cmm.pilot3.cleaning
Kris Evans
12/8/2020
#load library
library(readr)
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ dplyr 1.0.2
## ✓ tibble 3.0.4 ✓ stringr 1.4.0
## ✓ tidyr 1.1.2 ✓ forcats 0.5.0
## ✓ purrr 0.3.4## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()getwd()## [1] "/Users/krise/R Mac working folder/Controllable Meta-mindset data"#load data
d.study <- read_csv("~/R Mac working folder/Controllable Meta-mindset data/raw data files - controllable meta-mindset/CMM_pilot_study_1_December 8, 2020_11.16_raw.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_character()
## )
## ℹ Use `spec()` for the full column specifications.#drop 2nd row
glimpse(d.study)## Rows: 51
## Columns: 45
## $ StartDate <chr> "Start Date", "{\"ImportId\":\"startDate\",\"…
## $ EndDate <chr> "End Date", "{\"ImportId\":\"endDate\",\"time…
## $ Status <chr> "Response Type", "{\"ImportId\":\"status\"}",…
## $ IPAddress <chr> "IP Address", "{\"ImportId\":\"ipAddress\"}",…
## $ Progress <chr> "Progress", "{\"ImportId\":\"progress\"}", "1…
## $ `Duration (in seconds)` <chr> "Duration (in seconds)", "{\"ImportId\":\"dur…
## $ Finished <chr> "Finished", "{\"ImportId\":\"finished\"}", "1…
## $ RecordedDate <chr> "Recorded Date", "{\"ImportId\":\"recordedDat…
## $ ResponseId <chr> "Response ID", "{\"ImportId\":\"_recordId\"}"…
## $ RecipientLastName <chr> "Recipient Last Name", "{\"ImportId\":\"recip…
## $ RecipientFirstName <chr> "Recipient First Name", "{\"ImportId\":\"reci…
## $ RecipientEmail <chr> "Recipient Email", "{\"ImportId\":\"recipient…
## $ ExternalReference <chr> "External Data Reference", "{\"ImportId\":\"e…
## $ LocationLatitude <chr> "Location Latitude", "{\"ImportId\":\"locatio…
## $ LocationLongitude <chr> "Location Longitude", "{\"ImportId\":\"locati…
## $ DistributionChannel <chr> "Distribution Channel", "{\"ImportId\":\"dist…
## $ UserLanguage <chr> "User Language", "{\"ImportId\":\"userLanguag…
## $ cons <chr> "DESCRIPTION: The purpose of this research st…
## $ cmm_L_1 <chr> "The statements below describe your beliefs a…
## $ cmm_L_2 <chr> "The statements below describe your beliefs a…
## $ cmm_L_3 <chr> "The statements below describe your beliefs a…
## $ cmm_L_4 <chr> "The statements below describe your beliefs a…
## $ cmm_s_1 <chr> "The statements below describe your beliefs a…
## $ cmm_s_2 <chr> "The statements below describe your beliefs a…
## $ cmm_s_3 <chr> "The statements below describe your beliefs a…
## $ cmm_s_4 <chr> "The statements below describe your beliefs a…
## $ qq_1 <chr> "In your own words, what is a mindset?", "{\"…
## $ qq_2 <chr> "Can you give an example of some of the minds…
## $ qq_3 <chr> "Can you give an example of a time when a min…
## $ qq_4 <chr> "Describe a time that you did or didn’t chang…
## $ qq_5 <chr> "If you wanted to change your mindset, what m…
## $ qq_6 <chr> "Please tell us some other mindsets you think…
## $ dem_age <chr> "What is your age? (number only)", "{\"Import…
## $ dem_race <chr> "Which race/ethnicity best describes you? Sel…
## $ dem_race_6_TEXT <chr> "Which race/ethnicity best describes you? Sel…
## $ dem_gender <chr> "What is your gender? - Selected Choice", "{\…
## $ dem_gender_5_TEXT <chr> "What is your gender? - Other (please specify…
## $ dem_inc <chr> "What is your approximate combined annual hou…
## $ dem_ed <chr> "What is the highest education level you have…
## $ dem_emp <chr> "Employment\nStatus: Are you currently…?", "{…
## $ debrief_else <chr> "Is there anything else you want to tell us?"…
## $ Q_TotalDuration <chr> "Q_TotalDuration", "{\"ImportId\":\"Q_TotalDu…
## $ workerId <chr> "workerId", "{\"ImportId\":\"workerId\"}", NA…
## $ assignmentId <chr> "assignmentId", "{\"ImportId\":\"assignmentId…
## $ hitId <chr> "hitId", "{\"ImportId\":\"hitId\"}", NA, NA, …head(d.study)## # A tibble: 6 x 45
## StartDate EndDate Status IPAddress Progress `Duration (in s… Finished
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 "Start D… "End D… "Resp… "IP Addr… "Progre… "Duration (in s… "Finish…
## 2 "{\"Impo… "{\"Im… "{\"I… "{\"Impo… "{\"Imp… "{\"ImportId\":… "{\"Imp…
## 3 "12/8/20… "12/8/… "0" "165.225… "100" "275" "1"
## 4 "12/8/20… "12/8/… "0" "82.132.… "100" "361" "1"
## 5 "12/8/20… "12/8/… "0" "122.56.… "100" "449" "1"
## 6 "12/8/20… "12/8/… "0" "75.85.5… "100" "566" "1"
## # … with 38 more variables: RecordedDate <chr>, ResponseId <chr>,
## # RecipientLastName <chr>, RecipientFirstName <chr>, RecipientEmail <chr>,
## # ExternalReference <chr>, LocationLatitude <chr>, LocationLongitude <chr>,
## # DistributionChannel <chr>, UserLanguage <chr>, cons <chr>, cmm_L_1 <chr>,
## # cmm_L_2 <chr>, cmm_L_3 <chr>, cmm_L_4 <chr>, cmm_s_1 <chr>, cmm_s_2 <chr>,
## # cmm_s_3 <chr>, cmm_s_4 <chr>, qq_1 <chr>, qq_2 <chr>, qq_3 <chr>,
## # qq_4 <chr>, qq_5 <chr>, qq_6 <chr>, dem_age <chr>, dem_race <chr>,
## # dem_race_6_TEXT <chr>, dem_gender <chr>, dem_gender_5_TEXT <chr>,
## # dem_inc <chr>, dem_ed <chr>, dem_emp <chr>, debrief_else <chr>,
## # Q_TotalDuration <chr>, workerId <chr>, assignmentId <chr>, hitId <chr>c.study <- (d.study[ -c(1, 2), ])#keep all those that were finished, didnt drop anyone
#select key variables
cmm.study <- c.study %>% filter(Finished == "1") %>% select(cmm_L_1, cmm_L_2, cmm_L_3, cmm_L_4, cmm_s_1, cmm_s_2, cmm_s_3, cmm_s_4, qq_1, qq_2, qq_3, qq_4, qq_5, qq_6, dem_age, dem_race, dem_race_6_TEXT, dem_gender, dem_gender_5_TEXT, dem_inc, dem_ed, dem_emp, debrief_else)#In this questionnaire you will be shown a number of statements regarding mindsets. Please indicate your agreement with each statement by selecting the number that best represents your answer on the scale presented below the statement.
1. To be honest, you can’t really change your mindsets. (R)
2. As much as I hate to admit it, you can’t teach an old dog new tricks. You can’t really change your mindsets about things in the world. (R)
3. Even in moments when it really matters, I can’t do much to change my mindset. (R)
4. How much I can control my mindset is something about me that I can’t change very much. (R)
Scale: Strongly disagree (1), Disagree (2), Somewhat disagree (3), Somewhat agree (4), Agree (5), Strongly Agree (6)
Scoring: Reverse score items then, compute the mean score of all the items. A higher score will indicate greater agreement that one’s mindset is controllable and changeable.
#add id number
cmm.study <- cmm.study %>% mutate(id = row_number())#split into two data frames for short and long instructions
#tidy data so only filter all those with no response to q1
cmm.study.long <- cmm.study %>% filter(cmm_L_1 !="NA") %>% select(-cmm_s_1, -cmm_s_2, -cmm_s_3, -cmm_s_4)
#25 responses
cmm.study.short <- cmm.study %>% filter(cmm_s_1 !="NA") %>% select(-cmm_L_1, -cmm_L_2, -cmm_L_3, -cmm_L_4)
#24 responses#turn the necessary variables into numeric because they are characters
cmm.study.long$cmm_L_1 <- as.numeric(cmm.study.long$cmm_L_1)
cmm.study.long$cmm_L_2 <- as.numeric(cmm.study.long$cmm_L_2)
cmm.study.long$cmm_L_3 <- as.numeric(cmm.study.long$cmm_L_3)
cmm.study.long$cmm_L_4 <- as.numeric(cmm.study.long$cmm_L_4)
cmm.study.short$cmm_s_1 <- as.numeric(cmm.study.short$cmm_s_1)
cmm.study.short$cmm_s_2 <- as.numeric(cmm.study.short$cmm_s_2)
cmm.study.short$cmm_s_3 <- as.numeric(cmm.study.short$cmm_s_3)
cmm.study.short$cmm_s_4 <- as.numeric(cmm.study.short$cmm_s_4)#create CMM item score for long and short instructions
cmm.long.df <- cmm.study.long %>% mutate(cmm_Long_score = (cmm_L_1 + cmm_L_2 + cmm_L_3 + cmm_L_4)/4)
cmm.short.df <- cmm.study.short %>% mutate(cmm_Short_score = (cmm_s_1 + cmm_s_2 +
cmm_s_3 +
cmm_s_4)/4)#mean of the two scores long = 2.99, short = 2.60
#mean of the long scores are .39 higher, suggest may be higher score responses on longer, will look at significant difference between the groups
mean(cmm.long.df$cmm_Long_score) ## [1] 2.99mean(cmm.short.df$cmm_Short_score)## [1] 2.604167#summary
summary(cmm.long.df$cmm_Long_score)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 2.00 3.00 2.99 4.00 5.00summary(cmm.short.df$cmm_Short_score)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.500 2.000 2.500 2.604 3.000 4.000sd(cmm.long.df$cmm_Long_score) #sd long responses do have larder sd.3## [1] 1.093256sd(cmm.short.df$cmm_Short_score) #sd## [1] 0.7294901#in cmm pilot(n=324) mean = 3.16, sd=1.50
#view histogram of response cores for long adn short df - doesn't seem normally distributed with the obderved sample and responses
hist(cmm.long.df$cmm_Long_score)
hist(cmm.short.df$cmm_Short_score)
boxplot(cmm.long.df$cmm_Long_score)
boxplot(cmm.short.df$cmm_Short_score)
#no outliers only 224 and 25 obs each variable#export the data score and open ended question responses
xcl.short.df <- cmm.long.df %>% select(id, cmm_Long_score, qq_1, qq_2, qq_3, qq_4, qq_5, qq_6)
xcl.long.df <- cmm.short.df %>% select(id, cmm_Short_score, qq_1, qq_2, qq_3, qq_4, qq_5, qq_6)
write.csv(xcl.long.df,'cmm.pilot3.long.csv')
write.csv(xcl.short.df,'cmm.pilot3.short.csv')#Analyses and tests
#do the populations have the same variances? -- reject the null so the two samples do not have the same variance
cmm.ftest <- var.test(cmm.long.df$cmm_Long_score, cmm.short.df$cmm_Short_score)
cmm.ftest##
## F test to compare two variances
##
## data: cmm.long.df$cmm_Long_score and cmm.short.df$cmm_Short_score
## F = 2.246, num df = 24, denom df = 23, p-value = 0.05657
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.9769749 5.1254711
## sample estimates:
## ratio of variances
## 2.245974# is data normally distributed? - cannot reject the null, data seem to be normally distributed
shapiro.test(cmm.long.df$cmm_Long_score) #shapiro test- [value-0.06] > .05 - we assume normal distribution - long version seems normally distributed##
## Shapiro-Wilk normality test
##
## data: cmm.long.df$cmm_Long_score
## W = 0.925, p-value = 0.06668shapiro.test(cmm.short.df$cmm_Short_score) #pvalue = .305 same as above##
## Shapiro-Wilk normality test
##
## data: cmm.short.df$cmm_Short_score
## W = 0.95241, p-value = 0.3053#could try to run more samples at this response and do analyses?#is there a sig diff between the mean scores?
#Run a paired T-test of mean scores, reject null, the true difference in means is not = 0, it is positive meaning significantly higher at post
#independent 2 group t test? treat treatedthem as independet groups because two groups took different measures, using welch two sample t test -
cmm.ttest <- t.test(cmm.long.df$cmm_Long_score, cmm.short.df$cmm_Short_score, var.equal = FALSE) #variance of two groups is not equal
#p=.15 so we cannot reject the null, meaning there is not a sig diff in means between groups
#could create a new variablelike inst.length = Long or Short as a categorical variable with two levels but can just use the scores of the two group means like above
cmm.ttest##
## Welch Two Sample t-test
##
## data: cmm.long.df$cmm_Long_score and cmm.short.df$cmm_Short_score
## t = 1.4585, df = 41.998, p-value = 0.1521
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1480311 0.9196978
## sample estimates:
## mean of x mean of y
## 2.990000 2.604167#subset just the CMM item variables
cmm.long.scale.df <- cmm.long.df %>% select(cmm_L_1, cmm_L_2, cmm_L_3, cmm_L_4)
#subset the short scale
cmm.short.scale.df <- cmm.short.df %>% select(cmm_s_1, cmm_s_2, cmm_s_3, cmm_s_4)library(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha#check alpha and reliability analysis
alpha(cmm.long.scale.df) #long scale has higher alpha and reliability .92 > .82##
## Reliability analysis
## Call: alpha(x = cmm.long.scale.df)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.93 0.93 0.93 0.76 13 0.025 3 1.1 0.78
##
## lower alpha upper 95% confidence boundaries
## 0.88 0.93 0.98
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## cmm_L_1 0.88 0.88 0.87 0.71 7.4 0.042 0.02315 0.67
## cmm_L_2 0.96 0.96 0.95 0.90 27.5 0.013 0.00073 0.89
## cmm_L_3 0.88 0.88 0.87 0.72 7.6 0.042 0.02477 0.67
## cmm_L_4 0.88 0.88 0.87 0.70 7.1 0.047 0.03978 0.59
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## cmm_L_1 25 0.94 0.94 0.95 0.90 3.0 1.2
## cmm_L_2 25 0.78 0.79 0.66 0.64 2.6 1.2
## cmm_L_3 25 0.94 0.94 0.94 0.89 3.2 1.2
## cmm_L_4 25 0.95 0.95 0.94 0.91 3.2 1.3
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 miss
## cmm_L_1 0.08 0.36 0.16 0.32 0.08 0.00 0
## cmm_L_2 0.20 0.28 0.24 0.24 0.04 0.00 0
## cmm_L_3 0.08 0.28 0.08 0.48 0.08 0.00 0
## cmm_L_4 0.04 0.40 0.08 0.36 0.08 0.04 0alpha(cmm.short.scale.df)##
## Reliability analysis
## Call: alpha(x = cmm.short.scale.df)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.84 0.85 0.56 5.1 0.061 2.6 0.73 0.61
##
## lower alpha upper 95% confidence boundaries
## 0.7 0.82 0.94
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## cmm_s_1 0.67 0.69 0.65 0.43 2.3 0.122 0.038 0.37
## cmm_s_2 0.88 0.88 0.84 0.72 7.6 0.040 0.004 0.73
## cmm_s_3 0.76 0.77 0.78 0.53 3.4 0.089 0.053 0.58
## cmm_s_4 0.79 0.80 0.79 0.57 4.0 0.078 0.042 0.58
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## cmm_s_1 24 0.94 0.94 0.95 0.87 2.3 0.91
## cmm_s_2 24 0.72 0.68 0.53 0.46 2.5 1.06
## cmm_s_3 24 0.83 0.85 0.80 0.70 2.7 0.81
## cmm_s_4 24 0.78 0.81 0.74 0.63 3.0 0.81
##
## Non missing response frequency for each item
## 1 2 3 4 miss
## cmm_s_1 0.17 0.50 0.21 0.12 0
## cmm_s_2 0.21 0.33 0.25 0.21 0
## cmm_s_3 0.08 0.25 0.54 0.12 0
## cmm_s_4 0.04 0.21 0.50 0.25 0#suggest more stable using long measure? higher response scores as well#EFA and CFA long
EFA.model.CMM.long <- fa(cmm.long.scale.df)
EFA.model.CMM.long$loadings##
## Loadings:
## MR1
## cmm_L_1 0.952
## cmm_L_2 0.647
## cmm_L_3 0.941
## cmm_L_4 0.955
##
## MR1
## SS loadings 3.124
## Proportion Var 0.781#factor loadings
fa.diagram(EFA.model.CMM.long)
#this function shows individuals scores on factor
EFA.model.CMM.long$scores## MR1
## [1,] 1.92995775
## [2,] -0.91797359
## [3,] -0.90054464
## [4,] 1.19694616
## [5,] -0.93540253
## [6,] 0.08811625
## [7,] 0.50690888
## [8,] 0.74211976
## [9,] 0.75954870
## [10,] -0.90054464
## [11,] 0.75954870
## [12,] -1.40765788
## [13,] 0.75954870
## [14,] 0.75954870
## [15,] -0.71578700
## [16,] 0.72469081
## [17,] -0.69835806
## [18,] 0.10554519
## [19,] -0.91797359
## [20,] 0.74211976
## [21,] -0.93540253
## [22,] -1.75673473
## [23,] -0.91797359
## [24,] 1.10862555
## [25,] 0.82112784summary(EFA.model.CMM.long$scores)## MR1
## Min. :-1.7567
## 1st Qu.:-0.9180
## Median : 0.1055
## Mean : 0.0000
## 3rd Qu.: 0.7595
## Max. : 1.9300#feel for distribution of factor scores
summary(cmm.long.df)## cmm_L_1 cmm_L_2 cmm_L_3 cmm_L_4 qq_1
## Min. :1.00 Min. :1.00 Min. :1.0 Min. :1.00 Length:25
## 1st Qu.:2.00 1st Qu.:2.00 1st Qu.:2.0 1st Qu.:2.00 Class :character
## Median :3.00 Median :3.00 Median :4.0 Median :3.00 Mode :character
## Mean :2.96 Mean :2.64 Mean :3.2 Mean :3.16
## 3rd Qu.:4.00 3rd Qu.:4.00 3rd Qu.:4.0 3rd Qu.:4.00
## Max. :5.00 Max. :5.00 Max. :5.0 Max. :6.00
## qq_2 qq_3 qq_4 qq_5
## Length:25 Length:25 Length:25 Length:25
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## qq_6 dem_age dem_race dem_race_6_TEXT
## Length:25 Length:25 Length:25 Length:25
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## dem_gender dem_gender_5_TEXT dem_inc dem_ed
## Length:25 Length:25 Length:25 Length:25
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## dem_emp debrief_else id cmm_Long_score
## Length:25 Length:25 Min. : 2.00 Min. :1.00
## Class :character Class :character 1st Qu.:13.00 1st Qu.:2.00
## Mode :character Mode :character Median :23.00 Median :3.00
## Mean :23.56 Mean :2.99
## 3rd Qu.:35.00 3rd Qu.:4.00
## Max. :46.00 Max. :5.00plot(density(EFA.model.CMM.long$scores))
describe(cmm.long.df$cmm_Long_score)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 2.99 1.09 3 3.01 1.48 1 5 4 -0.11 -1.33 0.22#we see 1 factor also suggest the long scale explains more variance need to check the scale reliability parameters
#also more distributed between scores but more evenly with the short#EFA and CFA short
EFA.model.CMM.short <- fa(cmm.short.scale.df)## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefullyEFA.model.CMM.short$loadings##
## Loadings:
## MR1
## cmm_s_1 1.003
## cmm_s_2 0.491
## cmm_s_3 0.802
## cmm_s_4 0.734
##
## MR1
## SS loadings 2.431
## Proportion Var 0.608#factor loadings
fa.diagram(EFA.model.CMM.short)
#this function shows individuals scores on factor
EFA.model.CMM.short$scores## MR1
## [1,] 0.6040182
## [2,] -1.5183861
## [3,] 2.1031875
## [4,] -0.1475880
## [5,] -1.2811563
## [6,] 1.8485461
## [7,] 0.7873439
## [8,] 0.7873439
## [9,] -0.5295501
## [10,] 1.8485461
## [11,] -0.2749087
## [12,] -0.2738582
## [13,] 0.7873439
## [14,] -0.2749087
## [15,] -0.5284996
## [16,] -0.4022294
## [17,] -1.4470704
## [18,] 0.7313389
## [19,] -0.4571839
## [20,] -0.2749087
## [21,] -1.4074265
## [22,] -0.2025425
## [23,] -0.2035930
## [24,] -0.2738582summary(EFA.model.CMM.short$scores)## MR1
## Min. :-1.5184
## 1st Qu.:-0.4750
## Median :-0.2739
## Mean : 0.0000
## 3rd Qu.: 0.7453
## Max. : 2.1032#feel for distribution of factor scores
summary(cmm.short.df)## cmm_s_1 cmm_s_2 cmm_s_3 cmm_s_4
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.750
## Median :2.000 Median :2.000 Median :3.000 Median :3.000
## Mean :2.292 Mean :2.458 Mean :2.708 Mean :2.958
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.250
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
## qq_1 qq_2 qq_3 qq_4
## Length:24 Length:24 Length:24 Length:24
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## qq_5 qq_6 dem_age dem_race
## Length:24 Length:24 Length:24 Length:24
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## dem_race_6_TEXT dem_gender dem_gender_5_TEXT dem_inc
## Length:24 Length:24 Length:24 Length:24
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## dem_ed dem_emp debrief_else id
## Length:24 Length:24 Length:24 Min. : 1.00
## Class :character Class :character Class :character 1st Qu.:14.25
## Mode :character Mode :character Mode :character Median :26.50
## Mean :26.50
## 3rd Qu.:41.50
## Max. :49.00
## cmm_Short_score
## Min. :1.500
## 1st Qu.:2.000
## Median :2.500
## Mean :2.604
## 3rd Qu.:3.000
## Max. :4.000plot(density(EFA.model.CMM.short$scores))
describe(cmm.short.df$cmm_Short_score)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 24 2.6 0.73 2.5 2.58 0.74 1.5 4 2.5 0.22 -0.86 0.15#we see 1 factor#check out the open ended responses, text mining bag of words?
view(xcl.long.df)
xcl.long.df## # A tibble: 24 x 8
## id cmm_Short_score qq_1 qq_2 qq_3 qq_4 qq_5 qq_6
## <int> <dbl> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1 3.5 It is th… I though… "Going … "At wor… My mind… "Fixed,…
## 2 6 1.5 The way … Mindset … "When a… "I chan… I would… "I am n…
## 3 7 3.5 your tho… mind set… "mindse… "I can'… changin… "genera…
## 4 8 2.25 Mindset … mindsets… "workin… "I foug… I would… "Mindse…
## 5 11 1.75 A mindse… Abortion… "On the… "The to… Not hav… "Having…
## 6 12 4 It is ba… stance o… "I do n… "My min… There i… "honest…
## 7 15 3 The set … Depresse… "Having… "I was … I would… "Happy/…
## 8 16 3 Your tho… Circumst… "I have… "I had … Think r… "I can’…
## 9 19 3 Someones… Changing… "When I… "I was … Stubbor… "selfis…
## 10 20 4 Your att… Motivati… "I felt… "I need… Again i… "A posi…
## # … with 14 more rowsview(xcl.short.df)