Chi-Square Goodness-of-Fit Test in SPSS Statistics
Introduction
The chi-square goodness-of-fit test is a single-sample nonparametric test, also referred to as the one-sample goodness-of-fit test or Pearson's chi-square goodness-of-fit test. It is used to determine whether the distribution of cases (e.g., participants) in a single categorical variable (e.g., "gender", consisting of two groups: "males" and "females") follows a known or hypothesised distribution (e.g., a distribution that is "known", such as the proportion of males and
or a distribution that is "hypothesised", such as the proportion of males versus females that we anticipate voting for a particular political party in the next elections). The proportion of cases expected in each group of the categorical variable can be equal or unequal (e.g., we may anticipate an "equal" proportion of males and females voting for the Republican Party, or an "unequal" proportion, with 70% of those voting for the Republican Party being male and only 30% female).
When you carry out a chi-square goodness-of-fit test, "hypothesising" whether you expect the proportion of cases in each group of your categorical variable to be "equal" or "unequal" is critical. Not only is it an important aspect of your research design, but from a practical perspective, it will determine how you carry out the chi-square goodness-of-fit test in SPSS Statistics, as well as how you interpret and write up your results.
In this "quick start" guide, we show you how to carry out a chi-square goodness-of-fit test using SPSS Statistics when you have "equal" expected proportions (e.g., you anticipated an "equal" proportion of males and females voting for the Republican Party). In addition, we explain how to interpret the results from this test. However, if you have "unequal" expected proportions (e.g., you anticipated 70% of those voting for the Republican Party being male and only 30% female), we show you how to do this in our enhanced chi-square goodness-of-fit guide, which you can learn about . Therefore, assuming that you would like to know the SPSS Statistics procedure and interpretation of the chi-square goodness-of-fit test when you have equal expected proprotions, you first need to understand the different assumptions that your data must meet in order for a chi-square goodness-of-fit to give you a valid result. We discuss these assumptions next.
SPSS Statistics
Assumptions
When you choose to analyse your data using the chi-square goodness-of-fit test, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using a chi-square goodness-of-fit test. You need to do this because it is only appropriate to use a chi-square goodness-of-fit test if your data meets four assumptions that are required for a chi-square goodness-of-fit test to give you a valid result. In practice, checking for these assumptions is a relatively simple process, only requiring you to use SPSS Statistics. Let’s take a look at these four assumptions:
Assumption #1: One categorical variable (i.e., the variable can be dichotomous, nominal or ordinal).
Examples of dichotomous variables include gender (2 groups: male or female), treatment type (2 groups: medication or no medication), educational level (2 groups: undergraduate or postgraduate) and religious (2 groups: yes or no). Examples of nominal variables include ethnicity (e.g., 3 groups: Caucasian, African American and Hispanic), and profession (e.g., 5 groups: surgeon, doctor, nurse, dentist, therapist). Examples of ordinal variables include Likert scales (e.g., a 7-point scale from "strongly agree" through to "strongly disagree"), amongst other ways of ranking categories (e.g., a 5-point scale for measuring job satisfaction, ranging from "most satisfied" to "least satisfied"; a 4-point scale determining how easy it was to navigate a new website, ranging from "very easy" to " or a 3-point scale explaining how much a customer liked a product, ranging from "Not very much", to "It is OK", to "Yes, a lot"), and physical activity level (e.g., 4 groups: sedentary, low, moderate and high).
Assumption #2: You should have independence of observations, which means that there is no relationship between any of the cases (e.g., participants).
Assumption #3: The groups of the categorical variable must be mutually exclusive. For example, if the four groups of a categorical variable, Physical Activity Level, were "Sedentary", "Low", "Moderate" and "High", a case (e.g., a participant in an exercise study) could only be in one of these four groups (e.g., a participant could not be classified as having a "High" activity level and a "Low" activity level, but only one or the other).
Assumption #4: There must be at least 5 expected frequencies in each group of your categorical variable. This is an assumption of the chi-square goodness-of-fit test and will be shown in your SPSS Statistics output when you run the test.
Therefore, before proceeding, check that your study design meets assumptions #1, #2 and #3. Assuming they do, you will now need to check that your data meets assumption #4, which you can do using SPSS Statistics. We explain how to test for assumption #4 and how to interpret the SPSS Statistics output in our enhanced chi-square goodness-of-fit guide to help you get this right. You can find out about our enhanced content as a whole , or more specifically, learn how we help with testing assumptions .
In the section, , we illustrate the SPSS Statistics procedure required to perform a chi-square goodness-of-fit test assuming that no assumptions have been violated and when you have equal expected proportions. First, we set out the example we use to explain the chi-square goodness-of-fit procedure in SPSS Statistics.
SPSS Statistics
A website owner, Christopher, wants to offer a free gift to people that purchase a subscription to his website. New subscribers can choose one of three gifts of equal value: a gift voucher, a cuddly toy or free cinema tickets. After 1000 people have signed up, Christopher wants to review the figures to see if the three gifts offered were equally popular.
In this case, the three gifts & a gift voucher, a cuddly toy or free cinema tickets & reflect the three groups of the categorical variable, gift_type. The 1000 people that have signed up reflect the "cases" (i.e., cases can be anything from "people", to "animals", "objects", "organisations", and so forth).
SPSS Statistics
Data Setup in SPSS Statistics
There are two methods of entering data into SPSS Statistics in order to run a chi-square goodness-of-fit test in SPSS Statistics. Common to both methods is a column in the SPSS Statistics data file for the categorical variable, which in this example, we shall name gift_type. We have assigned codes of "1" for the gift certificate, which we labelled "Gift Certificate", "2" for the cuddly toy, which we labelled "Cuddly Toy", and "3" for the free cinema tickets, which we labelled "Cinema Tickets". If the frequency data has already been summated for the various categories, we need to create a second column that contains the respect we have called this variable frequency. This type of data entry is shown below:
Published with written permission from SPSS Statistics, IBM Corporation.
Note: If you have entered your data in this way, you cannot run the chi-square goodness-of-fit test without first "weighting" your cases. This is a procedure that tells SPSS Statistics that you have summated your categories. It is required because it changes the way that SPSS Statistics deals with your data in order to run the chi-square goodness-of-fit test. If you are unsure how to weight your cases, we show you how to do this in our enhanced chi-square goodness-of-fit guide.
Alternatively, you may have the data in raw form (i.e., you have not summated the frequencies). In this case, you do not need a second column as SPSS Statistics can calculate the frequencies of occurrence of each category for you. This would mean that, in this example, there are 1000 rows of data, of which the beginning of said data is shown below:
Published with written permission from SPSS Statistics, IBM Corporation.
If you are still unsure how to enter your data accurately into the Data View and Variable View of SPSS Statistics, we should you how to do this in our enhanced chi-square goodness-of-fit test guide. You can learn about our enhanced data setup content in general .
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Test Procedure in SPSS Statistics
The four steps below show you how to analyse your data using a chi-square goodness-of-fit test in SPSS Statistics when you have hypothesised that you have equal expected proportions (N.B., if you are unclear about the differences between equal and unequal expected proportions, see the ). Also, it is important to note that this procedure will only give you the correct results if you have set up your data correctly in SPSS Statistics (N.B., if you have entered the summated frequencies for each group of your categorical variable, this procedure will only work if you have already "weighted" your cases, as we explained in the
section earlier, but if you have entered all of your data into SPSS Statistics in raw form, this procedure will not give the correct results). In our enhanced chi-square goodness-of-fit test guide, we show all the SPSS Statistics procedures for when you have equal and unequal expected proportions, as well as when you have to weight your cases or have not summated your data. If you only need to follow this "quick start" guide for equal expected proportions (without the weighting of cases), the four steps you need are shown below. At the end of these four steps, we show you how to interpret the results from this test.
Click Analyze & Nonparametric Tests & Legacy Dialogs & Chi-square... on the top menu as shown below:
Note: If you are on older versions of SPSS Statistics, you will not have to go through the Legacy Dialogs menu.
Published with written permission from SPSS Statistics, IBM Corporation.
You will be presented with the Chi-square Test dialogue box, as shown below:
Published with written permission from SPSS Statistics, IBM Corporation.
Transfer the gift_type variable into the Test Variable List: box by using the
button, as shown below:
Published with written permission from SPSS Statistics, IBM Corporation.
Keep the All categories equal option selected in the &Expected Values& area as we are assuming equal proportions for each category.
button to generate the output.
SPSS Statistics
SPSS Statistics Output for Chi-Square Goodness-of-Fit Test
The SPSS Statistics output that is generated for the chi-square goodness-of-fit test will depend on whether you have hypothesised that the proportion of cases expected in each group of the categorical variable is equal or unequal. For a complete explanation of the output you have to interpret for the chi-square goodness-of-fit test for both scenarios (as well as the testing of ), you can access our enhanced chi-square goodness-of-fit guide, as well as all of our other SPSS Statistics guides . Below we show you the SPSS Statistics output when you have hypothesised that the proportion of cases expected in each group of your categorical varaible is equal.
The table below, gift_type, provides the observed frequencies (Observed N) for each gift, as well as the expected frequencies (Expected N), which are the frequencies expected if the null hypothesis is true. The difference between the observed and expected frequencies is provided in the Residual column.
Published with written permission from SPSS Statistics, IBM Corporation.
The table below, Test Statistics, provides the actual result of the chi-square goodness-of-fit test. We can see from this table that our test statistic is statistically significant: &2(2) = 49.4, p & .0005. Therefore, we can reject the null hypothesis and conclude that there are statistically significant differences in the preference of the type of sign-up gift, with less people preferring the "Cuddly Toy" (N = 230) compared to either the "Gift Certificate" (N = 370) or the "Cinema Tickets" (N = 400).
Published with written permission from SPSS Statistics, IBM Corporation.
In our enhanced chi-square goodness-of-fit test guide, we show you how to write up the results
if you need to report this in a dissertation/thesis, assignment or research report.
We do this using the Harvard and APA styles. You can learn more about our enhanced content .spss探索性因素分析good of fitness 怎么算_百度知道
spss探索性因素分析good of fitness 怎么算
我有更好的答案
因素分析不计算这个指标
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色情、暴力
我们会通过消息、邮箱等方式尽快将举报结果通知您。spss&关联分析
刚接触数据挖掘，就免不了要听到沃尔玛的啤酒与尿布的典故，是不是真的就不知道了，确定的是这个典故用的就是关联分析。
一、概念理解
置信度、支持度、提升度是评价关联规则的三个重要指标。
样本100，条件A=》结果B，A：60，B40，同时发生A和B：30
条件支持度=P(A)=条件A60/样本100=0.6
结果支持度=P(B)=结果B40/样本100=0.4(在sas中称为预期置信度)
规则支持度=P(A&B)=30/100=0.3
规则置信度=P(B|A)=P(A&B)/P(A)=30/60=0.5,即同时发生的记录数除以样本数，
提升度=P(B|A)/P(B)=0.5/0.4=1.25
，注意不要混淆了条件支持度和规则支持度，网文好多只说支持度，实际上有的指的条件支持度、有的值规则支持度，我今天搞了一早上才恍然大悟，效率低啊，自我鄙视一下。
在spss的apriori的运行结果中还有部署能力的概念，观察了一下，发现：部署能力=条件支持度-规则支持度，就是说还有多少人有发展空间，比如有10人，符合条件的有7人，同时如何条件和结果的有4人，那部署能力就是7-4=3人了。
关联分析基本就是Apriori算法，没用过其他的。
apriori算法的具体实现就不说，暂时我也说不清楚，我只追求会用，不求甚解，只知道大概步骤就是：1、根据设置的条件支持度找出频繁项集；2、分析找出来的这些频繁项集，得出规则；3、找出大于或等于给定置信度的规则。
一般各个dm软件跑apriori算法的时候都需要设置：最小条件支持度，最小规则置信度，有的还需要设置最大前项数，spss的modeler就需要设置这三个。
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