# Fleiss' kappa in SPSS Statistics

## Introduction

Fleiss' kappa, κ (Fleiss, 1971; Fleiss et al., 2003), is a measure of **inter-rater agreement** used to determine the **level of agreement** between **two or more raters** (also known as "judges" or "observers") when the method of assessment, known as the **response variable**, is measured on a **categorical scale**. In addition, Fleiss' kappa is used when: (a) the **targets** being rated (e.g., patients in a medical practice, learners taking a driving test, customers in a shopping mall/centre, burgers in a fast food chain, boxes delivered by a delivery company, chocolate bars from an assembly line) are **randomly selected** from the population of interest rather than being specifically chosen; and (b) the raters who assess these targets are **non-unique** and are **randomly selected** from a larger population of raters. We explain these three concepts – random selection of targets, random selection of raters and non-unique raters – as well as the use of Fleiss' kappa in the example below.

As an example of how Fleiss' kappa can be used, imagine that the head of a large medical practice wants to determine whether doctors at the practice agree on when to prescribe a patient antibiotics. Therefore, **four doctors** were **randomly selected** from the **population** of **all doctors** at the large medical practice to examine a **patient** complaining of an illness that might require antibiotics (i.e., the "four randomly selected doctors" are the **non-unique raters** and the "patients" are the **targets** being assessed). The four randomly selected doctors had to decide whether to "prescribe antibiotics", "request the patient come in for a follow-up appointment" or "not prescribe antibiotics" (i.e., where "prescribe", "follow-up" and "not prescribe" are **three categories** of the **nominal response variable**, antibiotics prescription decision). This process was repeated for 10 patients, where **on each occasion**, four doctors were randomly selected from all doctors at the large medical practice to examine one of the 10 patients. The 10 patients were also **randomly selected** from the **population** of patients at the large medical practice (i.e., the "population" of patients at the large medical practice refers to **all patients** at the large medical practice). The **level of agreement** between the four non-unique doctors for each patient is analysed using **Fleiss' kappa**. Since the results showed a very good strength of agreement between the four non-unique doctors, the head of the large medical practice feels somewhat confident that doctors are prescribing antibiotics to patients in a similar manner. Furthermore, an analysis of the **individual kappas** can highlight any differences in the level of agreement between the four non-unique doctors for **each category** of the nominal response variable. For example, the individual kappas could show that the doctors were in **greater agreement** when the decision was to "prescribe" or "not prescribe", but in **much less agreement** when the decision was to "follow-up". It is also worth noting that even if raters strongly agree, this does not mean that their decision is correct (e.g., the doctors could be misdiagnosing the patients, perhaps prescribing antibiotics too often when it is not necessary). This is something that you have to take into account when reporting your findings, but it cannot be measured using Fleiss' kappa.

In this **introductory guide** to Fleiss' kappa, we first describe the basic requirements and assumptions of Fleiss' kappa. These are not things that you will test for statistically using SPSS Statistics, but you **must** check that your study design **meets** these basic requirements/assumptions. If your study design does **not** meet these basic requirements/assumptions, Fleiss' kappa is the **incorrect** statistical test to analyse your data. However, there are often other statistical tests that can be used instead. Next, we set out the example we use to illustrate how to carry out Fleiss' kappa using **SPSS Statistics**. This is followed by the Procedure section, where we illustrate the simple 6-step **Reliability Analysis...** procedure that is used to carry out **Fleiss' kappa** in SPSS Statistics. Next, we explain how to interpret the main results of Fleiss' kappa, including the **kappa value**, **statistical significance** and **95% confidence interval**, which can be used to assess the agreement between your two or more non-unique raters. We also discuss how you can assess the **individual kappas**, which indicate the level of agreement between your two or more non-unique raters for each of the categories of your response variable (e.g., indicating that doctors were in greater agreement when the decision was the "prescribe" or "not prescribe", but in much less agreement when the decision was to "follow-up", as per our example above). In the final section, Reporting, we explain the information you should include when reporting your results. A Bibliography and Referencing section is included at the end for further reading. To continue with this introductory guide, go to the next section.

###### SPSS Statistics

**Basic requirements** and **assumptions** of Fleiss' kappa

Fleiss' kappa is **just one of many** statistical tests that can be used to assess the inter-rater agreement between two or more raters when the method of assessment (i.e., the response variable) is measured on a categorical scale (e.g., Scott, 1955; Cohen, 1960; Fleiss, 1971; Landis and Koch, 1977; Gwet, 2014). Each of these different statistical tests has **basic requirements** and **assumptions** that **must be met** in order for the test to give a **valid/correct** result. Fleiss' kappa is no exception. Therefore, you **must** make sure that your study design **meets** the basic requirements/assumptions of Fleiss' kappa. If your study design does **not** meet these basic requirements/assumptions, Fleiss' kappa is the **incorrect** statistical test to analyse your data. However, there are often other statistical tests that can be used instead. In this section, we set out **six** basic requirements/assumptions of Fleiss' kappa.

**Requirement/Assumption #1:**The**response variable**that is being assessed by your two or more raters is a**categorical variable**. (i.e., you have an**ordinal**or**nominal**variable). A**categorical variable**can be either a**nominal variable**or an**ordinal variable**, but Fleiss' kappa does not take into account the ordered nature of an ordinal variable. Examples of**nominal variables**include gender (with two categories: "male" and "female"), ethnicity (with three categories: "African American", "Caucasian" and "Hispanic"), transport type (four categories: "cycle", "bus", "car" and "train"), and profession (five categories: "consultant", "doctor", "engineer", "pilot" and "scientist"). Examples of**ordinal variables**include educational level (e.g., with three categories: "high school", "college" and "university"), physical activity level (e.g., with four categories: "sedentary", "low", "moderate" and "high"), revision time (e.g., with five categories: "0-5 hours", "6-10 hours", "11-15 hours", "16-20 hours" and "21-25 hours"), Likert items (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 explaining how much a customer liked a product, ranging from "Not very much" to "Yes, a lot"). If these terms are unfamiliar to you, please see our guide on Types of Variable for further help.

For example, two raters could be assessing whether a patient's mole was "normal" or "suspicious" (i.e., two categories); four raters could be assessing whether the quality of service provided by a customer service agent was "above average", "average" or "below average" (i.e., three categories); or three raters could be assessing whether a person's physical activity level should be considered "sedentary", "low", "medium" or "high" (i.e., four categories).**Requirement/Assumption #2:**The**two or more categories**of the**response variable**that are being assessed by the raters must be**mutually exclusive**, which has two components. First, the two or more categories are mutually exclusive because**no categories can overlap**. For example, a rater, such as a dermatologist (i.e., a skin specialist), could only consider a patient's mole to be "normal"**or**"suspicious". The mole**cannot**be "normal"**and**"suspicious" at the same time. Second, the two or more categories are mutually exclusive because**only one category can be selected for each response**. For example, when assessing the patient's mole, the dermatologist must judge the mole to be either "normal"**or**"suspicious". The dermatologist**cannot**select more than one category for each patient.Note: If you have a study design where the categories of your response variable are

**not**mutually exclusive, Fleiss' kappa is**not**the correct statistical test. If you would like us to let you know when we can add a guide to the site to help with this scenario, please contact us.**Requirement/Assumption #3:**The**response variable**that is being assessed must have the**same number of categories for each rater**. In other words, all the raters must use the same rating scale. For example, if one rater was asked to assess whether the quality of service provided by a customer service agent was "above average", "average" or "below average" (i.e., three categories), a second rater**cannot**only be given two options: "above average" and "below average" (i.e., two categories).Note: If you have a study design where each response variable does

**not**have the same number of categories, Fleiss' kappa is**not**the correct statistical test. If you would like us to let you know when we can add a guide to the site to help with this scenario, please contact us.**Requirement/Assumption #4:**The**two or more raters**are**non-unique**. As Fleiss et al. (2003, pp. 610-611) state: "The raters responsible for rating one subject are not assumed to be the same as those responsible for rating another".

To understand this further, as well as the difference between**non-unique**and**unique**raters, imagine a study where a large health organisation wants to determine the extent to which radiographers agree on the severity of a type of back injury, where severity is assessed on a scale from "Grade I" (the most severe), through to "Grade II", "Grade III" and "Grade IV" (the least severe). To assess severity, radiographers look at magnetic resonance imaging (MRI) slides that have been taken of the patient's back and are asked to make a judgement about whether the patient's back injury severity is "Grade I", "Grade II", "Grade III" or "Grade IV" (i.e., the four categories of the ordinal variable, "Back Injury Severity").

Now, imagine that in this study the large health organisation wants to determine the extent to which five radiographers (i.e.,**five raters**) agree on the severity of back pain injuries. Furthermore, a total of 20 MRI slides are used (i.e., one MRI slide shows the back injury for one patient). Also, the radiographers who take part in the study are**randomly selected**from**all 50 radiographers**in the large health organisation (i.e., the total**population**of radiographers in the organisation). If the**same**five radiographers assessed**all**20 MRI slides, these five radiographers would be described as**unique raters**. However, if a**different set/group**of radiographers rated**each**of the 20 MRI slides, these five radiographers would be described as**non-unique raters**(i.e., five randomly selected radiographers out of the 50 radiographers in the large organisation view and rate the first MRI slide, then another five randomly selected radiographers rate the second MRI slide, and so on, until all 20 MRI slides have been rated). Fleiss' kappa measures the level of agreement between**non-unique raters**.Note 1: As we mentioned above, Fleiss et al. (2003, pp. 610-11) stated that "the raters responsible for rating one subject are not assumed to be the same as those responsible for rating another". In this sense, there is no assumption that the five radiographers who rate one MRI slide are the same radiographers who rate another MRI slide. However, even though the five radiographers are randomly sampled from all 50 radiographers at the large health organisation, it is possible that some of the radiographers will be selected to rate more than one of the 20 MRI slides.

Note 2: If you have a study design where the two or more raters are

**not**non-unique (i.e., they are**unique**), Fleiss' kappa is**not**the correct statistical test. If you would like us to let you know when we can add a guide to the site to help with this scenario, please contact us.**Requirement/Assumption #5:**The**two or more raters**are**independent**, which means that one rater's judgement does**not**affect another rater's judgement. For example, if the radiographers in the example above discuss their assessment of the MRI slides before recording their response or perhaps are simply in the same room when they make their assessment, this could influence the assessment they make. It is important that the potential for such**bias**is**removed**from the study design**as much as possible**.**Requirement/Assumption #6:**The**targets**being rated (e.g., patients in a medical practice, learners taking a driving test, customers in a shopping mall/centre, burgers in a fast food chain, boxes delivered by a delivery company, chocolate bars from an assembly line) ) are**randomly selected**from the**population**of interest rather than being specifically chosen.

For example, the randomly selected, non-unique radiographers in the example above rated**20 MRI slides**. These 20 MRI slides were**randomly selected**from**all**MRI slides of patients' backs at the large health organisation (i.e., this is the total**population**of MRI slides from which the 20 MRI slides are randomly selected). The MRI slides from which 20 were selected were all of the same type. This is important because if some of MRI slides were taken with the latest equipment, whilst other MRI slides were taken with old equipment where the image was less clear, this will introduce**bias**. As another example, consider our first example of four randomly selected doctors in a large medical practice who assessed whether**10 patients**should be prescribed antibiotics. These 10 patients had to be**randomly selected**from the total**population**of patients at the large medical practice (i.e., the "population" of patients at the large medical practice refers to**all patients**at the large medical practice).Note: If you have a study design where the targets being rated are

**not**randomly selected, Fleiss' kappa is**not**the correct statistical test. If you would like us to let you know when we can add a guide to the site to help with this scenario, please contact us.

Therefore, before carrying out a Fleiss' kappa analysis, it is **critical** that you **first** check whether your study design meets these **six** basic requirements/assumptions. If your study design does **not** met requirements/assumptions **#1** (i.e., you have a categorical response variable), **#2** (i.e., the two or more categories of this response variable are mutually exclusive), **#3** (i.e., the same number of categories are assessed by each rater), **#4** (i.e., the two or more raters are non-unique), **#5** (i.e., the two or more raters are independent), and **#6** (i.e., targets are randomly sample from the population), Fleiss' kappa is the **incorrect** statistical test to analyse your data.

When you are confident that your study design has met all **six** basic requirements/assumptions described above, you can carry out a Fleiss' kappa analysis. In the sections that follow we show you how to do this using SPSS Statistics, based on the example we set out in the next section: **Example** used in this guide.

###### SPSS Statistics

**Example** used in this guide

A local police force wanted to determine whether police officers with a similar level of experience were able to detect whether the behaviour of people in a clothing retail store was "**normal**", "**unusual, but not suspicious**" or "**suspicious"**. In particular, the police force wanted to know the extent to which its police officers agreed in their assessment of individuals' behaviour fitting into one of these three categories (i.e., where the three categories were "normal", "unusual, but not suspicious" or "suspicious" behaviour). In other words, the police force wanted to assess police officers' **level of agreement**.

To assess police officers' level of agreement, the police force conducted an experiment where **three police officers** were **randomly selected** from **all available police officers** at the local police force of approximately 100 police officers. These three police offers were asked to view a video clip of a **person** in a clothing retail store (i.e., the people being viewed in the clothing retail store are the **targets** that are being rated). This video clip captured the movement of just **one** individual from the moment that they entered the retail store to the moment they exited the store. At the end of the video clip, **each** of the three police officers was asked to record (i.e., **rate**) whether they considered the personâ€™s behaviour to be "**normal**", "**unusual, but not suspicious**" or "**suspicious**" (i.e., where these are **three categories** of the **nominal response variable**, behavioural_assessment). Since there must be **independence of observations**, which is one of the assumptions/basic requirements of Fleiss' kappa, as explained earlier, each police officer rated the video clip in a room where they could not influence the decision of the other police officers to avoid possible bias.

This process was repeated for a total of **23 video clips** where: (a) each video clip was different; and (b) a new set of three police officers were **randomly selected** from all 100 police officers each time (i.e., three police officers were randomly selected to assess **video clip #1**, another three police officers were randomly selected to assess **video clip #2**, another three police officers were randomly selected to assess **video clip #3**, and so forth, until all 23 video clips had been rated). Therefore, the police officers were considered **non-unique raters**, which is one of the assumptions/basic requirements of Fleiss' kappa, as explained earlier. After all of the 23 video clips had been rated, Fleiss' kappa was used to compare the ratings of the police officers (i.e., to compare police officers' **level of agreement**).

Note: Please note that this is a fictitious study being used to illustrate how to carry out and interpret Fleiss' kappa.

###### SPSS Statistics

**SPSS Statistics procedure** to carry out a Fleiss' kappa analysis

The procedure to carry out **Fleiss' kappa**, including **individual kappas**, is **different** depending on whether you have **version 26** or the **subscription version** of SPSS Statistics **or** **version 25 or earlier**. If you are unsure which version of SPSS Statistics you are using, see our guide: Identifying your version of SPSS Statistics. In this section, we show you how to carry out Fleiss' kappa using the 6-step **Reliability Analysis...** procedure in SPSS Statistics, which is an "built-in" procedure that you can use if you have SPSS Statistics **version 26** (or the **subscription version** of SPSS Statistics). If you have SPSS Statistics **version 25 or earlier**, please see the **Note** below:

Note: If you have SPSS Statistics **version 25 or earlier**, you **cannot** use the **Reliability Analysis...** procedure. However, you **can** use the **FLEISS KAPPA** procedure, which is a simple 3-step procedure. Unfortunately, **FLEISS KAPPA** is **not** a built-in procedure in SPSS Statistics, so you need to first **download** this program as an "extension" using the **Extension Hub** in SPSS Statistics. You can then run the **FLEISS KAPPA** procedure using SPSS Statistics.

Therefore, if you have SPSS Statistics **version 25 or earlier**, our enhanced guide on **Fleiss' kappa** in the members' section of Laerd Statistics includes a page dedicated to showing how to download the **FLEISS KAPPA** extension from the **Extension Hub** in SPSS Statistics and then carry out a Fleiss' kappa analysis using the **FLEISS KAPPA** procedure. You can access this enhanced guide by subscribing to Laerd Statistics.

- Click
on the top menu, as shown below:__A__nalyze > Sc__a__le >__R__eliability Analysis...Published with written permission from SPSS Statistics, IBM Corporation.

You will be presented with the following

**Reliability Analysis**dialogue box:Published with written permission from SPSS Statistics, IBM Corporation.

- Transfer your two or more variables, which in our example are non_unique_rater_1, non_unique_rater_2 and non_unique_rater_3, into the R
__a__tings: box, using the bottom button. You will end up with a screen similar to the one below:Published with written permission from SPSS Statistics, IBM Corporation.

- Click on the button. You will be presented with the
**Reliability Analysis: Statistics**dialogue box, as shown below:Published with written permission from SPSS Statistics, IBM Corporation.

- Select the Display agreement on individual categories option in the –Interrater Agreement: Fleiss' Kappa– area, as shown below:
Published with written permission from SPSS Statistics, IBM Corporation.

- Click on the button. This will return you to the
**Reliability Analysis...**dialogue box. - Click on the button to generate the output for Fleiss' kappa.

Now that you have run the **Reliability Analysis...** procedure, we show you how to interpret the results from a Fleiss' kappa analysis in the next section.

###### SPSS Statistics

**Interpreting** the results from a Fleiss' kappa analysis

Fleiss' kappa (κ) is a statistic that was designed to take into account **chance agreement**. In terms of our example, even if the police officers were to guess randomly about each individual's behaviour, they would end up agreeing on some individual's behaviour simply by chance. However, you do not want this chance agreement affecting your results (i.e., making agreement appear better than it actually is). Therefore, instead of measuring the overall proportion of agreement, Fleiss' kappa measures the proportion of agreement **over and above** the agreement expected by chance (i.e., over and above chance agreement).

After carrying out the **Reliability Analysis...** procedure in the previous section, the following **Overall Kappa** table will be displayed in the **IBM SPSS Statistics Viewer**, which includes the value of Fleiss' kappa and other associated statistics:

Published with written permission from SPSS Statistics, IBM Corporation.

The value of Fleiss' kappa is found under the "**Kappa**" column of the table, as highlighted below:

Published with written permission from SPSS Statistics, IBM Corporation.

You can see that Fleiss' kappa is **.557**. This is the proportion of agreement **over and above** chance agreement. Fleiss' kappa can range from **-1 to +1**. A **negative value** for kappa (κ) indicates that agreement between the two or more raters was **less than** the agreement expected by chance, with **-1** indicating that there was **no observed agreement** (i.e., the raters did not agree on anything), and **0 (zero)** indicating that agreement was **no better than chance**. However, negative values rarely actually occur (Agresti, 2013). Alternately, kappa values **increasingly greater that 0 (zero)** represent **increasing better-than-chance agreement** for the two or more raters, to a maximum value of **+1**, which indicates **perfect agreement** (i.e., the raters agreed on everything).

There are **no** rules of thumb to assess how good our kappa value of **.557** is (i.e., how strong the level of agreement is between the police officers). With that being said, the following classifications have been suggested for assessing how good the strength of agreement is when based on the value of **Cohen's kappa coefficient**. The guidelines below are from Altman (1999), and adapted from Landis and Koch (1977):

Value of κ | Strength of agreement |
---|---|

< 0.20 | Poor |

0.21-0.40 | Fair |

0.41-0.60 | Moderate |

0.61-0.80 | Good |

0.81-1.00 | Very good |

Table: Classification of Cohen's kappa. |

Using this classification scale, since Fleiss' kappa (κ)=.557, this represents a **moderate** strength of agreement. However, the value of kappa is heavily dependent on the **marginal distributions**, which are used to calculate the level (i.e., proportion) of chance agreement. As such, the value of kappa will differ depending on the marginal distributions. This is one of the greatest weaknesses of Fleiss' kappa. It means that you **cannot compare** one Fleiss' kappa to another **unless the marginal distributions are the same**.

It is also good to report a **95% confidence interval** for Fleiss' kappa. To do this, you need to consult the "**Lower 95% Asymptotic CI Bound**" and the "**Upper 95% Asymptotic CI Bound**" columns, as highlighted below:

Published with written permission from SPSS Statistics, IBM Corporation.

You can see that the 95% confidence interval for Fleiss' kappa is **.389** to **.725**. In other words, we can be **95% confident that the true population value** of Fleiss' kappa is **between** .389 and .725.

We can also report whether Fleiss' kappa is **statistically significant**; that is, whether Fleiss' kappa is different from 0 (zero) in the **population** (sometimes described as being statistically significantly different from zero). These results can be found under the "**Z**" and "**P Value**" columns, as highlighted below:

Published with written permission from SPSS Statistics, IBM Corporation.

You can see that the **p****-value** is report as **.000**, which means that **p****< .0005** (i.e., the *p*-value is **less than** .0005). If **p****< .05** (i.e., if the *p*-value is **less than** .05), you **have** a statistically significant result and your Fleiss' kappa coefficient **is** statistically significantly different from 0 (zero). If **p****> .05** (i.e., if the *p*-value is **greater than** .05), you do **not** have a statistically significant result and your Fleiss' kappa coefficient is **not** statistically significantly different from 0 (zero). In our example, *p* =**.000**, which actually means *p* < .0005 (see the note below). Since a *p*-value less than .0005 is **less than** .05, our kappa (κ) coefficient **is** statistically significantly different from 0 (zero).

Note: If you see SPSS Statistics state that the "**P Value**" is ".000", this actually means that *p* < .0005; it does not mean that the significance level is actually zero. Where possible, it is preferable to state the actual *p*-value rather than a greater/less than *p*-value statement (e.g., *p* =.023 rather than *p* < .05, or *p* =.092 rather than *p* > .05). This way, you convey more information to the reader about the level of statistical significance of your result.

However, it is important to mention that because agreement will rarely be only as good as chance agreement, the statistical significance of Fleiss' kappa is **less important** than reporting a 95% confidence interval.

Therefore, we know so far that there was moderate agreement between the officers' judgement, with a kappa value of .557 and a 95% confidence interval (CI) between .389 and .725. We also know that Fleiss' kappa coefficient was statistically significant. However, we can go one step further by interpreting the **individual kappas**.

The individual kappas are simply Fleiss' kappa calculated for **each** of the categories of the response variable **separately** against all other categories **combined**. In our example, the following comparisons would be made:

**A.**The "Normal" behaviour category would be**compared**to the "Unusual, but not suspicious" behaviour category**and**the "Suspicious" behaviour category**combined**.**B.**The "Unusual, but not suspicious" behaviour category would be**compared**to the "Normal" behaviour category**and**the "Suspicious" behaviour category**combined**.**C.**The "Suspicious" behaviour category would be**compared**to the "Normal" behaviour category**and**the "Unusual, but not suspicious" behaviour category**combined**.

We can use this information to assess police officers' level of agreement when rating each category of the response variable. For example, these **individual kappas** indicate that police officers are in **better agreement** when categorising individual's behaviour as either normal or suspicious, but **far less in agreement** over who should be categorised as having unusual, but not suspicious behaviour. These **individual kappa** results are displayed in the **Kappas for Individual Categories** table, as shown below:

Published with written permission from SPSS Statistics, IBM Corporation.

If you are unsure how to interpret the results in the **Kappas for Individual Categories** table, our enhanced guide on **Fleiss' kappa** in the members' section of Laerd Statistics includes a section dedicated to explaining how to interpret these **individual kappas.** You can access this enhanced guide by subscribing to Laerd Statistics. However, to continue with this introductory guide, go to the next section where we explain how to report the results from a Fleiss' kappa analysis.

###### SPSS Statistics

**Reporting** the results from a Fleiss' kappa analysis

When you report the results of a Fleiss' kappa analysis, it is good practice to include the following information:

**A.**An**introduction**to the analysis you carried out, which includes: (a) the**statistical test**being used to analyse your data (i.e.,**Fleiss' kappa**); (b) the**raters**whose level of agreement is being assessed (e.g., police officers in our example); (c) the**targets**who are being rated (e.g., individuals in a clothing retail store in our example); and (d) the**categories**of your**response variable**(e.g., the "Normal", "Unusual, but not suspicious", and "Suspicious" categories in our example), to highlight that the response variable is a**categorical variable**, as discussed in Requirement/Assumption #1.**B.**Information about your**sample**, including: (a) the**number**of**non-unique raters**and the**population**from which these were**randomly selected**; and (b) the**number**of**targets**and the**population**from which these were**randomly selected**. Including terms such as**non-unique**and**randomly selected**indicates to the reader that Fleiss' kappa has been used appropriately, as per Requirement/Assumption #4 and Requirement/Assumption #6 respectively.**C.**A statement to indicate how you helped to ensure**independence of observations**in order to reduce potential bias, as discussed in Requirement/Assumption #5.**D.**A statement to indicate that: (a) each rater was presented with the**same number of categories**; and (b) the categories were**mutually exclusive**, as per Requirement/Assumption #3 and Requirement/Assumption #2 respectively.**E.**The results from the**Fleiss' kappa**analysis, including: (a) the**Fleiss' kappa coefficient**,**κ**(i.e., shown under the "**Kappa**" column in the**Overall Kappa**table), together with the**95% confidence interval (CI)**(i.e., shown under the "**Lower 95% Asymptotic CI Bound**" and the "**Upper 95% Asymptotic CI Bound**" columns); and (b) the**p****-value**given for the test (i.e., shown under the "**P Value**" column). You can also consider including (c), the**level of agreement**in terms of a general guideline, such as the classifications of "poor", "fair", "moderate", "good" or "very good" agreement, suggested by Altman (1999) for the**Cohen's kappa coefficient**, and adapted from Landis and Koch (1977).**F.**The results from the**individual kappa**analysis, including: (a) the**Fleiss' kappa coefficient**,**κ**, for**each category**of the response variable**separately**against all other categories**combined**; and (b) a statement of the**relative level of agreement**between raters for**each category**.**G.**A**table**of your results, showing**how**the raters scored for each category of the response variable, assuming that data is**anonymous**and meets other relevant**ethical standards**of care. Providing such a table is important, where possible, because it allows others to: (a) check that you have carried out your analysis**correctly**; and (b) analyse your data using**alternative methods**of**inter-rater agreement**.

In the example below, we show how to report the results from your Fleiss' kappa analysis in line with **five** of the **seven** reporting guidelines above (i.e., **A**, **B**, **C**, **D** and **E**). If you are interested in understanding how to report your results in line with the **two** remaining reporting guidelines (i.e., **F**, in terms of **individual kappas**, and **G**, using a **table**), we show you how to do this in our enhanced guide on **Fleiss' kappa** in the members' section of Laerd Statistics. You can access this enhanced guide by subscribing to Laerd Statistics. However, if you are simply interested in reporting guidelines **A to E**, see the reporting example below:

- General

Fleiss' kappa was run to determine if there was agreement between police officers' judgement on whether 23 individuals in a clothing retail store were exhibiting either normal, unusual but not suspicious, or suspicious behaviour, based on a video clip showing each shopper's movement through the clothing retail store. Three non-unique police officers were chosen at random from a group of 100 police officers to rate each individual. Each police officer rated the video clip in a separate room so they could not influence the decision of the other police officers. When assessing an individual's behaviour in the clothing retail store, each police officer could select from only one of the three categories: "normal", "unusual but not suspicious" or "suspicious behaviour". The 23 individuals were randomly selected from all shoppers visiting the clothing retail store during a one-week period. Fleiss' kappa showed that there was moderate agreement between the officers' judgements, κ=.557 (95% CI, .389 to .725), *p* < .0005.

Note: When you report your results, you may **not** always include **all** seven reporting guidelines mentioned above (i.e., **A**, **B**, **C**, **D**, **E**, **F** and **G**) in the "**Results**" section, whether this is for an assignment, dissertation/thesis or journal/clinical publication. Some of the seven reporting guidelines may be included in the "**Results**" section, whilst others may be included in the "**Methods/Study Design**" section. However, we would recommend that **all** seven are included in **at least one** of these sections.

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**Bibliography** and **Referencing**

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## Reference **this article**

Laerd Statistics (2019). Fleiss' kappa using SPSS Statistics. *Statistical tutorials and software guides*. Retrieved **Month**, **Day**, **Year**, from https://statistics.laerd.com/spss-tutorials/fleiss-kappa-in-spss-statistics.php

For example, if you viewed this guide on **19 ^{th} October 2019**, you would use the following reference:

Laerd Statistics (2019). Fleiss' kappa using SPSS Statistics. *Statistical tutorials and software guides*. Retrieved October, 19, 2019, from https://statistics.laerd.com/spss-tuorials/fleiss-kappa-in-spss-statistics.php