Evaluating Smartphone-based User Interface Designs for a2D Psychological Questionnaire

Muhsin UgurComputational Physiology Lab

University of HoustonHouston, TX 77004 USA

mugur@uh.edu

Dvijesh ShastriDept. of Computer Science

and Engineering TechnologyUniversity of Houston -

DowntownHouston, TX 77002 USA

shastrid@uhd.edu

Panagiotis TsiamyrtzisDept. of Statistics

Athens University ofEconomics and Business

Athens, Greecept@aueb.gr

Malcolm DcostaComputational Physiology Lab

University of HoustonHouston, TX 77004 USA

mtdcosta2@uh.edu

Allison KalpakciDevelopmental

Psychopathology LabUniversity of Houston

Houston, TX 77004 USAahkalpakci@uh.edu

Carla SharpDevelopmental

Psychopathology LabUniversity of Houston

Houston, TX 77004 USAcsharp2@uh.edu

Ioannis PavlidisComputational Physiology Lab

University of HoustonHouston, TX 77004 USA

ipavlidis@uh.edu

ABSTRACTThis study explored various user interface designs to tran-sition a two dimensional (2D) questionnaire from its paper-and-pencil testing format to the mobile platform. The currentadministration of the test limits its usage beyond the lab en-vironment. Creating a mobile version would facilitate ubiq-uitous administration of the test. Yet, the mobile design mustbe at least as good as its paper-based counterpart in termsof input accuracy and user interaction efforts. We developedfour user interface designs, each of which featured a specificinteraction approach. These approaches included displayingthe 2D space of the questionnaire in its original form (M1),inputting one variable at a time on the 2D space (M2), dis-solving the 2D space into two one-dimensional ordinal scales(M3), and orienting the input selections to the diagonal axes(M4). The designs were tested by a total of 34 participants,aged 18 to 52 years. The study results find the first three inter-action approaches (M1-M3) effective but the fourth approachinefficient. Furthermore, the results indicate that the two-tap

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designs (M2 and M3) are equally as good as the one-tap de-sign (M1).

Author KeywordsOnline questionnaires; 2D questionnaires; Mobile devices;Mobile health care; User interface design.

ACM Classification KeywordsH.5.2. Information Interfaces and Presentation: User Inter-faces - Evaluation/methodology, Input devices and strategies,Prototyping, Interaction styles.

INTRODUCTIONQuestionnaires are important elements of psychological andsocial science studies. Traditionally, questionnaires arepaper-and-pencil tests where study participants are requiredto mark their responses on paper. Recent proliferation of on-line survey tools (e.g., SurveyMonkey, Qualtrics, and Googleconsumer surveys) has led to the replacement of the paper-based administration of the tests with online testing. Thetools offer the convenience of completing these question-naires from anywhere at any time and on various computingplatforms including desktop, laptop, and mobile. Thus, theyfacilitate ubiquitous administration of these tests. The onlinetools are also convenient for the study experimenters as theyprovide the experimenters with instant access to the partici-pants’ data. The data can be instantly logged to online servers

and immediately made available to the experimenters for dataanalysis.

Some of these questionnaires aim to collect factual infor-mation. A demographic questionnaire is an example of thistype. A typical demographic questionnaire collects informa-tion about a person’s age, gender, education level, race, etc.Responses to these types of measures typically do not fluctu-ate with time, and hence, data input errors are relatively easyto rectify for these questionnaires.

In addition, psychological and social science studies assesspeople’s traits and states including emotions, attitudes, andperceptions for certain entities. These questionnaires are re-ferred to as psychological questionnaires. The Positive andNegative Affect Schedule (PANAS), and the State-Trait Anxi-ety Inventory (STAI) are well-known examples. Responses tovariables on these questionnaires may be time bound. There-fore, it is important to collect responses temporally close totheir actual occurrence. Equally important is to design an on-line user interface that allows the participants to input theirresponses as accurately as possible because the responses tothe psychological variables may be vulnerable to retrospec-tive recall bias [3].

A fair number of studies have been conducted to test the va-lidity and reliability of online questionnaires and the usabilityof their user interface designs to facilitate factual informationcollection [2]. However, lesser attention has been paid to on-line versions of psychological questionnaires.

Carlbring et al. investigated the effectiveness of online ver-sions of a panic disorder measure [1]. Zhu et al. evaluatedthe effectiveness of a mobile-based questionnaire designed toidentify children suffering from post-traumatic stress disor-der (PTSD) [10]. Shaik, Wong and Teo in their recent workreported how the layout of psychological questionnaires andnational culture affect people’s responses to online question-naires [8]. Vaataja and Virpi discussed user interface designguidelines specific to smartphone-based questionnaires [6].The guidelines were tested on a questionnaire that aimed toreport users’ emotions and satisfaction while interacting witha mobile journalism application.

The common denominator of these studies is the structureof their questionnaires. Typically, these questionnaires aremulti-question questionnaires in which the users are allowedto enter their responses for each question one at a time. Es-sentially, this is a one dimensional (1D) design because everystudy variable is treated independently. Typically, an ordinalscale is used to record the level of agreement or disagreementfor each study variable. The questionnaires can be either tex-tually (e.g., [1], [8], [10]) or visually (e.g., [6]) represented.The primary user interface (UI) design challenge is the layoutof the questionnaire to optimize input speed and accuracy.

Our study focuses on UI design optimization for a two di-mensional (2D) questionnaire. Two dimensional question-naires are common in psychological studies. Typically, a 2Dquestionnaire is formed by simultaneously presenting two (ormore) study variables on a 2D space. The space is discretizedinto a 2D grid in which each axis represents one study vari-

able. Usually, it is presented in a paper-and-pencil format.Participants are required to record their responses by mark-ing a single cell on the grid.

In 2010, Morris et. al presented a mobile version of a 2Dquestionnaire [4]. Specifically, they developed a mobile ap-plication called Mood Map which allows the users to recordtheir moods several times a day. Mood Map features a 2Dgrid formed by the horizontal axis representing mood di-mension and the vertical axis representing energy dimension.Their study aimed to explore the feasibility of delivering psy-chotherapies via the mobile platform. Little attention, how-ever, was given to the designing of the grid interface.

In this paper, we propose four different UI designs for a 2Dquestionnaire. The questionnaire assesses participants’ per-ceptions about others’ interpersonal behavior. Through thesedesigns, we explore various ways to interact with a 2D ques-tionnaire on the mobile platform. The fundamental designingquestion is how to optimize the user interface of the grid sothat the users can record their inputs as accurately as possi-ble with minimum input efforts.

In the remaining paper, we first introduce the 2D question-naire for which we developed four mobile interfaces. Next,we discuss the experimental design, the four user interfacedesigns, and study results. Finally, we conclude the paperwith the research summary.

THE 2D PSYCHOLOGICAL QUESTIONNAIREThe 2D questionnaire that we used in the study is a well-known measure for assessing interpersonal perceptions basedon the interpersonal circumplex [9]. The interpersonal cir-cumplex organizes interpersonal dispositions in a 2D space,with a communal dimension along the horizontal axis and anagentic dimension along the vertical axis [5]. Figure 1 showsthe layout of the grid. The grid is arranged in 11 columnsand 11 rows depicting 11 discrete levels of the communal andagentic dimensions, respectively. The communal dimensionrepresents efforts that augment affiliation and interpersonalconnectedness. It is ranged from Cold-Quarrelsome interac-tion on the left to Warm-Agreeable interaction on the right ofthe grid. The agentic dimension represents efforts that servedesires for autonomy and control. It is ranged from Assured-Dominant interaction on the top to Unassured-Submissive in-teraction on the bottom of the grid.

Traditionally, the interpersonal grid has been used in a paper-and-pencil format. Participants describe their perceptions ofothers’ behavior by marking their responses in a single cell ofthe grid, indicating the extent to which the other person wasperceived as agreeable (vs. quarrelsome) and as dominant(vs. submissive) in a specific interaction. The grid featuresfour possible interaction outcomes: 1) Engaging interactionanchored at the top-right corner of the grid, 2) Critical in-teraction anchored at the top-left corner of the grid, 3) With-drawn interaction anchored at the bottom-left corner of thegrid, and 4) Deferring interaction anchored at the bottom-right corner of the grid. Reliability and validity of the Inter-personal Grid is presented in [5].

Figure 1: The 2D psychological questionnaire.

EXPERIMENTAL DESIGN

ParticipantsA total of n = 34 participants (17 males + 17 females) vol-unteered for this study. Their ages ranged between 18 and 52years (µ ± σ = 30.5 ± 9.4). The participants were recruitedwithin the University of Houston’s premises via emails andadvertisement flyers. They received a $20 gift-card for theirparticipation. The study was approved by the university’s in-stitutional review board.

ProcedureThe study participants paid a single visit to the experiment.At the beginning of the experiment, participants were askedto complete the study consent form, a demographic form, anda smartphone familiarity form. About a minute after that, theexperiment began. The experiment was divided into five trials- one trial for the paper-and-pencil test, and the remainingfour trials for the mobile versions. The paper version wastreated as the gold-standard, against which the performanceof the four mobile designs were compared. The order of thetrials was randomized in a Latin Square fashion to counter-balance the order effect. Two successive trials were separatedby about a minute long break in which the participants wereasked to rest.

Figure 2 (a) shows the experimental setup for the paper trial,and Figure 2 (b) shows the experimental setup for the mo-bile trials. In each of these trials the participants were askedto report their most recent social interaction before arrivingfor the experiment. The interpersonal circumplex defines asocial interaction as an interaction with one other individuallasting five or more minutes. By allowing the interactions tobe outside the experimental setup, we were able to evaluate amajority of the spatial area of the grid.

Before completing the grid, the participants completed twoother questionnaires for all five trials. The first questionnairecollected the participant’s current emotional states on a seven-point scale. Specifically, the questionnaire recorded 12 emo-tions including happy, pleased, worried, depressed, ashamed,

(a) Paper trial (b) Mobile trial

Figure 2: Experimental setup

etc. The second questionnaire collected the information of theindividual with whom the participant interacted. Specifically,it recorded the individual’s age, gender, and the relationshipwith the participant. These two questionnaires were laid outin the exact same fashion in all four mobile designs. There-fore, they were not included in the design analysis. They werepart of the experiment primarily to avoid having the partici-pants carelessly input their responses for the grid. The deliv-ery order of the three questionnaires was the same for all fivetrials.

In the paper trial, the participants were asked to completethese three questionnaires in the paper-and-pencil format (SeeFigure 2 (a)). Each questionnaire was printed on a separate 8-by-11 inch paper. The time spent on each questionnaire wasrecorded.

In the mobile trials, the participants were asked to completethe same three questionnaires on an iPhone 5. The 2D ques-tionnaire is typically completed six times a day in clinicalstudies. Therefore, it is more practical to use a smartphonethan a tablet for the mobile versions. To facilitate the mo-bile trials, we developed an app in iOS 8. The app featuresthree UI views, one per questionnaire. The views were pre-sented in the same order as the paper trial. Moreover, thefirst two views did not differ between the mobile trials as theywere not the focus of the UI design. The third view presentedthe 2D grid. We developed four UI designs for the 2D grid.Each of the four mobile trials presented one of the four griddesigns. The responses to these grid designs were later eval-uated against the paper version.

For each UI design, the app logged three values: time spenton the grid, number of taps, and final cell selection. The timespent is defined as elapsed time between entering the gridview screen and proceeding to the next screen. The num-ber of taps is defined as the total number of taps that the usermade on the grid for inputing his/her response.

The participants were required to hold the phone in the por-trait fashion, as this orientation matched with its paper-basedcounterpart. Before the beginning of the first mobile trial, the

experimenter instructed the participants on how to proceedbetween the multiple views of the app.

At the end of each trial, the participants were asked to com-plete the NASA task load index (NASA-TLX) questionnaireand a usability questionnaire. The NASA task load indexevaluated the participants’ perceptions about their interactionefforts with the designs. The usability questionnaire evalu-ated the UI designs from the visual aesthetic and operatingconvenience standpoints.

USER INTERFACE DESIGNSFigure 3 illustrates the four UI designs of the 2D question-naire. The first design (M1) is a replica of the 2D question-naire’s paper-and-pencil format (see Figure 3 (a)). The usershad to tap at least once to input their responses. Thus, M1 fea-tures a one-tap design. Refining or reentering of the responserequires the grid to be reset first by deselecting its current se-lection.

The remaining three designs are two-tap designs. The usershad to tap at least twice to input their responses. By addingone additional tap, we aimed to achieve improved usabilitywithout sacrificing input accuracy. The two-tap designs easethe cognitive demand by allowing the users to input their re-sponses for one variable at a time.

The second design (M2) preserves the spatial visualization ofthe grid (see Figure 3 (b)). It demands two taps in series: thefirst tap is to input a response for the communal dimension,and the second tap is to input a response for the agentic di-mension. The interface guides the users by first enabling thecells in the middle row (see the left view), and then enablingthe cells in the corresponding column (see the middle view).The input process is illustrated in Figure 3 (b). Refining orreentering of the response requires the grid to be reset to itsinitial state. This happens in the reverse order in which theuser has to first deselect the current row selection (the agenticdimension) and then the current column selection (the com-munal dimension).

The third design (M3) dissolves the spatial representation ofthe grid (see Figure 3 (c)). It presents the 2D grid as two1D ordinal scales, one scale per grid dimension. This repre-sentation is common for online questionnaires, and hence theuser would have familiarity of its handling. A slider objectcontrol of iOS 8 is used as an input interface. The design de-mands two taps in series: the first tap is to input a communalresponse and the second tap is to input an agentic response.The final selection is superimposed on the grid. The inputprocess is illustrated in Figure 3 (c). The response refinementor reselection can be achieved in any order via the sliders.

The fourth design (M4) is similar to the second design withonly difference is that it features two diagonal selections (seeFigure 3 (d)). Here, the users can directly input their re-sponses for the interaction outcomes. Since the questionnairededuces a social interaction into critical, deferring, with-drawn, or engaging interactions, we decided to let the usersinput their responses for these outcomes directly. The de-sign demands two taps in series: the first tap is to input the

(a) Grid Design M1

(b) Grid Design M2

(c) Grid Design M3

(d) Grid Design M4

Figure 3: Four UI designs of the 2D questionnaire.

response for the Critical - Deferring interactions, and the sec-

ond tap is to input the response for the Withdrawn - Engaginginteractions. The input process is illustrated in Figure 3 (d).The response refinement is achieved in the reverse order sim-ilar to M2.

APP ARCHITECTUREWe developed an iPhone app to support the four UI designs.Figure 4 shows a schematic diagram of the app framework.The framework features the client-server architecture. Theweb server uses PHP scripts for handling the client’s requestsand MySQL database for data storage. The database consistsof two tables: User table and Log table. The user table storesthe user’s ID, whereas the log table stores the user interactiondata including grid ID, time spent on grid, number of taps,and the final cell selection.

Figure 4: App Architecture

Once the users input their data, the app passes the data alongwith the other necessary parameters to the web server. Theweb server accepts data from the iPhone client, logs the data,and sends back appropriate messages to the iPhone client inthe JSON format.

DATA ANALYSISWhen factual information is collected, the aim is to collectresponses that are deliberate and accurate, but in case of thepsychological information the aim is to collect responses thatare spontaneous [7]. Unlike factual information, spontaneousresponses are vulnerable to retrospective recall bias. Fur-thermore, mobile questionnaires are meant for data collec-tion outside the lab environment where the experimenters arenot available to rectify the users’ mistakes. Therefore, a userinterface for a psychological questionnaire should be as intu-itive as possible to minimize the design ambiguity and max-imize input accuracy. Moreover, the interface should not beperceived as mentally demanding. Otherwise, the users maynot be able to provide in-the-moment responses consistentlyby delaying the interaction or avoiding the interaction com-pletely. Both actions are detrimental to the study because theformer action potentially logs inaccurate responses while thelatter action generates missing values. Considering these fac-tors, we evaluated the proposed UI designs along the threeaxes: User input consistency, user input effort, and user per-ception.

User Input ConsistencyAn ambiguous user interface may cause the users to inputtheir responses differently from what they would enter for

the paper-based test. Thus, by comparing the users’ mobileinputs against their inputs for the paper-based test, one canidentify design ambiguity. Figure 5 illustrates the grid inputsfor the entire study population. Each grid shows a partici-pant’s five responses - one response for the paper trial andfour responses for the mobile trials. The figure visualizes allthe input responses in a single view. Two observations canbe drawn from it. First, the input responses are all over thegrid indicating that the study included a majority of the inter-action outcomes. Second, except for one case (P6), the inputresponses vary by design.

We performed statistical analysis to test the significance ofthe response variation. Assuming the paper-based test asground truth, we computed Manhattan distance between eachmobile design input and paper input. The boxplots in Figure6 present these distances for the entire study population. Thedistance measurement signifies input consistency of a mobiledesign. A lower distance value indicates lower input errorand hence, higher input consistency, whereas a higher dis-tance value indicates lower input consistency. Among thefour designs, M1 shows the highest input consistency whileM4 shows the lowest input consistency.

M1 M2 M3 M4

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Figure 6: Boxplot diagrams represent the Manhattan distancebetween the paper inputs and each mobile design inputs forthe entire study population (n = 34).

We performed repeated-measures ANOVA test on the dis-tance variable. The test reveals significant mean differencesin input consistency among the four designs (mixed effectsANOVA, p = 0.0010). Post analysis indicates that M4 hassignificantly higher mean Manhattan distance error than therest of the designs, indicating that M4 has the lowest inputconsistency among the proposed designs. Remaining threedesigns (M1-M3) do not have significant mean Manhattan er-ror differences (mixed effects ANOVA, p = 0.3926).

User Input EffortWe computed the number of excessive taps that the partici-pants made before finalizing their responses. Specifically, we

P1 P2 P3 P4 P5

P6 P7 P8 P9 P10

P11 P12 P13 P14 P15

P16 P17 P18 P19 P20

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Figure 5: The grid inputs of the entire study population (n = 34). Each grid shows a participant’s five responses including oneresponse for the paper version and four responses for the mobile versions (M1-M4).

subtracted the minimum required taps per design from thetotal number of taps made per design by each participant.Higher number of excessive steps indicates more input ef-fort from the user. We ran repeated-measures ANOVA teston all four designs. The test shows that the variability in theexcessive number of taps for all four designs is statisticallyinsignificant (p = 0.3802). Thus, all the designs demand onaverage the same amount of effort from the users.

User Perception

Analysis of the NASA-TLX questionnaire:To evaluate how the participants perceived their interactionswith each design, we analyzed their responses to the NASA-TLX questionnaire. The questionnaire evaluated their per-ceptions for six variables: mental demand, physical demand,temporal demand, performance, effort, and frustration.

We performed repeated-measures ANOVA tests for each vari-able. The analysis reveals that the participants perceived theM4 design significantly mentally demanding (p = 0.0262).We believe that the design ambiguity of M4 may have madethe interaction mentally demanding for the participants. Theother five variables, physical demand (p = 0.1014), tempo-ral demand (p = 0.8138), performance (p = 0.2775), effort(p = 0.1148), and frustration (p = 0.1477) were not statisti-cally different among all four designs. The boxplots in Figure7 present NASA-TLX scores for the entire study population.

Analysis of the usability questionnaire:To evaluate how the participants perceived the usability ofeach design, we analyzed their responses to the usabilityquestionnaire. The questionnaire included the followingquestions:

Q1. The interface is aesthetic. Agree?Q2. The interface is ambiguous. It took me a while to

understand how to input my response. Agree?Q3. The interface is easy to use. Agree?Q4. I learned to use it quickly. Agree?

We performed non-parametric Kruskal Wallis test for eachvariable. The test reveals that the distributions of responsesto Q1 for all four designs are not statistically different (p =0.1225), which indicates that the aesthetics of M1-M4 do notdiffer significantly. The test for Q2, Q3 and Q4 reports sig-nificant difference among the 4 designs (p = 0.0016 for Q2,p = 0.0014 for Q3, and p = 0.0013 for Q4). ExcludingM4 in the follow-up analysis reveals that the distributions ofresponses to Q2, Q3 and Q4 for designs M1-M3 are not sta-tistically different (p = 0.624 for Q2, p = 0.2686 for Q3,and p = 0.709 for Q4). These indicate that M4 was per-ceived ambiguous, difficult to use and hard to learn. The barcharts in Figure 8 present usability scores for the entire studypopulation.

CONCLUSIONThis research explores various ways to interact with a 2Dquestionnaire on the mobile platform. Two dimensional ques-tionnaires are common in psychological studies. Yet, mostquestionnaires are administered in paper-and-pencil format.

Mental score: How mentally

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Design Design

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Physical score: How physically

demanding was the task?

Temporal score: How hurried or rushed

was the pace of the task?

Performance score: How succes-

sful were you in accomplishing what

you were asked to do?

Effort score: How hard did you

have to work to accomplish your

level of performance?

Frustration score: How insecure,

discouraged, irritated, stressed,

and annoyed were you?

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Figure 7: Boxplot diagrams represent analysis of the NASATask Load Index questionnaire for the entire study population(n = 34). The participants were required to respond to eachof these variables on a 20-point scale where 1 being very lowand 20 being very high.

This is an inconvenient approach, particularly when a ques-tionnaire needs to be completed multiple times a day. Hav-ing the questionnaire on the mobile platform allows ubiqui-tous data collection. The challenge, however, is to designintuitive user interfaces that facilitate seamless user interac-tions. This requires consideration of the smartphone’s view-ing space, which is much smaller than the standard paper-and-pencil testing format.

In addition, the UI design should take into account the inter-action efforts demanded by a 2D questionnaire. Typically, a2D questionnaire is formed by simultaneously presenting two(or more) study variables on a 2D space. The users have toinput the variables’ values in a single selection. This is funda-mentally different from most online questionnaires in whicheach study variable is treated independently, and presentedon a 1D ordinal scale. Handling a 2D questionnaire is cogni-tively more demanding.

The proposed UI designs are developed with having theseconsiderations in mind. Specifically, we developed four UIdesigns for a 2D psychological questionnaire which is a well-

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User Perception

Figure 8: Bar graphs represent analysis of the usability ques-tionnaire for the entire study population (n = 34). The par-ticipants responded to each of these questions on a five-pointscale where 1 being Not at all and 5 being completely.

known measure for assessing participants’ perceptions aboutothers’ interpersonal behavior. Each design featured a spe-cific interaction approach. Design M1 is a replica of the 2Dquestionnaire’s paper test. Design M2 preserves the spatialvisualization of the questionnaire but lets the user handle onevariable at a time. Design M3 dissolves the spatial visual-ization and presents both variables on two 1D ordinal scales.This representation is common for the online questionnaireand hence, the users would find it easy to interact with. Thelast design, M4, orients M2 for diagonal selections. In placeof the two orthogonal dimensions (communal and agentic),this design allows selections of their interactions (engaging,critical, withdrawn, and deferring) directly. All the designswere tested by a total of 34 participants.

The study results show that M4 is the most inefficient user in-terface design. The participants not only made more mistakeswhile entering their responses but also perceived the designmentally demanding, ambiguous, difficult to learn and use.Two lessons can be learned from this finding. First, a userinterface design featuring meta-data input should be discour-aged. Second, inputing responses along the diagonal dimen-sions is not a conventional practice and hence, should be usedwith caution. A combination of both could be an ineffectiveUI design such as M4.

The performance across the remaining three designs (M1-M3) is not statistically different indicating that a two-tap de-sign (M2-M3) was not necessarily better than a one-tap de-sign (M1). A field study in which participations are requiredto input their responses multiple times a day is necessary tofurther verify this finding.

In the near future, we plan on expanding these designs to afield study in which the users will input their responses sixtimes a day for an extended period of time. The field studywill not include the M4 design, since it has been observed asthe most inefficient user interface design.

ACKNOWLEDGMENTSThis research was supported by the Eckhard-Pfeiffer Distin-guished Professorship fund of Ioannis Pavlidis. We wouldlike to thank all the volunteers who participated in our study.

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