS.
SPSS Factor Analysis – Beginners Tutorial
Factor analysis examines which variables in your data measure which underlying factors.
This tutorial illustrates the ideas behind factor analysis with a simple step-by-step example in SPSS.
SPSS Variable Types and Formats
SPSS has 2 types of variables:
- numeric variables contain only numbers and can be used for calculations;
- string variables contain text and cannot be used for calculations.
Numeric variables come in several formats such as plain numbers, dates and percentages. Working with SPSS becomes much faster and easier if you’re aware of variable types and formats.
Essential SPSS Commands
SPSS IF – A Quick Tutorial
In SPSS, IF computes a new or existing variable but for a selection of cases only.
For example: IF(GENDER = 0) SCORE = MEAN(Q1 TO Q5). computes “score” as the mean over variables Q1 to Q5 but only for cases whose gender is 0 (female).
SPSS SELECT IF – Tutorial & Examples
In SPSS, SELECT IF removes a selection of cases from your data.
This tutorial walks you through the basics and some FAQ’s such as
- how to remove cases based on 2 variables instead of one?
- how to remove cases based on (number of) missing values?
- how to visually inspect only those cases that will be removed?
SPSS FAQ
How to Find & Exclude Outliers in SPSS?
Three common ways to find outliers are
- inspecting histograms;
- inspecting boxplots;
- inspecting z-scores.
So how to find precisely which values to exclude? This tutorial walks you through all 3 methods.
Which Statistical Test Should I Use?
The vast majority of statistical tests fall into one of 6 basic types:
Look up which type of test is right for your data and you’ll see which test you should use.
How to Convert String Variables into Numeric Ones?
The correct way to convert a string variable into a numeric one is the ALTER TYPE command.
This tutorial walks you through with some examples. We’ll point out some tricks, pitfalls and alternatives as well.
In depth, critical research needs proper research aim and objectives, research questions, literature review, methodology as well as data collection and data analysis to draw the final conclusion. The researchers in the rennet era of globalisation perform efficient dissertation with recent data and market trend in order to explore alternative solution for enhancing creativity and innovation. Hence, for data analysis and performing a successful research, the researchers and analysts are trying to utilise the best tools and techniques so that the data would be interpreted well to draw the final conclusion. In this regard, SPSS is considered as one of the effective software system, which contributes efficiently in data analysis and completing the research successfully. Through this truckle, it is possible to understand the research activities, mainly data analysis with the SPSS help of the software system.
Understanding research and data analysis with SPSS
SPSS is mainly developed and occupied by IBM, where the users try to utilise it for better data collection and analysis. The data mining and sorting properties of SPSS are beneficial where the researchers can represent the data and information with the help of different charts, tables and graphs, to show the interlinkage and correlation between the variables.
Features of SPSS
- Statistics and charting capabilities
- Forecasting and decision trees on data
- Advanced statistics and custom tables add-on package
- Base edition
- Complex sampling
- Testing add-on
Major Statistical Tools are used in SPSS
- Correlation
- Regression
- Mean, median and mode
- Standard deviation
- ANOVA
- T Test
The above mentioned statistical tools are utilised efficiently in the dissertation, where the researchers develop hypothesis for further testing to draw the final conclusion. With the help of effective statistical tools, the researchers can include the numeric data and information in order to sort it out and handle the vast range of data and information.
For the research, the major steps of developing the ground the study are such as developing background of the study, aim and objectives of the research, questions and hypothesis. After that, literature review is being resented for gathering secondary information, theories and models related to the research. The research methodology is useful to choose the best way to conduct the study and complete it successfully. The next chapter in the dissertation is data findings and analysis, and it is considered as the main chapter of the research, where the researchers can gather data and information for critical evaluation. The study of SPSS data analysis Software is being utilised by most of the researchers in order to perform better and understand the correlation between the variables in the study. There are mainly one dependent and one independent variable which are included in the column of the SPSS software and in the row section; the data will be represented for analysis. The correlation and will be performed to analyse the recent trend and understand the impacts of the independent variable on the dependent one to draw the final conclusion.
The main data utilised in SPSS software system are in numeric in nature and there are vast range of data that can be managed well through the software system. Hence, it is easy software for the researchers to represent the data and import in the software system for further data analysis and evaluation. With the help of SPSS software, the researchers can analyse the data by performing the statistical analytical tools such as mean, median mode, as well as standard deviation, regression and correlation. The regression and correlation coefficient is the major statistical tools that are utilized by the researchers for better analysis to find the relationship between the dependent and independent variables in the study. Hence, SPSS is playing a crucial role to handle the vast data and perform the statistical analytics including descriptive analytics. The final report of the SPSS is also easily understandable, where there is a graphical interpretation that is understood by the researchers. The researchers and analysts can find the relationship, data trend and also predict future trends successfully.
Summary
It can be summarised that, SPSS is widely utilised by the researchers and analysts in order to perform better and complete the research efficiently with critical analysis and evaluation. The inbuilt process of the SPSS software system is beneficial for the researchers to conduct data sorting and mining as well as perform statistical data analysis successfully. The results are helpful to meet the research aim and objectives as well as test the research hypothesis for drawing final conclusion. It would be beneficial to utilise SPSS software system for completing the research successfully without any unethical practice, data manipulation and hacking. SPSS is hereby advantageous to support data analysis to perform efficiently in the research and complete the study with meeting the objectives.
Regression is a versatile statistical test used to understand and quantify the relationship between two or more variables. It is popularly used in secondary and primary data. It helps to not only understand the past trends but also predict the future.
For example, businesses use various regression models to predict future sales. Similarly, government agencies use it to understand economic performances from secondary data.
However, before applying a regression model it is important to check the correlation between the variables first. The previous article explained how to perform correlation tests on secondary data using SPSS. This article will explain the application of regression tests on secondary data using SPSS, using the same dataset.
Case description for regression test on secondary data using SPSS
In the previous article, it was shown that there is a correlation present among India’s Gross Domestic Product (GDP), unemployment rate (UNE) and population growth (POPG) for the period 2012-18. This article explores the extent of the impact of UNE and POPG on India’s GDP.
Accordingly, the hypothesis is:
Null hypothesis (H0): There is no relationship between unemployment rate, population growth and economic growth of India for the period 2012-2018.
Follow the below steps to perform the regression.
Step 1
Perform the correlation test as shown in the previous article.
Step 2
Run the regression test. For this, click on ‘Analyse’, then ‘Regression’, then ‘Linear’ as shown below.
The following window will appear. From this window move the natural log-transformed variable LnGDP to Dependent followed by LnUNE and LnPOPG to Independent(s).
Step 3
Click on ‘OK’. The following output window appears for regression analysis.
Interpreting the results showing impact of population growth and unemployment on India’s economic output
The above figure shows the output of the regression test. It has many values. Each must be interpreted independently before deciding whether to accept or reject the null hypothesis.
In the case of this example, the values of R square and adjusted R square are 0.98 and 0.97, depicting that about 97% of the variation in the economic growth of India is being represented by unemployment growth and population growth. This is a favourable result. The F value is 292.70, which is also favourable since it is more than 1. It denotes that there is more precision in the model due to the independent variables. The significance ‘Sig.’ value is 0.00. The significance value should always be less than 0.10 in order to prove that an impact is present. Since in this case, it is meeting the criteria, we can conclude that the unemployment rate and population growth rate rise has an impact on the economic growth of India.
Best Way to Analyze Data Using a Software
Before the conclusion is made about research that has been done, it is a requirement that the researcher analyzes the research findings using the most reliable statistical analysis tool. The analysis of the research findings is usually done in the fourth chapter of the scientific research paper or project. In this article, we will evaluate the importance of using SPSS as the data analyzing software on a research project’s data, for students and other researchers. First, a relevant chapter four content (a chapter in which the analysis of research findings is done) helps the research paper author have an easy task in making the conclusion in the research paper. This is because a good analysis of the research findings enables the person writing the research paper come up with the best way in which he or she can arrive at the conclusion about the research. There are many tools that can be used in the analysis of research findings. In this context, we will only focus on how important it is to analyze data with SPSS. It is evident that each researcher is always interested in obtaining relevant and high-quality research papers. This means that the person writing a research paper would desire to increase the relevance of his or her research paper content by ensuring that the chapter 4 content is relevant to the readers and/or the reviewers.
Need Research Project Data Analyzing Help?
By using the statistical tool we are looking at relevantly, there is always the assurance that the conclusion that will be made will be reliable in determining the success of the research that one has done. Once, as a researcher, you understand that there is a great importance of using statistical software in data analyzing, you should not fail to seek after knowing the best way you can use the tool. Online aid is always available and reliable for anyone who is not able to use any of the statistical tools to analyze the research findings. The advantage of the online help with using such tools is that there are different services available to help those who are unable to use the tools on their own. Having learned about SPSS data analyzing software importance, I presume that the students doing their academic research projects and the other researchers will now be able to increase the quality of their research papers content. An analyzed dissertation data should be understandable by even readers who are not scholars. This, however, depends on the target audience for the respective research paper. The value of thesis and dissertation data analysts cannot be ignored since they will always know how to match the requirements given for the paper with the readers’ needs. This means that depending on the audience targeted by the researcher, these analysts will know the approach in which they will analyze secondary data in a dissertation.
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The scientific study of collecting, analyzing, interpreting, modeling, and presenting data is known as statistics and it is used widely by researchers in the medical, business, organizations, government, and learning institutions to address several research issues in a social setting. However, these researchers face a lot of challenges during the data analysis phase since they have a lot of data to analyze and interpret and this has made them appreciate the importance of SPSS software in analyzing data. In a nutshell, SPSS is computer software that allows users to enter data, perform statistical tests such as t-test and draw graphs. Moreover, the software is user-friendly, easy to learn, cheap and compatible with a personal computer. SPSS is a preferred data analysis software and of great importance among students and professional researchers due to its capability of analyzing a wide scope as well as a large amount of data. Our firm has qualified dissertation data analysts, experts who have been assisting scholars in diverse academic fields to produce high-quality dissertations and theses. Thesis-Dissertations Writing Services guarantee you that consulting our experts for data analyzing services will make a difference in your academic achievement.
Academic Research Data Presentation & Discussions Help
Which approach are you supposed to use when it comes to the time of analyzing your researched data? This is a question which a number of students will find hard to answer. While some students might think that analyzing or discussing thesis data is mainly under the control of the student, there are some common and very crucial things which you should bear in mind before you start analyzing your thesis or dissertation data. You should be prepared to use different sources of references to building ideas for your research paper. In addition to that, you are supposed to be familiar with the scholarly formats for presenting dissertation data so that you will be able to present the details in your paper as required. More importantly, you should also be prepared to edit your project chapter 4 after you are through with analyzing. Editing ensures that your paper is well-organized, concise and clear. You also have the choice of consulting experts who offer Ph.D. research paper data analysis services if you feel challenged in writing and editing your thesis or dissertation. If you need SPSS data analysis service, do not hesitate to hire online expert analysts.
Professional Research Data Analyzing Services Using SPSS
As a statistical tool, SPSS software is very fast in analyzing data and it has minimum errors. The user has the freedom to choose a graph that will suitably represent the distribution of their data. Both qualitative and quantitative analysis can be analyzed by the software thus making the researchers work easier. Therefore, why SPSS is important in data analysis cannot be undermined since it has revolutionized the data analysis process. However, if students or researchers are unfamiliar with using SPSS software to analyze their data, they should not panic because online writing companies are offering excellent SPSS data analysis services. We are among the top ten best online writing companies offering urgent data analysis help to clients. We guarantee our clients that they will not only have their data analyzed and presented in a professional manner but they will also learn the significance of using SPSS in data analysis. Thesis data experts have always proven to be helpful to the students who have been seeking our assistance. A number of students often fail to realize that their research papers should not only be acceptable by their project supervisors but also useful to the readers who will be accessing their work online or in any way.
While Alchemer has powerful built-in reporting features that are easy to use and present for most online surveys, NPS survey, and employee satisfaction surveys, when it comes to in-depth statistical analysis most researchers consider SPSS the best-in-class solution.
SPSS is short for Statistical Package for the Social Sciences, and it’s used by various kinds of researchers for complex statistical data analysis. The SPSS software package was created for the management and statistical analysis of social science data. It was originally launched in 1968 by SPSS Inc., and was later acquired by IBM in 2009.
Officially dubbed IBM SPSS Statistics, most users still refer to it as SPSS. As the world standard for social-science data analysis, SPSS is widely coveted due to its straightforward and English-like command language and impressively thorough user manual.
SPSS is used by market researchers, health researchers, survey companies, government entities, education researchers, marketing organizations, data miners, and many more for processing and analyzing survey data, such as you collect with an online survey platform like Alchemer.
Most top research agencies use SPSS to analyze survey data and mine text data so that they can get the most out of their research and survey projects.
The Core Functions of SPSS
SPSS offers four programs that assist researchers with your complex data analysis needs.
Statistics Program
SPSS’s Statistics program provides a plethora of basic statistical functions, some of which include frequencies, cross-tabulation, and bivariate statistics.
Modeler Program
SPSS’s Modeler program enables researchers to build and validate predictive models using advanced statistical procedures.
Text Analytics for Surveys Program
SPSS’s Text Analytics for Surveys program helps survey administrators uncover powerful insights from responses to open-ended survey questions.
Visualization Designer
SPSS’s Visualization Designer program allows researchers to use their data to create a wide variety of visuals like density charts and radial boxplots from their survey data with ease.
In addition to the four programs mentioned above, SPSS also provides solutions for data management, which allow researchers to perform case selection, create derived data, and perform file reshaping.
SPSS also offers data documentation, which allows researchers to store a metadata dictionary. This metadata dictionary acts as a centralized repository of information pertaining to the data, such as meaning, relationships to other data, origin, usage, and format.
There are a handful of statistical methods that can be leveraged in SPSS, including:
- Descriptive statistics, including methodologies such as frequencies, cross-tabulation, and descriptive ratio statistics.
- Bivariate statistics, including methodologies such as analysis of variance (ANOVA), means, correlation, and nonparametric tests.
- Numeral outcome prediction such as linear regression.
- Prediction for identifying groups, including methodologies such as cluster analysis and factor analysis.
The Benefits of Using SPSS for Survey Data Analysis
Thanks to its emphasis on analyzing statistical data, SPSS is an extremely powerful tool for manipulating and deciphering survey data.
Fun fact: The data from any online survey collected using Alchemer can be exported to SPSS for detailed analysis.
Exporting survey data from Alchemer to SPSS’s proprietary .SAV format makes the process of pulling, manipulating, and analyzing data clean and easy. Using the .SAV format, SPSS automatically sets up and imports the designated variable names, variable types, titles, and value labels, making the process much easier on researchers.
Once survey data is exported to SPSS, the opportunities for statistical analysis are practically endless.
In short, remember to use SPSS when you need a flexible, customizable way to get super granular on even the most complex data sets. This gives you, the researcher, more time to do what you do best — identifying trends, developing predictive models, and drawing informed conclusions.
For more information on the benefits of using SPSS to conduct survey data analysis, here are some helpful resources:
Multiple linear regression is a method we can use to understand the relationship between two or more explanatory variables and a response variable.
This tutorial explains how to perform multiple linear regression in SPSS.
Example: Multiple Linear Regression in SPSS
Suppose we want to know if the number of hours spent studying and the number of prep exams taken affects the score that a student receives on a certain exam. To explore this, we can perform multiple linear regression using the following variables:
Explanatory variables:
- Hours studied
- Prep exams taken
Response variable:
- Exam score
Use the following steps to perform this multiple linear regression in SPSS.
Step 1: Enter the data.
Enter the following data for the number of hours studied, prep exams taken, and exam score received for 20 students:
Step 2: Perform multiple linear regression.
Click the Analyze tab, then Regression, then Linear:
Drag the variable score into the box labelled Dependent. Drag the variables hours and prep_exams into the box labelled Independent(s). Then click OK.
Step 3: Interpret the output.
Once you click OK, the results of the multiple linear regression will appear in a new window.
The first table we’re interested in is titled Model Summary:
Here is how to interpret the most relevant numbers in this table:
- R Square: This is the proportion of the variance in the response variable that can be explained by the explanatory variables. In this example, 73.4% of the variation in exam scores can be explained by hours studied and number of prep exams taken.
- Std. Error of the Estimate: The standard error is the average distance that the observed values fall from the regression line. In this example, the observed values fall an average of 5.3657 units from the regression line.
The next table we’re interested in is titled ANOVA:
Here is how to interpret the most relevant numbers in this table:
- F: This is the overall F statistic for the regression model, calculated as Mean Square Regression / Mean Square Residual.
- Sig: This is the p-value associated with the overall F statistic. It tells us whether or not the regression model as a whole is statistically significant. In other words, it tells us if the two explanatory variables combined have a statistically significant association with the response variable. In this case the p-value is equal to 0.000, which indicates that the explanatory variables hours studied and prep exams taken have a statistically significant association with exam score.
The next table we’re interested in is titled Coefficients:
Here is how to interpret the most relevant numbers in this table:
- Unstandardized B (Constant): This tells us the average value of the response variable when both predictor variables are zero. In this example, the average exam score is 67.674 when hours studied and prep exams taken are both equal to zero.
- Unstandardized B (hours): This tells us the average change in exam score associated with a one unit increase in hours studied, assuming number of prep exams taken is held constant. In this case, each additional hour spent studying is associated with an increase of 5.556 points in exam score, assuming the number of prep exams taken is held constant.
- Unstandardized B (prep_exams): This tells us the average change in exam score associated with a one unit increase in prep exams taken, assuming number of hours studied is held constant. In this case, each additional prep exam taken is associated with a decrease of .602 points in exam score, assuming the number of hours studied is held constant.
- Sig. (hours): This is the p-value for the explanatory variable hours. Since this value (.000) is less than .05, we can conclude that hours studied has a statistically significant association with exam score.
- Sig. (prep_exams): This is the p-value for the explanatory variable prep_exams. Since this value (.519) is not less than .05, we cannot conclude that number of prep exams taken has a statistically significant association with exam score.
Lastly, we can form a regression equation using the values shown in the table for constant, hours, and prep_exams. In this case, the equation would be:
Estimated exam score = 67.674 + 5.556*(hours) – .602*(prep_exams)
We can use this equation to find the estimated exam score for a student, based on the number of hours they studied and the number of prep exams they took. For example, a student that studies for 3 hours and takes 2 prep exams is expected to receive an exam score of 83.1:
Estimated exam score = 67.674 + 5.556*(3) – .602*(2) = 83.1
Note: Since the explanatory variable prep exams was not found to be statistically significant, we may decide to remove it from the model and instead perform simple linear regression using hours studied as the only explanatory variable.
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Those who have already done dissertations can confirm to you that it isn’t usually a task that one decides to begin without any preparations since these projects support scholars’ academic achievement. It is very good you feel that you need Ph.D. thesis data analysis services since these are very crucial services that will enable you to boost your research paper’s quality for the best. Therefore, do not feel like you are doing the wrong thing since your research paper counts a lot in boosting your overall academic performance as you complete your course. What you want at the end of it all, even after getting a Ph.D. dissertation SPSS analysis help, is to have your research paper accepted and approved, and that is what matters most after you have written your paper. Note that; analyzing research results in a Ph.D. research project is something that can take a lot of your time and efforts, and even to the worst part of it your paper ends up being rejected. As such, we recommend you hire statisticians that analyze research results from our firm and be enabled to boost your academic achievement. Our experts are persons that are experienced in doing different statistical tests and using all the statistical software, hence they will offer you reliable assistance. SPSS is among the most commonly used analytical tool, which is very suitable due to its capacity to interpret even the most complex data. Many researchers have used SPSS to analyze Ph.D. dissertation data, and even though they are skilled, they, at times, require professional aid. There is no shame or guilt in looking for assistance, considering that you only seek to professionalize your work.
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In almost every school, organization or company, the activities that are undertaken will very much require a lot of data collection. In schools, students are given assignments to do, which they are expected to handle professionally. The mostly applied statistical software in data analysis is SPSS, which has for a long time turned to be very efficient. We know that each of our clients’ expectations is to get the best from us, the reason we have hired qualified persons who we also take through regular professional training to sharpen their skills. Thus be sure that the maximum level of consistency is guaranteed with our services. You won’t be delayed when you write to us “I need to pay an experienced Ph.D. dissertation data analyzing expert” since we always aim at meeting our client’s expectations.
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- To understand the kind of data you’ve collected
- To realize the best statistical approach to use
- To know which statistical tools and software to apply
- To be aware of the tests supposed to be done on the data
- To have an intuition of the kind of results to expect
Get Quick Help with Analyzing Research Data Using SPSS
Now that you know what is expected of you, it is high time to look for help from statisticians who use SPSS in Ph.D. Dissertations. You do not hire experts because you are incapable, but rather to provide assistance and make your work more professional. Imagine the kind of results you will be obtained by working with experts. Do you realize that poor grades will no longer be a threat to you? This is why you should hire us, since besides being very professional in analyzing data in Ph.D. dissertations using SPSS; we also are highly confidential when it comes to handling any client’s information. You can, therefore, trust us with your data, fully sure that we will provide nothing but the best. You are our much-esteemed client, the reason why we charge you fairly while keeping to our promise of being very timely. You are supposed to write a Ph.D. dissertation, and it is a matter of your professional reputation. As a student who understands how relevant it is to write a good dissertation, you will stop at nothing to ensure the professionalism and credibility of your work. A dissertation is a task that involves a lot of workloads; the most daunting activity is how to analyze data. That becomes even challenging when you are supposed to use a given analytical software package, given that you may be very new to such statistical tools. It is vital to ensure the professionalism of your work, but for that to happen; you may need a team of experts to assist you. That is why we have a readily available team of expert analysts, who have what it takes to provide you with reliable services. Analyzing data is a task that requires professionalism, which is why we are a suitable choice when it comes to experts. We are your very reliable data analysis partner you can count on, even when the time you have is limited. We provide the best SPSS help within the given deadline, without overcharging you.
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Book Description
A Handbook of Statistical Analyses Using SPSS clearly describes how to conduct a range of univariate and multivariate statistical analyses using the latest version of the Statistical Package for the Social Sciences, SPSS 11. Each chapter addresses a different type of analytical procedure applied to one or more data sets, primarily from the social and behavioral sciences areas. Each chapter also contains exercises relating to the data sets introduced, providing readers with a means to develop both their SPSS and statistical skills. Model answers to the exercises are also provided. Readers can download all of the data sets from a companion Web site furnished by the authors.
Table of Contents
A Handbook of Statistical Analyses Using SPSS clearly describes how to conduct a range of univariate and multivariate statistical analyses using the latest version of the Statistical Package for the Social Sciences, SPSS 11. Each chapter addresses a different type of analytical procedure applied to one or more data sets, primarily from the social and behavioral sciences areas. Each chapter also contains exercises relating to the data sets introduced, providing readers with a means to develop both their SPSS and statistical skills. Model answers to many of the exercises are provided on a companion Web site furnished by the authors. Readers can also download all of the data sets from the Internet.
Reviews
“. . . well written and does cover a wide range of techniques very effectively . . . It gives clear descriptions using interesting data sets with exercises designed to check understanding. A worthwhile addition to the bookshelf for anyone about to utilize one of the more advanced statistical techniques covered.”
– Angie Wade, Centre for Paediatric Epidemiology and Biostatistics, Institute of Child Health, in Statistics in Medicine , 2005, Vol. 25
For students, researchers and data analysts who don’t have a strong statistical background, the Data Analysis and Interpretation using SPSS course teaches you statistical data analysis, interpretation and APA reporting in a simple, practical approach
6 weeks | 70 videos | 6 Exercises | Final Project | Certificate of Completion
What you will learn in the course
The course takes you from absolute beginner of SPSS and statistics, all the way to advanced topics that you will be frequently using in your research projects
- Data entry, data importing and preparation
- Summarizing data using descriptive statistics
- Exploring relationships between different types of variables
- Choosing appropriate charts and developing them
- Transforming variables and managing the data to suit your analyses
- Choosing the appropriate inferential tests such as chi-square, t-tests and regression and running them
- How to interpret all the statistics presented in the course and presenting them using the APA format
- How to write your research methodology, results and discussions sections
Description
Are you supposed to do data analysis but you don’t have a string statistics or data analysis background?
Whether you are a student writing a thesis/dissertation or you work on a role that requires that you analyze data and report the results, this is the course for you.
In the course , you will learn statistical data analysis from the very basics to the advanced without worrying about complicated calculations:
You will discover:
- How to structure your data for analysis
- How to choose the type of analyses to run depending on the structure of your data
- How to run those analyzes in SPSS
- Understanding and interpreting the analyses
- How to report your analyzes in the APA standard
- Aside from the self-paced video lessons, you will also have the chance to ask questions related to your data analysis projects during our 2-hour long weekly live Q&A sessions through Zoom.
With this course, you are guaranteed to ace your statistical data analyses and report writing.
Task 1: Analyse a data set and respond to research questions provided. Produce a research report with supporting statistical evidence.
Intended Course Outcomes to be demonstrated:
Determine an appropriate method of analysis.
Describe and analyse group data using the statistical package SPSS.
Interpret the results of analysis and write reports of results.
The data set provided for this task has been drawn from the publicly available PISA data (the OECD’s Programme for International Student Assessment), in this case the ‘school survey’, typically completed by each sample school’s principal. You will also find the technical manual explaining what each question and variable is AND how scale variables have been constructed. You will find the scales toward the bottom end of the variables.
There are also ‘weighted’ variables in the set which I have left there in case anyone wants to explain sample weighting as an issue. (That is an advanced issue, so I don’t expect it from the class, but if you think you want to understand how to approach advanced sampling issues, you will find some of the best technical work on that in the PISA surveys.)
Using the data set provided, produce a report that responds to the following questions. To address the questions, you will need to choose two scale outcome measures (dependent variables), from among the many in this data set. For each question below, use both numeric and graphic representations to present what you find, and explain your results and interpretations textually (in prose exposition):
How would you describe the overall distribution of your chosen two or three dependent scale variables available in the data? (For this consider the overall sample mean, n, standard deviation, skew and kurtosis.)
Select at least two different school context variables (independent variables), one each of a nominal grouping and a continuous (scale) measure. How do your chosen dependent variables differ between your chosen independent measures? That is, compare the descriptive statistics of the outcome variable based on the context variable differences, as denoted by the appropriate variable. Determine which differences are statistically significant, and determine the relevant effect size for any differences using conventional formula.
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You have been asked to analyse some data using SPSS. Your boss hands you a sheaf of sheets with data records relating to panic disorder and anxiety of 26 people. This data is in the file “Input data for portfolio”. You have three tasks to do on this data…
Task 1:
Construct an SPSS data file for the data you have been given. Design a suitable file, using labels, values, etc. and enter the data into it. Make sure the data types and measures are correct for all the data recorded.
For each respondent, calculate the BMI (Body Mass Index) by dividing the weight by the height squared (BMI=weight/height2). For this, you need to create a new variable for the BMI and automatically calculate the values (using Transform -> Compute variable option in the SPSS menu).
Task2:
Using the SPSS data file you created, do the following analysis…
Produce a summary of the frequencies of each gender and age groups in the sample.
Produce a cross-tabulation of age group and gender.
Draw a histogram of the heights of the males.
Draw an appropriate graph showing the frequency distribution of the age groups, by gender.
Draw a scatterplot of height (as the independent variable) against weight (as the dependent variable) for the whole sample.
Task 3:
Your boss has some hypotheses that she wants you to investigate. Perform the appropriate tests and summarise the conclusion to each hypothesis.
Null hypothesis: Those who have a diagnosis of panic disorder have a similar Zung Anxiety score to those who have not.
Question: Is there a correlation between BMI and Zung Anxiety score?
Null hypothesis: There is no difference in mean BMI between the men and women.
Null hypothesis: Those aged under 45 years are no more or less likely to have been diagnosed with a panic disorder as those aged 45 or over.
SPSS, standing for Statistical Package for the Social Sciences, is a powerful, user-friendly software package for the manipulation and statistical analysis of data. The package is particularly useful for students and researchers in psychology, sociology, psychiatry, and other behavioral sciences, containing as it does an extensive range of both univariate and multivariate procedures much used in these disciplines. Our aim in this handbook is to give brief and straightforward descriptions of how to conduct a range of statistical analyses using the latest version of SPSS, SPSS 11.
Each chapter deals with a different type of analytical procedure applied to one or more data sets primarily (although not exclusively) from the social and behavioral areas. Although we concentrate largely on how to use SPSS to get results and on how to correctly interpret these results, the basic theoretical background of many of the techniques used is also described in separate boxes. When more advanced procedures are used, readers are referred to other sources for details. Many of the boxes contain a few mathematical formulae, but by separating this material from the body of the text, we hope that even readers who have limited mathematical background will still be able to undertake appropriate analyses of their data.
The text is not intended in any way to be an introduction to statistics and, indeed, we assume that most readers will have attended at least one statistics course and will be relatively familiar with concepts such as linear regression, correlation, significance tests, and simple analysis of variance. Our hope is that researchers and students with such a background will find this book a relatively self-contained means of using SPSS to analyze their data correctly.
Crosstabs is an SPSS procedure that cross-tabulates two variables, thus displaying their relationship in tabular form. Crosstabs creates a table that contains a cell for every combination of categories in the two variables. Inside each cell is the number of cases that fit that particular combination of responses.
- Load your excel file with all the data. Once you have collected all the data, keep the excel file ready with all data inserted using the right tabular forms.
- Import the data into SPSS.
- Give specific SPSS commands.
- Retrieve the results.
- Analyse the graphs and charts.
- Postulate conclusions based on your analysis.
Regarding this, how do you analyze cross tabulation?
Cross tabulation is a method to quantitatively analyze the relationship between multiple variables. Also known as contingency tables or cross tabs, cross tabulation groups variables to understand the correlation between different variables. It also shows how correlations change from one variable grouping to another.
What is a crosstab query?
A crosstab query is a type of select query. When you create a crosstab query, you specify which fields contain row headings, which field contains column headings, and which field contains values to summarize. You can use only one field each when you specify column headings and values to summarize.