SEM Series (2016) 2. Data Screening

Summary

This video script is a comprehensive guide to data screening in statistical analysis. It covers essential steps like identifying missing data, handling unengaged responses, managing outliers, and checking for skewness and kurtosis. The tutorial uses a practical approach with examples from a dataset, demonstrating how to clean and prepare data for analysis in SPSS. It also discusses the implications of each step and provides tips for reporting the findings in research.

Takeaways

• 🔍 The video script discusses a systematic approach to data screening, focusing on handling missing data, identifying unengaged responses, and managing outliers in continuous variables.
• 🗂️ The process starts with organizing the dataset by removing unnecessary variables and keeping track of IDs to ensure data integrity.
• 📊 To detect missing data, the script suggests using Excel to count blank cells and sort the data to identify rows with a high number of missing values, which may be candidates for removal.
• 🚫 The script identifies unengaged responses by looking for patterns such as identical answers across all questions or extremely short survey completion times, indicating a lack of attention from respondents.
• 📈 For continuous variables, the script recommends checking for outliers using scatter plots and replacing extreme values with the mean or median to maintain data integrity.
• 📉 The video also covers the importance of dealing with skewness and kurtosis, suggesting the use of conditional formatting in Excel to highlight values that exceed certain thresholds, indicating potential issues with data distribution.
• 📝 It's advised to report the data screening process in a research paper, including details about missing data imputation, removal of unengaged responses, and handling of outliers.
• ❌ The script emphasizes the need to be cautious when dealing with a high percentage of missing data in a variable, as it can dilute the potency of the variable and affect analysis outcomes.
• 🔢 The process of imputing missing values differs for ordinal and continuous scales, with medians used for ordinal scales and means for continuous scales, to maintain the integrity of the data.
• ⏱️ The video mentions the use of attention traps and reverse-coded items as strategies to identify unengaged respondents, which can help in cleaning the dataset.

Q & A

What is the first step in data screening as described in the script?

The first step in data screening is to check for missing data in the rows of the dataset.

How does the script suggest handling unengaged responses in a survey?

Unengaged responses can be detected by visually inspecting data, using attention traps, recording time elapsed for survey completion, employing reverse-coded items, or identifying respondents who give the same response to every question.

What is an 'attention trap' in the context of survey data?

An 'attention trap' is a question in a survey designed to identify respondents who are not paying attention, such as asking them to select a specific, counterintuitive answer to a straightforward question.

How can outliers on continuous variables be identified and addressed according to the script?

Outliers on continuous variables can be identified using scatter plots or by calculating the standard deviation of the values. Addressing them may involve removing the outlier if it's due to an erroneous response or imputing a value like the mean or median for the variable.

What is the purpose of using reverse-coded items in a survey?

Reverse-coded items are used to detect unengaged respondents. They are negatively worded questions where the expected answer should be the opposite of what is typically agreed upon, helping to identify respondents who may not be paying attention.

Why is it important to check for missing data before conducting further analysis?

Checking for missing data is important because it can affect the validity and reliability of the analysis. Missing data can lead to biased results or require imputation, which should be reported in the study.

What is the recommended approach for handling missing data in ordinal scales versus continuous scales?

For ordinal scales, the median is recommended for imputation, while for continuous scales, the mean is more appropriate. This distinction is made because ordinal scales do not have actual mean values between the scale points.

How does the script suggest detecting respondents who may not be engaged in the survey?

The script suggests detecting unengaged respondents by looking for those who provide the same response to every question (liners), those who complete the survey in an unrealistically short amount of time, or those who do not correctly answer attention trap questions.

What is the significance of checking for skewness and kurtosis in the data?

Checking for skewness and kurtosis is important to assess the normality of the data distribution. Extreme values can indicate that the data may not meet the assumptions required for certain statistical tests, potentially affecting the analysis and its interpretation.

How should the presence of outliers be reported in a research paper according to the script?

The presence of outliers should be reported by noting the specific variables affected, the nature of the outliers (e.g., extremely high or low values), and the actions taken to address them, such as removal or correction of the values.

Outlines

00:00

📊 Data Screening and Missing Data Analysis

The paragraph discusses the initial steps of data screening, focusing on identifying rows with missing data in a dataset. It explains the process of removing rows with unengaged responses and handling outliers in continuous variables. The speaker demonstrates how to access and organize the data, checking for missing values, and deciding which variables to keep or remove based on their relevance to the study. The importance of keeping a unique identifier for each row is emphasized to maintain data integrity during sorting or other manipulations.

05:01

🔢 Imputing Missing Values and Dealing with Unengaged Responses

This section delves into the process of imputing missing values in the dataset. The speaker explains the difference between ordinal and continuous variables and the appropriate methods for imputing missing data for each type. For ordinal variables, the median is used, while the mean is suitable for continuous variables. The paragraph also addresses the issue of unengaged responses during surveys, suggesting methods to detect and remove such data. The speaker demonstrates how to use SPSS for data manipulation and emphasizes the importance of reporting these steps in research.

10:03

🕵️‍♂️ Detecting and Removing Unengaged Responses

The focus of this paragraph is on detecting unengaged responses in survey data. Techniques such as attention traps, time elapsed for survey completion, and reverse-coded items are discussed to identify respondents who may not have paid attention. The speaker also introduces the concept of using standard deviation to find respondents who gave the same response to every question, indicating a lack of engagement. The paragraph concludes with the speaker's decision to remove unengaged responses and the rationale for reporting these actions in research.

15:06

📉 Identifying and Handling Outliers in Continuous Variables

The paragraph discusses the identification and handling of outliers in continuous variables such as age and experience. The speaker demonstrates how to use scatter plots to visualize potential outliers and the importance of corroborating extreme values with other data points. The decision-making process for dealing with outliers, including the choice to either correct erroneous responses or remove them, is explored. The speaker also discusses the implications of not having recorded timestamps for additional verification.

20:06

📋 Variable Screening, Skewness, and Kurtosis

In this final paragraph, the speaker addresses the last steps of data screening, including checking for skewness and kurtosis in the variables. The process of using descriptive statistics in SPSS to obtain skewness and kurtosis values is demonstrated. The speaker sets a rule for what constitutes problematic values (greater than three) and uses conditional formatting in Excel to highlight these. The paragraph concludes with a discussion on how to report these findings and the potential actions to take if significant skewness or kurtosis is found, such as watching the variable or removing it from the analysis.

Keywords

💡Case Screening

Case screening refers to the process of examining individual cases or rows in a dataset to determine their suitability for analysis. In the context of the video, the speaker discusses the importance of case screening to identify rows with missing data or unengaged responses, which may be candidates for removal from the dataset. This process is crucial for ensuring the quality and reliability of the data analysis.

💡Missing Data

Missing data is a common issue in data analysis where some values within a dataset are not available or are incomplete. The video script mentions checking for missing data in rows as a part of the data cleaning process. The speaker demonstrates how to identify and handle missing data, such as by imputing values or removing rows with excessive missing data, to maintain the integrity of the dataset.

💡Outliers

Outliers are data points that are significantly different from other observations, indicating potential errors or extreme values. In the video, the speaker discusses identifying outliers on continuous variables, such as age and experience, by visually inspecting data or using statistical methods. Outliers can skew the results of an analysis, so the speaker considers removing or correcting them to ensure accurate insights.

💡Ordinal Scales

Ordinal scales are a level of measurement where the data can be ranked but the differences between the ranks are not necessarily equal. The script mentions that for ordinal variables, such as Likert scales, the median is often used for imputation of missing values because it is more appropriate than the mean, which assumes equal intervals between scale points.

💡Continuous Variables

Continuous variables are variables that can take any value within a range, as opposed to discrete variables which can only take certain values. The video discusses handling missing data in continuous variables like experience by imputing the mean, which is suitable because these variables can have any value within a range and are not restricted to a set number of ordered categories.

💡Imputation

Imputation is the process of estimating and filling in missing data points in a dataset. The speaker in the video uses imputation to deal with missing values by replacing them with the median for ordinal scales and the mean for continuous variables. This technique helps to maintain the completeness of the dataset and is a common practice in data preprocessing.

💡Unengaged Responses

Unengaged responses are instances where survey respondents do not pay attention to the questions and provide random or identical answers across all items. The video script describes methods to detect unengaged responses, such as identical responses to all questions or extremely short completion times for a survey. The speaker advises removing these responses to maintain data quality.

💡Attention Trap

An attention trap is a question or item intentionally designed to identify unengaged respondents in a survey. The video mentions using an attention trap question with a specific instruction to answer in a certain way, such as 'strongly disagree', to catch respondents who are not paying attention. Those who fail the attention trap are identified as unengaged and may have their data removed.

💡Skewness and Kurtosis

Skewness and kurtosis are statistical measures that describe the shape of a distribution of data. Skewness measures the asymmetry of the distribution, while kurtosis measures the 'tailedness'. In the video, the speaker checks for skewness and kurtosis to identify any data that might not be normally distributed, which could indicate issues with the data or the need for transformation. The script mentions using these measures as part of the data screening process.

💡Descriptives Frequencies

Descriptives frequencies are statistical tools used to summarize data in terms of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation). The video script includes a step where the speaker uses descriptives frequencies in SPSS to identify variables with missing data, which helps in deciding how to handle these missing values during the data cleaning process.

Highlights

Case screening is essential for identifying rows with missing data in a dataset.

Unengaged responses can be detected by analyzing missing data in rows and removing them if necessary.

Outliers in continuous variables should be examined and addressed to maintain data integrity.

Data organization is crucial before analyzing missing data to ensure variables are correctly aligned.

Using Excel to check for missing data by counting blank cells can be an efficient method.

Deleting variables not required for analysis streamlines the dataset and focuses on relevant information.

ID variables are important for tracking individual responses within a dataset.

Imputing missing values is necessary for variables with less than 5% missing data to maintain data completeness.

Median imputation is suitable for ordinal scales, while mean imputation is appropriate for continuous scales.

SPSS can be used to replace missing values with median or mean values for specific variables.

Attention traps in surveys can help identify unengaged respondents by their inconsistent responses.

Recording time elapsed during a survey can be a method to detect unengaged respondents who complete it too quickly.

Reverse-coded items in surveys can reveal unengaged respondents who fail to answer them correctly.

Standard deviation can be used to detect respondents who give the same response to every question.

Outliers on continuous variables can be identified through scatter plots and addressed accordingly.

Skewness and kurtosis should be checked for normality in the data, with values over three indicating potential issues.

Extremely non-normal variables may need to be watched or dropped if they cause problems in further analysis.

Reporting the handling of missing data, unengaged responses, and outliers is important for transparency in research.

Transcripts

alright first things first let's go back

to here and we're going to do some case

screening now cases refer to the rows in

your data set let's go get that data set

and then we'll look at the missing data

in the rows unengaged responses these

are potential candidates for dismissal

removing their row from the data set and

then outliers on our continuous

variables so the data again is right

here on the home page of stat wiki it's

the YouTube SCM series I'm just going to

click this so you know I'm using the

exact same data as you open it up okay

we have the data now this has more

variables than you will need but just

barely so first things first we need to

see if we have missing data on the rows

let's see

missing nope here missing data in rows

now the easiest way for you to do this

is actually first to organize the data

let me get rid of any variables we don't

need do we need playful let's go back to

this model here here's the model here's

the data and do we need playful the

answer is yes do we need comp latent new

let me just delete that for now you

might want to just drag it down to the

bottom of yours I'm going to just delete

it because I won't be using it a typical

use yes we are using usefulness yes joy

yes that's enjoyment the same info AK

yes decision quality yes gender yes age

yes education no frequency experience

yes yes and ID I'm actually going to

keep the ID because it allows us to keep

keep track of which road belongs to

which person so we have all the data we

need I'm going to save this as something

else in my downloads folder that's fine

YouTube SEM series trimmed okay alright

now what I'd like to do is first check

to see if I have a lot of missing data

I'm just going to do ctrl a ctrl C

that's a select all and copy and then

I'm going to go into Excel let's do a

new excel sheet here

and paste that in here and I'm just

going to go to the very end

ooh here it is the very end for me

that's column a s and I'm going to

include a formula it's equals count

blank now what that'll do is it'll tell

me how many missing values I have in

whatever range I specify so my range is

from a 1 to a r1 and in this first row I

have no missing data that's awesome

just double click that and we'll see we

can get here I have 1 1 2 you know let

me just do this I'm going to sort by

this let's see if it lets me a to Z sort

largest to smallest now before you start

sorting data outside of SPSS make sure

you have the unique identifier in there

because if you start sorting and you

only have a portion of the data you need

to be able to match it back up now in

this case I have all of the data so what

I'm going to do is if I remove any rows

it doesn't matter X I'm just going to

delete all of the data in my current

data set and replace it all with

whatever I end up with here but again if

you're only sorting a few of the

variables or manipulating a few of the

variables out of all of the variables

you need to be able to resort it in the

correct order so that when you put it

back in SPSS the data for each row

belongs to the right row hope that made

sense

okay so largest to smallest we can see

whoa I have eggs there are two these two

records 288 and 303 that have 42 missing

values that's epic Wow

um okay looks like they didn't fill out

any of the survey except whatever this

question was um so I can't use those

roads they are completely useless to me

so I'm going to go over to SPSS and

delete those rows that's 288 and 303 for

the identifiers let me go over here find

ID 288 and 303 so here's the ID looks

like it is in order which is helpful

288 here yep missing and you can see

this one right here 303 also missing so

I just held ctrl while clicking single

clicking on the row number and that lets

me select both of them and now if I

right-click one of those selected ones

and hit clear it will delete those and

shift the others up so those two are now

gone from the data set other than that

it looks like I'm doing pretty good just

one or two missing it's not more than 5%

missing so what I'm going to do is just

impute those values so which variables

are the ones that have a missing no good

what I can do is go back over to SPSS

and do analyze descriptives frequencies

and just stick all my variables in there

except ID I don't need ID oops throw

those in there and then make sure I

display frequencies table and hit OK and

this will show me right here which ones

are missing data ah useful three and why

don't we do make my life easier I'm

going to copy this over go back to excel

new tab paste this in here and I know

I'm going fast you're welcome to pause

stop you can even play these in slow-mo

if you click on the COG on the bottom

right you can put me at like 0.5 speed

or 0.75 speed and I'll still be talking

but it will be like this super slow

anyway if that is helpful so I'm just

going to get rid of the ones that don't

have missing just so I can consolidate

okay so it looks like we only have a few

that have missing values oh I didn't

select gender here we go get rid of that

one okay so these are all ordinal Likert

scales so what we'll do for these will

impute the median for each of those for

decision quality one that's also ordinal

experience as continuous so we'll want

to impute the mean now why is that

median for ordinal and mean for

continuous well in an ordinal scenario

there is no such thing as a one point

two seven which might be the mean or

three point six six on those don't exist

on ordinal scale and ordinal is a one

two three four etc whereas experience

could be any range of numbers

this is experienced in years I believe

so it could be anything from zero to

however old Excel is twenty-five or

something like that so a mean is more

appropriate here although median would

work just as well okay so let's go do

that over in SPSS here we go we're going

to go to transform and replace missing

values and let's put the ones in there

that had some missing it was useful two

three and five so go down here useful

two three five throw those in and joy

six

it's choice six and decision quality one

there's decision quality one and that

the last one was experience throw that

in there okay well with these guys I

wonder if I can Slyke multiple at a time

I can Ray with all of them except

experience and expand that um I want to

use Oh poop he won't let me change them

one uh more than one at a time I want to

just replace the existing variable so

I'm going to get rid of this underscore

one change and I want it to be a median

of nearby points and how many points all

points

and then a change then this one get rid

of the same

but for experience I want it to be the

mean I'll still get rid of this

underscore one so it just replaces the

existing variable okay and then I hit

okay and it says change existing

variables it says that because I'm

naming them the exact same thing as I

have variables named already the answer

is yes I want to replace them

okay and at this point if you want to

play it say if you might save as not

this screen you might save your dataset

as something else like imputed or no

missing or something like that now why

do we want no missing values well in

Amos later on you can't do a few things

if you have missing values you can't

estimate modification indices you can't

run a bootstrap I believe there are a

few different issues that come up when

you have missing values so it's best to

impute them now what do you do if you

have more than five or ten maybe percent

of missing values on a single column

well that gets tricky because you start

um what's the word diluting like when

you dilute your your drink with water

you start diluting the potency of the

variable because you start bringing

everything towards the mean and so all

of the effects the regression of the

correlations will be diluted or dampened

and so that's not ideal so moving on

what do we report well what we could say

is may bring this down again what we

could say in our paper is something like

we had one two three four five six seven

variables with missing values all less

than 5% missing which we replaced with

the median for ordinal scales and the

mean for continuous scales period you

also might want to say that we deleted

two rows due to having fully incomplete

responses so more than 20% of the

responses we're missing and so we

removed those rows that's what you'd say

in your report nothing more than that

you don't need to say exactly how many

were

each variable in which variables they

were just tell it like it is like that

okay now is simple what's next

unengaged responses so this is tricky

and not everybody looks into this and

unengaged response is when somebody is

taking your survey but not really paying

attention so how do we detect this well

let's go back let me copy all this and

ctrl C and let's create a new sheet

trophy what we can do is visually

inspect all of this data which is kind

of tricky right especially if you have

300 respondents or more I mean that is a

lot of visual inspection so the easiest

way to do this is to actually throw in

an attention trap so an attention trap

is something like um if you are still

paying attention please answer strongly

disagree or if you are still pegan

tition please answer somewhat agree and

then anyone who does not answer that

question correctly obviously wasn't

paying attention and so you can remove

you are justified to remove their data

another way you can do this is by

recording the time elapsed for taking

the survey and then sort by that time in

the end and those who took less than

whatever's reasonable let's say you had

a 60 item survey and you see many people

finish the survey in under 60 seconds

that is mentally physically impossible

right

um and so clearly they were not engaged

if they spent less than minute filling

out your 60 item questionnaire another

way to do this is to use reverse coded

items so let me go back to the survey

here and go right here and you can see

here in the labels we have the wording

of each of the questions a reverse code

item is a negatively code coded item so

for playful you can see I am playful

when I interact with Excel what if I had

another question that said I am rigid or

not playful or

or something like that when I play it

when I interact with Excel well you

would expect them to answer in the

opposite direction so if they answer

strongly agree to this one then if you

have a negatively coded item reverse

coded item they should answer like

strongly disagree for that item or

something like that in the negative end

of the scale and if they don't well then

they weren't paying attention you can

move their row the other kind which is

what we call it be liner is someone who

answers the exact same value for every

question so there's one way to detect

this and that is to do an equals

standard deviation stdp

there we go standard deviation on the

row again that was equals stdev

there it is and see if there is much of

a standard deviation

now I actually don't need it for this

whole row um once we start looking at

gender and age I'm no longer interested

so let me change this range here just to

there okay and we'll see a standard

deviation and what this is going to tell

us is that there is zero variance on a

row meaning the answer the exact same

value for every question and oh we do

have one actually do we have any others

everything else looks reasonable

reasonable what is reasonable and

depends on what kind of scales you're

using I use five-point Likert scales so

maybe like a point five standard

deviation

there's no literature support for that

I'm just saying this is a an indicator

sort of like a red flag go then look

well here's another one

okay um looks like we have two okay let

me scroll back up we have two cases

where in fact I should just sort this by

this column small so largest two came

out at the very bottom of zero there

your largest smallest whoops okay these

two rows which are cases nineteen and

295 they had zero variance on the

questions look this guy answered for all

the way across the sky answered three

all the way across clearly not engaged

if you answer the exact same thing for

every single question now if I had

recorded times I could also see how long

it took them and so anyway there's a

good way to detect them what I would do

at this point now that I've visually

inspected them after looking that

standard deviation I'm going to go to

IDs 19 and 295 and just remove those two

rows and what would I report I report

that I found two respondents who were

unengaged as evidenced by giving the

exact same response for every single

item and that is fully justifiable 19

and 295 okay go back to here ID and 19

there is I'm going to right-click and

clear and then go find number 295

here we go ID 295 this guy I know the

rule number says 293 but the IDS 295

don't get those confused there's 295 I'm

going to delete this one clear okay we

have now looked at unengaged responses

excellent

last thing outliers on continuous

variables what continuous variables am I

actually using well we have age and

experience here's age you can see it's

fairly continuous and experience I

believe this went in years so it's up to

as many years as excels been in

existence so let's take these two and

see if there are any outliers so how do

we do this well one simple way is to go

to graphs go to chart builder and select

a scatter dot just a simple one drag

that out and on the y-axis throw in your

variable I'm use experience in this case

and on the x-axis we can throw ID in

there and then hit OK and looks like

we're pretty good except whoa this guy

out here so we want to know who this is

how could we figure that out they say

they have

I have years of Excel experience now

that is possible Excel has existed for

quite a while let me go ahead and sort

this row right click it and sort

descending

here's that person of 25 years of

experience how can we corroborate

whether this is possible well we have

their age look this person is 21 years

old they've been using this for years

prior to being born probably not

possible so and these two people have

used since they were 6 or 7 hmm

definitely possible so we'll leave those

in but this one impossible so what do we

think happened here is this an erroneous

response is this a useless case you know

looking at this they responded for for

everything really even paying attention

their 21-6 useful for 2 2 2 2 2 2 2 2 1

1 1 1 1 2 4 4 4 4 having it's hard to

say whether this was an engaged person

or not 25 was the top of the scale I

wonder if they just clicked on the

slider and pulled it too right oh this

is a tough choice what I'm going to do

and this is your call I would probably

delete this guy

why because selected to for all the play

holes for for all the joys those two

should move together right um and then

ones for all atypicals

that might be valid or oh this is our

decision if you want to play it safe

then what you can do is just correct

this erroneous response what is the

median for experience let's go find out

I have experienced just over here let's

see here's the experience column let me

just get the median or the mean if I

just highlight that whole column you can

see down in Excel says the average is

four point four one so let me go ahead

and stick that in for point four one

nine so four point four two as well

stick in there four point four two I'm

going to give this person the average

not knowing what the real value might

have been

also fairly suspicious that this is just

a bad respondent I wish I had recorded

timestamps oh well okay that was the

only one on experience let's go do the

same thing for age in fact if we want we

can just take age here right click it

sort descending we have a 35 year old a

34 year old 31 year old nothing really

outlying on top let's sort it ascending

we have two people who didn't report age

now are we even using age in our

analyses let's go back to here we are

controlling for age so we have two here

that haven't even been imputed yet what

respondent didn't respond let's go ahead

and stick mean in there as well back to

excel let's find out what that average

was here's the age column the average

was twenty one point six five I'll go

ahead and stick that in here twenty one

point six five twenty one point six five

okay we have imputed those two values

with the mean and that is outliers on

continuous variables okay next is

variable screening we already did this

we looked at missing data and columns

the last thing is skewness and kurtosis

and then I'll start a new video

okay skewness and kurtosis back to here

go to analyze descriptives frequencies

and we have it all in there already

except these guys throw those back in

those the ones we imputed and go to

statistics and what we want we want

skewness and kurtosis continue continue

and here we go

if you go up to the very top it shows it

right here boom okay right click this

copy this go back over to excel and you

tab throw this in there and what we're

looking for

depends on who you quote who you cite

but a fairly liberal rule which I'll use

because I find it works just fine

is that values over three are

problematic if you want to be a little

more rigid there's a two point two and a

little more rigid there's a one absolute

value of one and even more rigid it's

something about dividing

the value by the standard error and

making sure it's not more than three

times that so let's use the easy one

let's say three so if any of these are

greater than three we have a problem if

you highlight both of those and go to

conditional formatting highlight cell

rules greater than three then make it

red and we're going to do one more time

conditional formatting highlight cell

rules less than negative three then

highlight it okay

I highlight these because they're not

numbers but let's go look over looks

like we're doing pretty good pretty good

actually age is the only one Wow I'm

surprised okay

so age has a kurtosis issue and you may

ask why would that be well we were

sampling undergraduate students at a

college they're all like 19 to 21 years

old so of course you're gonna have a

kurtosis issue on everything else fairly

normal so we're pretty good there we

wouldn't need to make any changes now

what would we do if we did have non

normal ordinal measures you can't really

transform them they're on a short order

five-point scale so transformation

really won't do anything um so what do

you do you can if they are extremely non

normal you know greater than a three

watch them and see if they have other

problems or just drop them if you have

that capability let's say for example

decision quality five was highly non

normal let's say this was a eight right

here right big bad eight then we'd say

well we can afford to drop decision

quality five why because we had decision

quality two three four six seven and

eight we have tons of these things and

it's reflective so dropping one item

really shouldn't change the nature of

the construct so I would just get rid of

it oh now let's say you had only one or

two variables let's say we had let's go

here let's get rid of these guys let's

say decision quality only had three

items and it turns out decision quality

three is highly skewed to put a six in

there highly skewed what do we do I only

have two other items Yi this is tricky

what I would do is I would leave it in

but watch it and if it gives you

problems in the future

we may have to cut it out if it starts

giving us problems in the factor

analysis either exploratory or

confirmatory factor analysis and that's

it what do you do to report it you'd say

well we have one item that was skewed

and we decided to watch it because we

only had two other items on that

construct or you could say we had one

item that was highly skewed you know

value six

we set it delete that item from the set

you could say we also had a highly crew

tote age variable but this is expected

because of the sample population its

students so obviously the age is going

to be highly Curt oat period there we go

okay I think that does it for data

screening woohoo