Cochran's Q Test - NCSS
NCSS Statistical SoftwareNCSS.comChapter 521Cochran’s Q TestIntroductionThis procedure computes the non-parametric Cochran’s Q test for related categories where the response is binary.Cochran’s Q is used for testing k 2 or more matched sets, where a binary response (e.g. 0 or 1) is recorded fromeach category within each subject. Cochran’s Q tests the null hypothesis that the proportion of “successes” is thesame in all groups versus the alternative that the proportion is different in at least one of the groups.Cochran’s Q test is an extension of the McNemar test to a situation where there are more than two matchedsamples. When Cochran’s Q test is computed with only k 2 groups, the results are equivalent to those obtainedfrom the McNemar test (without continuity correction). Cochran’s Q is also considered to be a special case of thenon-parametric Friedman test, which is similar to repeated measures ANOVA and is used to detect differences inmultiple matched sets with numeric responses. When the responses are binary, the Friedman test becomesCochran’s Q test.This procedure also computes two-sided, pairwise multiple comparison tests that allow you to determine which ofthe individual groups are different if the null hypothesis in Cochran’s Q test is rejected. The individual alpha levelis adjusted using the Bonferroni method to control the overall experiment-wise error rate.This procedure is based on the results and formulas given in chapter 26 of Sheskin (2011). We refer you there foradditional information about Cochran’s Q test.Experimental DesignA typical design for this scenario involves N individuals where a binary measurement (e.g. 0 or 1) is made oneach individual for each of k categories, where k 2. Typical data might appear asSubjectCondition 1Condition 2Condition 00where, in this case, each subject responds to 3 different conditions with either a Yes (1) or No (0). In NCSS, theresponses may be coded as either text values (e.g. Yes, No) or numeric values (e.g. 0, 1).521-1 NCSS, LLC. All Rights Reserved.
NCSS Statistical SoftwareNCSS.comCochran’s Q TestTechnical DetailsSuppose we have k binary measurements on each of 𝑁𝑁 subjects (where the “subject” may be a set of matchedindividuals). Let 𝑌𝑌𝑖𝑖,𝑗𝑗 be the binary response from subject i in category j (i 1 to N, j 1 to k), with success 1and failure 0. Let the proportions, 𝜋𝜋1 , 𝜋𝜋2 , , 𝜋𝜋𝑘𝑘 , represent the proportion of “successes” in each of the kgroups.Cochran’s Q is used to test the null hypothesis𝐻𝐻0 : 𝜋𝜋1 𝜋𝜋2 𝜋𝜋𝑘𝑘versus the alternative𝐻𝐻𝐴𝐴 : 𝜋𝜋a 𝜋𝜋b for at least one pair 𝜋𝜋a , 𝜋𝜋b, with a b and 1 a, b k.In NCSS, these proportions, 𝜋𝜋a and 𝜋𝜋b, are displayed as percentages.AssumptionsThe Cochran’s Q test and associated multiple comparisons require the following assumptions:1. Responses are binary and from k matched samples.2. The subjects are independent of one another and were selected at random from a larger population.3. The sample size is sufficiently “large”. (As a rule of thumb, the number of subjects for which theresponses are not all 0’s or 1’s, n, should be 4 and nk should be 24. This assumption is not requiredfor the exact binomial McNemar test.)Cochran’s Q Test StatisticFor binary responses, 𝑌𝑌𝑖𝑖,𝑗𝑗 , in k matched groups from N subjects, the Cochran’s Q test statistic is computed as𝑄𝑄 where𝑘𝑘𝑁𝑁𝑗𝑗 1𝑖𝑖 12(𝑘𝑘 1)[𝑘𝑘𝑘𝑘 𝑇𝑇 2 ]𝑘𝑘𝑘𝑘 𝑅𝑅𝐶𝐶 𝑌𝑌𝑖𝑖,𝑗𝑗 𝑁𝑁𝑘𝑘𝑇𝑇 𝑌𝑌𝑖𝑖,𝑗𝑗 𝑖𝑖 1𝑁𝑁𝑗𝑗 1𝑘𝑘𝑅𝑅 𝑌𝑌𝑖𝑖,𝑗𝑗 𝑖𝑖 12𝑗𝑗 1For “large” samples, the test statistic, Q, is distributed as chi-square with 𝑘𝑘 1 degrees of freedom. As in theMcNemar test, only subjects who do not have the same response in all categories contribute to the overall Qstatistic.521-2 NCSS, LLC. All Rights Reserved.
NCSS Statistical SoftwareNCSS.comCochran’s Q TestThe p-value for the test is computed as2P-Value Pr 𝑄𝑄 𝜒𝜒1 𝛼𝛼,𝑘𝑘 1 2where 𝜒𝜒1 𝛼𝛼,𝑘𝑘 1is the value of the (1 𝛼𝛼) quantile of the chi-square distribution with 𝑘𝑘 1 degrees of freedom.Multiple ComparisonsWhen the null hypothesis of success proportion equality is rejected by Cochran’s Q test, you can proceed todetermine which of the groups are different by computing multiple pairwise comparisons.Pairwise tests between groups “a” and “b” test the null hypothesis𝐻𝐻0 : 𝜋𝜋a 𝜋𝜋bversus the alternative𝐻𝐻𝐴𝐴 : 𝜋𝜋a 𝜋𝜋bIn NCSS, these proportions, 𝜋𝜋a and 𝜋𝜋b, are displayed as percentages.NCSS provides two methods as described in chapter 26 of Sheskin (2011). The first method identifies theminimum required difference, MRD, needed to declare a pair of experimental conditions as significantly different.The second method simply employs pairwise McNemar tests among groups to find significant differences.Both multiple comparison methods use the Bonferroni alpha adjustment to control the overall experiment-wiseerror of the tests. The adjustment simply divides the overall required alpha, 𝛼𝛼, by the number of pairwise tests, c,whereThe alpha-level for each individual test, 𝛼𝛼𝑎𝑎𝑎𝑎𝑎𝑎 , is𝑐𝑐 𝑘𝑘(𝑘𝑘 1).2𝛼𝛼𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎.𝑐𝑐For example, if the desired overall alpha were 0.05 and three groups were being compared, then the individualalpha level for each test would be 0.05/[3(2)/2] 0.05/3 0.0167.Minimum Required DifferenceFor sufficiently large sample sizes (i.e. n 4 and nk 24, where n is the number of subjects for which theresponses are not all 0’s or 1’s), the minimum required difference in proportions for any pair of k experimentalgroups to be declared different is𝑀𝑀𝑀𝑀𝑀𝑀 𝑧𝑧𝑎𝑎𝑎𝑎𝑎𝑎 2 𝑘𝑘𝑘𝑘 𝑅𝑅 𝑁𝑁 2 𝑘𝑘(𝑘𝑘 1)where N, T, and R are defined as in Cochran’s Q statistic, with𝑁𝑁𝑘𝑘𝑖𝑖 1𝑗𝑗 1𝑇𝑇 𝑌𝑌𝑖𝑖,𝑗𝑗 𝑁𝑁𝑘𝑘𝑅𝑅 𝑌𝑌𝑖𝑖,𝑗𝑗 𝑖𝑖 12𝑗𝑗 1521-3 NCSS, LLC. All Rights Reserved.
NCSS Statistical SoftwareNCSS.comCochran’s Q Testand𝑧𝑧𝑎𝑎𝑎𝑎𝑎𝑎 is the value of the 1 𝛼𝛼𝑎𝑎𝑎𝑎𝑎𝑎 /2 quantile from the standard normal distribution.Two groups are declared to be significantly different with protected overall alpha, 𝛼𝛼, if their absolute difference inproportions is greater than MRD, that is ifMcNemar Tests 𝜋𝜋a 𝜋𝜋b 𝑀𝑀𝑀𝑀𝑀𝑀The McNemar test statistic for each pair of groups is computed aswhere𝑀𝑀 (𝑛𝑛1 𝑛𝑛2 )2𝑛𝑛1 𝑛𝑛2𝑛𝑛1 number of subjects where group “a” response 0 and group “b” response 1.𝑛𝑛2 number of subjects where group “a” response 1 and group “b” response 0.Large-Sample (Asymptotic)For sufficiently large sample sizes (i.e. n 4 and nk 24, where n is the number of subjects for which theresponses are not all 0’s or 1’s), the test statistic, M, is asymptotically distributed as chi-square with 1 degree offreedom. The p-value for the individual test with protected overall alpha, 𝛼𝛼, is computed as2 P-Value Pr 𝑀𝑀 𝜒𝜒1 𝛼𝛼𝑎𝑎𝑎𝑎𝑎𝑎 ,12is the value of the 1 𝛼𝛼𝑎𝑎𝑎𝑎𝑎𝑎 quantile of the chi-square distribution with 1 degree of freedom.where 𝜒𝜒1 𝛼𝛼𝑎𝑎𝑎𝑎𝑎𝑎 ,1Exact TestExact p-values for the McNemar test can also be computed by enumerating and summing individual binomialprobabilities of results more extreme than the observed. The exact test results are more accurate than theasymptotic test results because there is no approximation.A Note on the Power of the Two Multiple Comparison TestsThe Minimum Required Difference multiple comparison method uses all of the available information in the datain its calculations, but the multiple McNemar tests comparison method uses just the values from subjects whohave different responses for the two categories, not all of the data. For this reason some argue that the MinimumRequired Difference method is more powerful for finding differences than using multiple McNemar tests (seeNote #9 on page 1135 of Sheskin (2011)).Data StructureThe data may be entered in two formats, as shown in the examples below. The examples give the binary responsesof 12 subjects to each of 3 experimental conditions.The first format, shown in the first table below, puts the responses for each group in separate columns; that is,each column contains all responses for each condition. Each row corresponds to a single subject. This formatallows for the use of an additional optional frequency variable for summarized data.The second format, shown in the second table below, arranges the data so that all responses are entered in a singlecolumn. A grouping variable contains an index that gives the group (Condition 1, 2, or 3) to which each row ofdata belongs. The subject variable specifies the individual to which each response belongs. This second formatallows you to specify multiple response variables; a separate analysis is carried out for each response variable.521-4 NCSS, LLC. All Rights Reserved.
NCSS Statistical SoftwareNCSS.comCochran’s Q TestMultiple Response VariablesSubjectCondition 1Condition 2Condition 00Response Variable, Grouping Variable, and a Subject 21-5 NCSS, LLC. All Rights Reserved.
NCSS Statistical SoftwareNCSS.comCochran’s Q TestExample 1 – Multiple Response VariablesThis section presents an example of how to run an analysis on hypothetical data where the responses are stored inseparate columns, one for each category. In this example, a series of physical exams were given to 12 subjects. Ifthe subject passed the exam, a “1” was recorded, otherwise a “0” was recorded for failure. They wish to determineif there is a difference among the three exams.SetupTo run this example, complete the following steps:1Open the PhysExam1 example dataset From the File menu of the NCSS Data window, select Open Example Data. 2Select PhysExam1 and click OK.Specify the Cochran’s Q Test procedure options Find and open the Cochran’s Q Test procedure using the menus or the Procedure Navigator. The settings for this example are listed below and are stored in the Example 1 settings template. To loadthis template, click Open Example Template in the Help Center or File menu.OptionValueVariables TabInput Type . Multiple Response Variables, One Variable per GroupCategoryResponse Variables . Exam1-Exam33Run the procedure Click the Run button to perform the calculations and generate the output.Data Summary SectionData Summary Rows Processed:Rows with Missing Values:Rows Used in the Analysis:12012Responses:Groups (k):Subjects or Blocks (N): Number with responsesthat are not all equal (n):2 (0, 1)3 (Exam1, Exam2, Exam3)1211 (nk 33)** Large-sample (asymptotic) test results should be used only if n 4 and nk 24.Status: Conditions are met.The Data Summary report gives a summary description of the data used in the analysis. The summary indicatesthat the large-sample conditions (n 4 and nk 24) are met. One subject passed every exam, so his/her responseswill not contribute to the Cochran’s Q test statistic.521-6 NCSS, LLC. All Rights Reserved.
NCSS Statistical SoftwareNCSS.comCochran’s Q TestCombined Table SectionCombined Table ResponseVariableExam1Count% within Group0650.001650.00Total12100.00Exam2Count% within Group216.671083.3312100.00Exam3Count% within Group975.00325.0012100.00TotalCount% within Group1747.221952.7836100.00This report give the counts and percentages of each response within each category. Exam 2 has the highest passrate with 83.33%, while Exam 3 has the lowest pass rate with only 25%.Cochran’s Q Test SectionCochran's Q Test (Exam1 by Exam2 by Exam3)H0: The proportions of [Response "1"] in all groups are equal.H1: The proportion of [Response "1"] in at least one group is different.TestCochran's cProbLevel0.03461Reject H0at α 0.05?YesCochran’s Q test has a p-value of 0.03461, indicating that the success rate for at least one group is different fromthe others. Because this result is significant, we can proceed to consider the multiple comparison tests.Multiple Comparisons using Minimum Required Absolute DifferenceSectionMultiple Comparisons using Minimum Required Absolute Difference H0: πᵢ πⱼ (The proportions of [Response "1"] in the two groups are equal.)H1: πᵢ πⱼ (The proportions of [Response "1"] in the two groups are not equal.)Number of Comparisons (c): 3Comparison*Exam1 vs. Exam2Exam1 vs. Exam3Exam2 vs. Exam3πᵢ (%)50.0050.0083.33πⱼ (%)83.3325.0025.00AbsoluteDifference πᵢ - πⱼ .0254.0254.02Reject H0with Overallα 0.05?†NoNoYes* These tests should only be considered if the null hypothesis of equality was rejected by Cochran's Q Test.† Individual Comparison Alpha (Overall Alpha)/c 0.05/3 0.01667.These multiple comparison results indicate that exams 2 and 3 are significantly different from each other with anabsolute different of 58.33%.521-7 NCSS, LLC. All Rights Reserved.
NCSS Statistical SoftwareNCSS.comCochran’s Q TestMultiple Comparisons using the McNemar Test SectionMultiple Comparisons using the McNemar Test H0: πᵢ πⱼ (The proportions of [Response "1"] in the two groups are equal.)H1: πᵢ πⱼ (The proportions of [Response "1"] in the two groups are not equal.)Number of Comparisons (c): ject H0with Overallα 0.05?†NoNoComparison*Exam1 vs. Exam2πᵢ (%)50.00πⱼ (%)83.33TestAsymptoticBinomial ExactExam1 vs. Exam350.0025.00AsymptoticBinomial Exact1.800010.179710.37500NoNoExam2 vs. Exam383.3325.00AsymptoticBinomial Exact5.444410.019630.03906NoNo* These tests should only be considered if the null hypothesis of equality was rejected by Cochran's Q Test.† Individual Comparison Alpha (Overall Alpha)/c 0.05/3 0.01667.These multiple comparison results indicate that there are no pairs significantly different from one another. It’sinteresting to note here that no pairs were found to be different even though the overall Cochran’s Q test found asignificant difference and the multiple comparisons test using minimum required absolute difference found adifference between exams 2 and 3. This is likely due to the fact that McNemar test has lower overall powerbecause only discordant pairs are used in the computation of the test statistic. The other multiple comparisonprocedure uses all of the data and, thus, has more power.Plots SectionPlots Section This section provides a graphical representation of the counts and percentages for each response within eachcategory.521-8 NCSS, LLC. All Rights Reserved.
NCSS Statistical SoftwareNCSS.comCochran’s Q TestExample 2 – Multiple Response Variables with a FrequencyVariableContinuing from Example 1, we’ll now show you how to analyze data that has been tabulated (summarized) usinga frequency variable. The data for this example is exactly the same as that from Example 1 except that it has beensummarized. The variable “Count” indicates how many subjects correspond to each series of responses.SetupTo run this example, complete the following steps:1Open the PhysExam2 example dataset From the File menu of the NCSS Data window, select Open Example Data. 2Select PhysExam2 and click OK.Specify the Cochran’s Q Test procedure options Find and open the Cochran’s Q Test procedure using the menus or the Procedure Navigator. The settings for this example are listed below and are stored in the Example 2 settings template. To loadthis template, click Open Example Template in the Help Center or File menu.OptionValueVariables TabInput Type . Multiple Response Variables, One Variable per GroupCategoryResponse Variables . Exam1-Exam3Frequency (Count) Variable . Count3Run the procedure Click the Run button to perform the calculations and generate the output.OutputData Summary Rows Processed:Rows with Missing Values:Rows Used in the Analysis:808Responses:Groups (k):Subjects or Blocks (N): Number with responsesthat are not all equal (n):2 (0, 1)3 (Exam1, Exam2, Exam3)1211 (nk 33)** Large-sample (asymptotic) test results should be used only if n 4 and nk 24.Status: Conditions are met.521-9 NCSS, LLC. All Rights Reserved.
NCSS Statistical SoftwareNCSS.comCochran’s Q TestCochran's Q Test (Exam1 by Exam2 by Exam3)H0: The proportions of [Response "1"] in all groups are equal.H1: The proportion of [Response "1"] in at least one group is different.TestCochran's cProbLevel0.03461Reject H0at α 0.05?YesThe Data Summary report indicates that only 8 rows were processed this time (12 rows were processed inExample 1), but the number of subjects, 12, is the same as in Example 1 because some of the rows represent morethan one individual. The Cochran’s Q test results are exactly the same as in Example 1.521-10 NCSS, LLC. All Rights Reserved.
NCSS Statistical SoftwareNCSS.comCochran’s Q TestExample 3 – Responses All in a Single ColumnThis section presents an example of how to run an analysis on hypothetical data where the responses are stored ina single column with a separate subject and grouping variable. In this example, responses from 20 individuals arerecorded for each of 4 pain relief drugs (A, B, C, and D). One hour after the administration of each drug, eachsubject was asked whether the medication was effective for them in controlling pain. Responses were recorded aseither “Yes” or “No”. Drugs were administered in random order, each after a washout period.SetupTo run this example, complete the following steps:1Open the PainDrug example dataset From the File menu of the NCSS Data window, select Open Example Data. 2Select PainDrug and click OK.Specify the Cochran’s Q Test procedure options Find and open the Cochran’s Q Test procedure using the menus or the Procedure Navigator. The settings for this example are listed below and are stored in the Example 3 settings template. To loadthis template, click Open Example Template in the Help Center or File menu.OptionValueVariables TabInput Type . Response Variable(s), a Grouping Variable, and a SubjectVariableResponse Variable(s) . ResponseGrouping Variable . DrugSubject Variable. SubjectReports TabData Summary Report . UncheckedShow Combined Table . CheckedCounts . CheckedPercentages within Groups . CheckedPercentages within Responses . UncheckedOverall Total Percentages . UncheckedShow Individual Tables . UncheckedCochran's Q Test . CheckedMultiple Comparisons using . UncheckedMinimum Required Absolute DifferenceMultiple Comparisons using the . UncheckedMcNemar TestReport Options TabVariable Names . LabelsPlots TabShow Plots . Unchecked3Run the procedure Click the Run button to perform the calculations and generate the output.521-11 NCSS, LLC. All Rights Reserved.
NCSS Statistical SoftwareNCSS.comCochran’s Q TestOutputCombined Table Was the Drug Effective?DrugACount% within GroupNo735.00Yes1365.00Total20100.00BCount% within Group1155.00945.0020100.00CCount% within Group735.001365.0020100.00DCount% within Group1470.00630.0020100.00TotalCount% within Group3948.754151.2580100.00Cochran's Q Test (A by B by C by D)H0: The proportions of [Response "Yes"] in all groups are equal.H1: The proportion of [Response "Yes"] in at least one group is different.TestCochran's cProbLevel0.10121Reject H0at α 0.05?NoCochran’s Q test indicates that there is not a significant difference among the 4 medications. There is no reason tolook at the multiple comparison tests at this point.521-12 NCSS, LLC. All Rights Reserved.
NCSS Statistical SoftwareNCSS.comCochran’s Q TestExample 4 – Validation of Cochran’s Q and Multiple ComparisonTests using Sheskin (2011)Sheskin (2011) presents an example of computing Cochran’s Q test and the associated multiple comparisons inchapter 26, starting on page 1120. The data for the example consists of responses from 12 female subjects aboutwhether or not they would purchase an automobile manufactured by three different companies: Chenesco,Howasaki, and Gemini. The data for this validation example are contained in the dataset called “Sheskin”.Sheskin (2011) computes the group proportions for Response “1” in Chenesco, Howasaki, and Gemini as 0.25(25%), 0.75 (75%), and 0.25 (25%), respectively. They compute a Cochran’s Q value of 8.0. They do notcompute the p-value directly, but state that it is between 0.01 and 0.05. For the multiple comparison test using theminimum required absolute difference with an overall alpha level of 0.05, they find the minimum requireddifference to be 0.49 (or 49%). They conclude that Howasaki is different from both Chenesco and Gemini, eachwith differences of 50%, which are both greater than 49%. Sheskin further computes the binomial exact test pvalues for the multiple McNemar test comparisons as 0.0312 for Chenesco vs. Howasaki and 0.0704 forHowasaki vs. Gemini. Both are greater than the Bonferroni-adjusted alpha level of 0.0167 so both tests fail toreject the null hypothesis.The results from NCSS match all of these results, with slight difference due to rounding.SetupTo run this example, complete the following steps:1Open the Sheskin example dataset From the File menu of the NCSS Data window, select Open Example Data. Select Sheskin and click OK.2Specify the Cochran’s Q Test procedure options Find and open the Cochran’s Q Test procedure using the menus or the Procedure Navigator. The settings for this example are listed below and are stored in the Example 4 settings template. To loadthis template, click Open Example Template in the Help Center or File menu.OptionValueVariables TabInput Type . Multiple Response Variables, One Variable per GroupCategoryResponse Variables . Chenesco-Gemini3Run the procedure Click the Run button to perform the calculations and generate the output.521-13 NCSS, LLC. All Rights Reserved.
NCSS Statistical SoftwareNCSS.comCochran’s Q TestOutputCombined Table ResponseVariableChenescoCount% within Group0975.001325.00Total12100.00HowasakiCount% within Group325.00975.0012100.00GeminiCount% within Group975.00325.0012100.00TotalCount% within Group2158.331541.6736100.00Cochran's Q Test (Chenesco by Howasaki by Gemini)H0: The proportions of [Response "1"] in all groups are equal.H1: The proportion of [Response "1"] in at least one group is different.TestCochran's cProbLevel0.01832Reject H0at α 0.05?YesMultiple Comparisons using Minimum Required Absolute Difference H0: πᵢ πⱼ (The proportions of [Response "1"] in the two groups are equal.)H1: πᵢ πⱼ (The proportions of [Response "1"] in the two groups are not equal.)Number of Comparisons (c): 3Comparison*Chenesco vs. HowasakiChenesco vs. GeminiHowasaki vs. Geminiπᵢ (%)25.0025.0075.00πⱼ e48.8748.8748.87AbsoluteDifference πᵢ - πⱼ 50.000.0050.00Reject H0with Overallα 0.05?†YesNoYes* These tests should only be considered if the null hypothesis of equality was rejected by Cochran's Q Test.† Individual Comparison Alpha (Overall Alpha)/c 0.05/3 0.01667.Multiple Comparisons using the McNemar Test H0: πᵢ πⱼ (The proportions of [Response "1"] in the two groups are equal.)H1: πᵢ πⱼ (The proportions of [Response "1"] in the two groups are not equal.)Number of Comparisons (c): ject H0with Overallα 0.05?†YesNoComparison*Chenesco vs. Howasakiπᵢ (%)25.00πⱼ (%)75.00TestAsymptoticBinomial ExactChenesco vs. Gemini25.0025.00AsymptoticBinomial Exact0.000011.000001.00000NoNoHowasaki vs. Gemini75.0025.00AsymptoticBinomial Exact4.500010.033890.07031NoNo* These tests should only be considered if the null hypothesis of equality was rejected by Cochran's Q Test.† Individual Comparison Alpha (Overall Alpha)/c 0.05/3 0.01667.The results from NCSS match Sheskin (2011), with slight differences due to rounding. Key matched items arehighlighted in purple.521-14 NCSS, LLC. All Rights Reserved.
This procedure computes the non-parametric Cochran’s Q test for related categories where the response is binary. Cochran’s Q is used for testing k 2 or more matched sets , where a binary response (e.g. 0
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NCSS Statistical Software NCSS.com C Charts 258-3 NCSS, LLC. All Rights Reserved. is one sigma wide and is labeled A, B, or C, with the C zone being the closest to .
Standards for Social Studies, which described what NCSS expected pre-K-12 learners should know and be able to do through ten thematic standards (NCSS, 1994). While the 2016 committee continued the efforts of previous committees, the five standards and twenty-one
3. CNCT or RNCST certification required, plus training in performing advanced NCSs. A minimum of 4 years as a CNCT or RNCST performing NCSs in the patient setting, with at least a total of 5 years of experience in performing NCSs, and may have experience in the ICU. 4. Meets CPR certi
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Mar 24, 2020 · Example 2 To All Cochran Personnel - Since the arrival of COVID-19, we been faced with a new challenge. At Cochran, our safety plans and protocols have been updated to reflect our evolving response to this health threat. This COVID-19 Response Plan encompasses all we ha
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