In the questionnaire analysis of SPSS, a questionnaire is a case. Firstly, variables should be defined according to different questionnaire questions. There are two noteworthy points in defining variables: one is to distinguish the measure of variables from the value of the measure, in which the scale is quantitative, the ordinal number is ordinal number, and the nominalization is designated class; Second, pay attention to defining different data types.
The types of various questionnaire topics can be roughly divided into four types: single choice, multiple choice, ranking and open-ended questions. Their variables are defined and handled in different ways. We will introduce the following examples in detail:
1 multiple choice question: the answer can only have one option.
Example 1 Does your organization currently have an organization-oriented career planning system?
A has B, and it is starting. C has no D, but it has been interrupted.
Code: Only one variable is defined, and the values of 1, 2, 3 and 4 respectively represent four options: A, B, C and D.
Enter: enter the corresponding value of the option; If c is selected, please enter 3.
2 Multiple choice questions: The answer can have multiple options, including multiple choices for indefinite items and multiple choices for fixed items.
(1) method 1 (dichotomy):
What groups does your career planning system cover? When you draw a hook, please put all the hints in.
Take it into consideration.
A monthly worker b daily worker c hourly worker
Code: each corresponding option is defined as a variable, and the value of each variable is defined as follows: Select 1 instead of "0".
Input: Enter 1 for the option selected by the respondent, and do not select 0. If the respondent chooses AC, the three variables are input as 1, 0 and 1 respectively.
(2) Method 2:
What do you think are the three most important goals in carrying out the educational activities to maintain the advanced nature of Communist party member?
1( ) 2 ( ) 3( )
A, improve the quality of party member; b, strengthen grass-roots organizations; c, persist in promoting democracy.
D. Stimulate the enthusiasm of entrepreneurs E. Serve the people F. Promote all work.
Coding: three variables are defined to represent the brackets of 1, 2,3 in the title, and their values are all defined by the corresponding options, namely "1"a, "2" b, "3" c, "4" d, "5" e and "6" f.
Input: the input values of 1, 2, 3, 4, 5, and 6 respectively represent the option ABCDEF, and they are input under the variable corresponding to each bracket. If the respondent chooses ACF in three brackets, enter 1, 3, and 6 respectively under three variables.
Note: Multiple choice questions that can be coded by Method 2 can also be coded by Method, but multiple choice questions with uncertain items can only be coded by dichotomy, that is, Method 1 is the general method for multiple choice questions.
3 ranking question: rank the importance of options.
When you buy goods, the order of your attention is (please fill in the code and rearrange it).
First, second, third, fourth and fifth.
Code: five variables are defined, which can represent the first bit and the fifth bit respectively. The value of each variable is defined as follows: "1" brand, "2" popularity, "3" quality, "4" practicality and "5" price.
Input: Enter the numbers 1, 2, 3, 4 and 5 to represent five options respectively. If the respondent ranks quality first, enter "3" under the variable representing the first place.
4 Select the sorting problem:
In case 5, the question in case 3 was changed to "What do you think is the most important thing to carry out educational activities to maintain the advanced nature of party member?"
The goal is those three items, in order of importance from high to low, and the options remain the same.
Coding: Six variables are defined according to the six options of ABCDEF6, and the value of each variable is defined as follows: "1" is not selected, "2" ranks first, "3" ranks second and "4" ranks third.
Input: input according to the value of the variable. For example, if ECF is selected in three brackets, the values of the six variables in this question should be entered respectively: 1 (representing that option A is not selected), 1, 3 (representing that option C ranks second), 1, 2, 4.
Note: This method is a combination of multiple-choice questions and ranking questions, and can also be applied to general ranking questions (Example 4), except that they use different analysis methods (Example 4 uses frequency analysis and Example 5 uses descriptive analysis), and the output results reflect the importance of the questions from different aspects (the former method looks at the ranking from the frequency of variables, and the latter method looks at the ranking from variables).
5 Open-ended numerical questions and scale questions: These questions require respondents to fill in their own numerical values or score.
Example 6 Your age (actual age): _ _ _ _ _ _
Code: Variable with no defined value.
Input: enter the actual value filled in by the respondent.
Six open text questions:
If possible, the answers with similar meanings can be coded and converted into closed options for analysis. If the answers are rich and difficult to classify, we will directly make a qualitative analysis of such questions.
Third, the general analysis of the questionnaire
The following details the general processing methods of questionnaires in SPSS. Operation takes spss 13.0 version as an example, and the menu items mentioned below are all under the analysis main menu.
1 frequency analysis: the frequency process can be used as a univariate frequency distribution table; Displays the frequency of occurrence of specific values of variables specified by users in data files; Get some statistics describing the range of values and statistics describing the range of values.
Scope of application: multiple-choice questions (example 1), sorting questions (example 4) and multiple-choice methods (example 3)
Frequency analysis is also the most commonly used method in questionnaire analysis.
Implementation: Descriptive Statistics ... Frequency
2 Descriptive analysis: Descriptives: This process can calculate descriptive statistics of univariate. These statistics include mean value, arithmetic sum, standard deviation, maximum value, minimum value, variance, range of mean value and standard error.
Scope of application: multiple-choice questions and sorting questions (Example 5) and numerical questions (Example 6).
Implementation: descriptive statistics ... descriptive, click the statistics button to select the required statistics.
3 Frequency analysis under multiple reactions:
Scope of application: multiple choice dichotomy (Example 2)
Realization: Step 1, gather all the variables defined by a multiple-choice question in multiple answers ... define a set, name a new set variable, and enter 1 in the dichotomy count. The second step is to do frequency analysis under multiple response ... frequencies.
4. Cross-frequency analysis: solve the frequency analysis problem at all levels of multivariate combination.
Scope of application: It is suitable for contingency table formed by cross-classification of two or more variables, and analyzes the correlation between variables. For example, if you want to know how people with different job properties use transportation at work, you can get a two-dimensional frequency table through cross analysis, which is clear at a glance.
Realization: Step 1, determine the options of cross-analysis according to the analysis purpose, and determine the control variables and explanatory variables (for example, artificial control variables with different work properties use vehicles as explanatory variables). Step 2: Select descriptive statistics ... crosstab.
Introduce four simple graphic descriptions.
When doing the above frequency analysis and descriptive analysis, you can directly make a graph, which is simple and convenient, or you can make another graph. The drawing function of SPSS is powerful, and the graphics under the menu graphics are clear and beautiful. Now the common charts are briefly introduced as follows.
1 pie chart: also known as pie chart, it is a statistical chart that uses the area of a circle to represent the population of the research object, and divides the area of the circle into several sectors according to the proportion of each component in the population to represent the proportional relationship between the phenomenon part and the population. The results of frequency analysis should be represented by pie charts.
2 graph: it is a statistical graph that shows the change of data with the rise and fall of line segments. It mainly shows the changing trend of phenomena in time, the distribution of phenomena and the dependence of the two phenomena.
Area map: a statistical map that emphasizes the change of phenomena with the shadow area under the line segment.
Bar chart: a statistical chart showing the size and change of statistical data by the length or height of bars with the same width.
Five questionnaire in-depth analysis
In addition to the above simple analysis, we can also use the powerful functions of spss to make in-depth analysis of the questionnaire, such as cluster analysis, cross analysis, factor analysis, mean ratio analysis (parameter test), correlation analysis, regression analysis and so on. Because it involves very professional statistical knowledge, the following is a brief introduction to the application scope and analysis purpose of personal useful methods:
1 cluster analysis
Sample clustering can classify the respondents and calculate the proportion of each category according to these attributes, so as to clearly study the groups concerned. For example, the respondents are clustered according to their consumption characteristics.
2 correlation analysis
Correlation analysis is an analysis method for whether there is correlation between two or more variables, and different correlation measurement methods should be selected according to the different characteristics of variables. Most of the variables used in questionnaire analysis belong to classified variables, and Spearman correlation coefficient should be adopted.
Chi-square test can be used, which is an analysis method of whether there is significant influence between two variables.
Comparison and test of three average values
(1) mean process: comprehensively describe and analyze the specified variables, calculate the mean in groups and then compare them. For example, it can be divided into men and women according to gender variables to study whether there is a gap in income between them.
(2)T test:
Independent sample t test is used to test whether irrelevant samples come from the same population. For example, study whether there is a significant difference in income between customers who buy the product and customers who don't buy the product.
If the samples are not independent, paired t test should be used. For example, study whether the work efficiency is improved after participating in vocational training.
4 Regression analysis
In the regression analysis of questionnaire analysis, discrete regression model, usually logistic model, is often used to explain the influence of one variable on another. For example, study the impact of income on the consumption of a commodity.