Five steps of results and analysis - Towards a process view of adherence

This section records the results obtained from the quantitative research and analyses them. The process that was followed is documented in the previous chapter.

15.2.1 Step 1: Download and initial analysis

Survey data was downloaded from the Qualtrics survey management tool into a spreadsheet. A total of 192 surveys was recorded.

Upon investigation it was discovered that the majority of respondents had commenced the survey but had not completed it. Some had stopped towards the end but many had stopped much earlier.

Removing the incomplete responses resulted in a total of 49 valid respondents.

15.2.2 Step 2: Descriptive analysis for each instrument

Each question in Table 43 in Appendix C.1 which is noted as “inverted” was re- inverted such that the high score indicated the best result for adherence. For

example, question “Belief35” makes the statement, “I believe that I can get better in other ways than taking this medicine” with a range of “Strongly Disagree” (1) to “Strongly Agree” (7). However, so that higher scores gave most positive results for adherence, the scores were re-inverted so that it effectively was rated from “Strongly Agree” (1) to “Strongly Disagree” (7).

The results of the summarize commands for each instrument are recorded in Appendix C.2.

One notable feature of the results is that almost every question shows a range of scores from 1 to 7. This results in high standard deviations. The reason for this is hard to determine. It may be because the wide range of views represents a wide- ranging experience of medicine consumption. It is also possible that it originates from a failure to understand the questions or a lack of interest in the survey leading to multiple selections of the same score for multiple questions. There is some evidence of the latter.

15.2.3 Step 3: Selection of representative items

This process iterated the factor and rotate commands, then calculated the level of convergent validity with Cronbach’s alpha by using the alpha command. Only the first Principal Component was extracted because of the lack of valid responses.

The results of the final iteration are shown in each figure in this section, together with Cronbach’s alpha coefficient. The section provides a table summarising the information. Details for each latent variable follow.

Suitable statistical processes were applied to analysing the data. However it should be noted that, since the number of completed survey responses was inadequate for statistically valid results to be obtained, no conclusions can be drawn from the quantitative research. As part of this it should be recognised that the extraction of items satisfactorily loading the first Principal Component is not statistically supported.

The Adhere(nce) latent variable reflects respondents’ level of adherence to instructions. Four items made up this instrument.

All four Adhere items loaded factor 1 at 0.7 or above.They were all taken as the first Principal Component representing adherence. Cronbach’s alpha was good at 0.76. The proportion of variance explained was 0.58.

. factor Adhere1a Adhere1b Adhere1c Adhere1d, pcf factors(1)

. rotate, orthogonal

Factor analysis/correlation Number of obs = 49 Method: principal-component factors Retained factors = 1 Rotation: orthogonal varimax (Kaiser off) Number of params = 4

--- Factor | Variance Difference Proportion Cumulative ---+--- Factor1 | 2.33651 . 0.5841 0.5841 --- LR test: independent vs. saturated: chi2(6) = 51.45 Prob>chi2 = 0.0000

Rotated factor loadings (pattern matrix) and unique variances

--- Variable | Factor1 | Uniqueness ---+---+--- Adhere1a | 0.7003 | 0.5096 Adhere1b | 0.7904 | 0.3752 Adhere1c | 0.7866 | 0.3812 Adhere1d | 0.7762 | 0.3975 ---

. alpha Adhere1a Adhere1b Adhere1c Adhere1d

Test scale = mean(unstandardized items)

Average interitem covariance: .6570295 Number of items in the scale: 4 Scale reliability coefficient: 0.7587

The Aff(ordance) latent variable reflects the qualities which the medicine brings to the consumption context.

Five out of the 13 Aff items loaded factor 1 at 0.7 or above. These were taken as the first Principal Component representing affordance. Cronbach’s alpha was high at 0.87. The proportion of variance explained was 0.66.

The items which load the Principal Component most strongly form a logical group of the questions. Aff1c is to do with problems taking medicine. Aff2, Aff3, Aff4 and Aff5 relate directly to reasons why the medicine is not easy to consume. Therefore the first Principal Component represents this.

. factor Aff1c Aff2 Aff3 Aff4 Aff5, pcf factors(1)

. rotate, orthogonal

Factor analysis/correlation Number of obs = 49 Method: principal-component factors Retained factors = 1 Rotation: orthogonal varimax (Kaiser off) Number of params = 5

--- Factor | Variance Difference Proportion Cumulative ---+--- Factor1 | 3.31572 . 0.6631 0.6631 --- LR test: independent vs. saturated: chi2(10) = 123.29 Prob>chi2 = 0.0000

Rotated factor loadings (pattern matrix) and unique variances

--- Variable | Factor1 | Uniqueness ---+---+--- Aff1c | 0.7184 | 0.4840 Aff2 | 0.8116 | 0.3413 Aff3 | 0.8406 | 0.2933 Aff4 | 0.8343 | 0.3039 Aff5 | 0.8592 | 0.2618 ---

. alpha Aff1c Aff2 Aff3 Aff4 Aff5

Test scale = mean(unstandardized items)

Average interitem covariance: 2.029365 Number of items in the scale: 5 Scale reliability coefficient: 0.8710

The Agency latent variable explains the patient’s resources which they bring to consuming the medicine.

Four out of the 11 Agency items loaded factor 1 at 0.7 or above. These were taken as the first Principal Component representing agency. Cronbach’s alpha was good at 0.79. The proportion of variance explained was 0.61.

The four items are aligned in the Agency items. They all relate to respondents’ ability to consume their medicine at the right time in the right way.

. factor Agency1 Agency2 Agency8 Agency10, pcf factors(1)

. rotate, orthogonal

Factor analysis/correlation Number of obs = 49 Method: principal-component factors Retained factors = 1 Rotation: orthogonal varimax (Kaiser off) Number of params = 4

--- Factor | Variance Difference Proportion Cumulative ---+--- Factor1 | 2.44275 . 0.6107 0.6107 --- LR test: independent vs. saturated: chi2(6) = 55.17 Prob>chi2 = 0.0000

Rotated factor loadings (pattern matrix) and unique variances

--- Variable | Factor1 | Uniqueness ---+---+--- Agency1 | 0.8630 | 0.2552 Agency2 | 0.7508 | 0.4362 Agency8 | 0.7589 | 0.4241 Agency10 | 0.7472 | 0.4417 ---

. alpha Agency1 Agency2 Agency8 Agency10

Test scale = mean(unstandardized items)

Average interitem covariance: 1.21875 Number of items in the scale: 4 Scale reliability coefficient: 0.7859

The Belief latent variable relates to the patient’s beliefs about themselves and their medicines.

Four out of the 31 Belief items loaded factor 1 at 0.7 or above. These were taken as the first Principal Component representing belief. Cronbach’s alpha was good at 0.78. The proportion of variance explained was 0.61.

These four items form a group which refer to qualities of the medicine. These beliefs are therefore directly related to the decision on whether to consume the medicine.

. factor Belief3 Belief14 Belief25 Belief28, pcf factors(1)

. rotate, orthogonal

Factor analysis/correlation Number of obs = 49 Method: principal-component factors Retained factors = 1 Rotation: orthogonal varimax (Kaiser off) Number of params = 4

--- Factor | Variance Difference Proportion Cumulative ---+--- Factor1 | 2.42067 . 0.6052 0.6052 --- LR test: independent vs. saturated: chi2(6) = 51.52 Prob>chi2 = 0.0000

Rotated factor loadings (pattern matrix) and unique variances

--- Variable | Factor1 | Uniqueness ---+---+--- Belief3 | 0.8081 | 0.3469 Belief14 | 0.7565 | 0.4276 Belief25 | 0.7837 | 0.3858 Belief28 | 0.7622 | 0.4190 ---

. alpha Belief3 Belief14 Belief25 Belief28

Test scale = mean(unstandardized items)

Average interitem covariance: 1.789966 Number of items in the scale: 4 Scale reliability coefficient: 0.7817

The Con(text) variable is to do with the context in which the patient consumes their medicine.

Four out of the ten Con items loaded factor 1 at around 0.7 or above. These were taken as the first Principal Component representing context. Cronbach’s alpha was good at 0.78. The proportion of variance explained was 0.61.

The items selected relate directly to the patient having the contextual resources required to consume their medicine.

. factor Con1c Con1d Con2 Con3, pcf factor(1)

. rotate, orthogonal

Factor analysis/correlation Number of obs = 49 Method: principal-component factors Retained factors = 1 Rotation: orthogonal varimax (Kaiser off) Number of params = 4

--- Factor | Variance Difference Proportion Cumulative ---+--- Factor1 | 2.42453 . 0.6061 0.6061 --- LR test: independent vs. saturated: chi2(6) = 54.13 Prob>chi2 = 0.0000

Rotated factor loadings (pattern matrix) and unique variances

--- Variable | Factor1 | Uniqueness ---+---+--- Con1c | 0.6913 | 0.5221 Con1d | 0.8673 | 0.2478 Con2 | 0.7411 | 0.4508 Con3 | 0.8032 | 0.3548 ---

. alpha Con1c Con1d Con2 Con3

Test scale = mean(unstandardized items)

Average interitem covariance: 1.767432 Number of items in the scale: 4 Scale reliability coefficient: 0.7754

The Mot(ivation) variable relates to the patient’s motivation to consume the medicine.

Four out of the 16 Mot items loaded factor 1 at 0.7 or above. These were taken as the first Principal Component representing motivation. Cronbach’s alpha was high at 0.81. The proportion of variance explained was 0.64.

This group of four items reflect a strong level of motivation since they are very high up the scale at the “Identification” (one question) and “Integrated” (three questions) levels (see Table 35). This is therefore a sensible grouping of items into a latent variable.

. factor Mot11 Mot13 Mot15 Mot16, pcf factors(1)

. rotate, orthogonal

Factor analysis/correlation Number of obs = 49 Method: principal-component factors Retained factors = 1 Rotation: orthogonal varimax (Kaiser off) Number of params = 4

--- Factor | Variance Difference Proportion Cumulative ---+--- Factor1 | 2.56899 . 0.6422 0.6422 --- LR test: independent vs. saturated: chi2(6) = 65.92 Prob>chi2 = 0.0000

Rotated factor loadings (pattern matrix) and unique variances

--- Variable | Factor1 | Uniqueness ---+---+--- Mot11 | 0.7229 | 0.4775 Mot13 | 0.8009 | 0.3585 Mot15 | 0.8644 | 0.2529 Mot16 | 0.8111 | 0.3422 ---

Factor rotation matrix

--- | Factor1 ---+--- Factor1 | 1.0000 ---

. alpha Mot11 Mot13 Mot15 Mot16

Test scale = mean(unstandardized items)

Average interitem covariance: 2.648597 Number of items in the scale: 4 Scale reliability coefficient: 0.8103

Norms relates to the service ecosystems in which the patient is located, and the rules and support obtained from them.

Three out of the four Norm items loaded factor 1 at 0.7 or above. These were taken as the first Principal Component representing norms. Cronbach’s alpha was low at 0.65 but this is a new instrument. The proportion of variance explained was 0.60.

This group of three items reflect a coherent view of norms which clearly relate to others’ perception of the patient.

. factor Belief5 Belief31 Belief32, pcf factors(1)

. rotate, orthogonal

Factor analysis/correlation Number of obs = 49 Method: principal-component factors Retained factors = 1 Rotation: orthogonal varimax (Kaiser off) Number of params = 3

--- Factor | Variance Difference Proportion Cumulative ---+--- Factor1 | 1.78478 . 0.5949 0.5949 --- LR test: independent vs. saturated: chi2(3) = 19.86 Prob>chi2 = 0.0002

Rotated factor loadings (pattern matrix) and unique variances

--- Variable | Factor1 | Uniqueness ---+---+--- Belief5 | 0.7932 | 0.3709 Belief31 | 0.7511 | 0.4359 Belief32 | 0.7691 | 0.4084 ---

. alpha Belief5 Belief31 Belief32

Test scale = mean(unstandardized items)

Average interitem covariance: 1.056548 Number of items in the scale: 3 Scale reliability coefficient: 0.6520

Based on the latent variable creation process above, it is now possible to display them in tabular form.

Table 38: Table of latent variables

Latent variable Question Factor loading Variance

explained Cronbach’s alpha Adhere(nce) Adhere1a 0.70 0.58 0.76 Adhere1b 0.79 Adhere1c 0.79 Adhere1c 0.78 Aff(ordance) Aff1c 0.72 0.66 0.87 Aff2 0.81 Aff3 0.84 Aff4 0.83 Aff5 0.86 Agency Agency1 0.86 0.61 0.79 Agency2 0.75 Agency8 0.76 Agency10 0.75 Belief Belief3 0.81 0.61 0.78 Belief14 0.76 Belief25 0.78 Belief28 0.76 Con(text) Con1c 0.69 0.61 0.78 Con1d 0.87 Con2 0.74 Con3 0.80 Mot(ivation) Mot11 0.72 0.64 0.81 Mot13 0.80 Mot15 0.86 Mot16 0.81 Norm Belief5 0.79 0.60 0.65 Belief31 0.75 Belief32 0.77

15.2.4 Step 4: Principal Component correlation matrix

The next step in statistical analysis was to create a correlation matrix of the eight first-level latent variables. This is shown in Figure 40.

. pwcorr Adhere Aff Agency Belief Con Norm Mot, obs sig

| Adhere Aff Agency Belief Con Norm Mot ---+--- Adhere | 1.0000 | | 49 | Aff | 0.2093 1.0000 | 0.1490 | 49 49 | Agency | 0.4944 0.4413 1.0000 | 0.0003 0.0015 | 49 49 49 | Belief | -0.0665 -0.1070 -0.1347 1.0000 | 0.6497 0.4641 0.3561 | 49 49 49 49 | Con | 0.1165 0.2452 0.4299 -0.3233 1.0000 | 0.4254 0.0895 0.0021 0.0235 | 49 49 49 49 49 | Norm | -0.0798 -0.1610 -0.0070 0.4585 -0.1688 1.0000 | 0.5859 0.2692 0.9617 0.0009 0.2463 | 49 49 49 49 49 49 | Mot | 0.0102 0.0375 0.0423 -0.0813 -0.0003 -0.1063 1.0000 | 0.9448 0.7979 0.7730 0.5785 0.9984 0.4674 | 49 49 49 49 49 49 49 |

Figure 40: Correlation matrix for the seven first-level latent variables

This matrix shows statistically significant correlations between Adhere(nce) and Agency, between Aff(ordance) and Agency, between Norm and Belief, between Con(text) and Agency, and between Con(text) and Belief. These are highlighted in bold text. However, it must be reiterated that any such relationship is not statistically supported and is therefore likely to be spurious.

Although no correlations are strong, any greater than 0.3 may indicate a possible regression result. However, the lack of a statistically significant relationship between latent variables does not necessarily mean that those variables are not involved in any relationships. There may be moderating relationship in which they play a part. This was examined next.

15.2.5 Step 5: Moderation analysis

This step tested for any moderating effects of the three non-patient variables on the three patient variables. The interaction term was in each case created by multiplying together the independent and putative moderation terms.

In summary, there were no statistically significant moderating effects between any of the moderating variables and the relationship of the independent variables to Adhere(nce). This means that none of the hypotheses 4-12 were found to be supported. In addition, there were no significant relationships found between the independent variables and Adhere(nce). This means that none of the hypotheses 1-3 were found to be supported.

. regress Adhere Aff Mot AffXMot

Source | SS df MS Number of obs = 49 ---+--- F(3, 45) = 0.97 Model | 2.51425967 3 .838086555 Prob > F = 0.4172 Residual | 39.0520669 45 .867823708 R-squared = 0.0605 ---+--- Adj R-squared = -0.0021 Total | 41.5663265 48 .865965136 Root MSE = .93157

Figure 41: Regression of Aff(ordance) on Mot(ivation)

. regress Adhere Aff Belief AffXBelief

Source | SS df MS Number of obs = 49 ---+--- F(3, 45) = 1.93 Model | 4.73152456 3 1.57717485 Prob > F = 0.1388 Residual | 36.834802 45 .818551155 R-squared = 0.1138 ---+--- Adj R-squared = 0.0548 Total | 41.5663265 48 .865965136 Root MSE = .90474

. regress Adhere Aff Agency AffXAgency

Source | SS df MS Number of obs = 49 ---+--- F(3, 45) = 4.89 Model | 10.2184868 3 3.40616227 Prob > F = 0.0050 Residual | 31.3478397 45 .696618661 R-squared = 0.2458 ---+--- Adj R-squared = 0.1956 Total | 41.5663265 48 .865965136 Root MSE = .83464

Figure 43: Regression of Aff(ordance) on Agency

. regress Adhere Con Mot ConXMot

Source | SS df MS Number of obs = 49 ---+--- F(3, 45) = 0.32 Model | .88018161 3 .29339387 Prob > F = 0.8076 Residual | 40.6861449 45 .904136554 R-squared = 0.0212 ---+--- Adj R-squared = -0.0441 Total | 41.5663265 48 .865965136 Root MSE = .95086

Figure 44: Regression of Con(text) on Mot(ivation)

. regress Adhere Con Belief ConXBelief

Source | SS df MS Number of obs = 49 ---+--- F(3, 45) = 0.36 Model | .964441475 3 .321480492 Prob > F = 0.7848 Residual | 40.6018851 45 .902264112 R-squared = 0.0232 ---+--- Adj R-squared = -0.0419 Total | 41.5663265 48 .865965136 Root MSE = .94988

. regress Adhere Con Agency ConXAgency

Source | SS df MS Number of obs = 49 ---+--- F(3, 45) = 5.31 Model | 10.86838 3 3.62279333 Prob > F = 0.0032 Residual | 30.6979465 45 .68217659 R-squared = 0.2615 ---+--- Adj R-squared = 0.2122 Total | 41.5663265 48 .865965136 Root MSE = .82594

Figure 46: Regression of Con(text) on Agency

. regress Adhere Norm Mot NormXMot

Source | SS df MS Number of obs = 49 ---+--- F(3, 45) = 0.20 Model | .545404186 3 .181801395 Prob > F = 0.8962 Residual | 41.0209223 45 .911576052 R-squared = 0.0131 ---+--- Adj R-squared = -0.0527 Total | 41.5663265 48 .865965136 Root MSE = .95476

Figure 47: Regression of Norm on Mot(ivation)

. regress Adhere Norm Belief NormXBelief

Source | SS df MS Number of obs = 49 ---+--- F(3, 45) = 0.12 Model | .318520704 3 .106173568 Prob > F = 0.9504 Residual | 41.2478058 45 .916617907 R-squared = 0.0077 ---+--- Adj R-squared = -0.0585 Total | 41.5663265 48 .865965136 Root MSE = .9574

. regress Adhere Norm Agency NormXAgency

Source | SS df MS Number of obs = 49 ---+--- F(3, 45) = 5.12 Model | 10.5777174 3 3.52590578 Prob > F = 0.0039 Residual | 30.9886092 45 .68863576 R-squared = 0.2545 ---+--- Adj R-squared = 0.2048 Total | 41.5663265 48 .865965136 Root MSE = .82984

Figure 49: Regression of Norm on Agency

In document Towards a process view of adherence (Page 160-174)