Development of a New Measurement Tool to Evaluate the Impact of Technology on Parents' Interactions with Their Children: The Parent Technoference Scale

Development of a New Measurement Tool to Evaluate the Impact of Technology on Parents' Interactions with Their Children: The Parent Technoference Scale | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development of a New Measurement Tool to Evaluate the Impact of Technology on Parents' Interactions with Their Children: The Parent Technoference Scale Özlem Gözün Kahraman, Aysel Korkmaz, Zeynep Sena Derdiyok This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7155648/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study aimed to develop a valid and reliable measurement tool to evaluate the impact of technology-induced interruptions (parental technoference) on interactions between parents and their children aged 4–9. Parent Technoference Scale (PTS) items were rated on a five-point Likert scale (1 to 5), and both forms included three control items. A total of 549 parents with children aged 4–9 participated in the study. Data were collected from 151 participants for exploratory factor analysis (Study-1) and from 398 participants for confirmatory factor analysis (Study-2). Following these analyses, PTS-A was refined to 12 items, and PTS-B to 11 items, with both forms including two reverse-scored items. Factor analysis revealed two distinct sub-dimensions in each form: Interaction of Simultaneous Time and Interaction of Discipline and Safeness. Higher scores on the scale indicate greater levels of parental technoference. The results demonstrated that the Parent Technoference Scale is a robust tool for assessing the degree of technology-induced disruptions in parent-child interactions. Both PTS-A and PTS-B showed high reliability, with Cronbach's alpha values exceeding 0.80 for PTS-A and 0.90 for PTS-B. The internal consistency of both forms and their sub-dimensions was strong, with acceptable item correlations. Furthermore, the scale showed excellent concurrent and discriminant validity. technology technoference parenting parent-child relationship scale Figures Figure 1 Figure 2 Figure 3 Highlights This is a study to develop a valid and reliable measurement tool to assess the impact of technology-related interruptions on interactions between parents and their children. A measurement tool consisting of two forms, PTS-A with 12 items and PTS-B with 11 items, and each form having two sub-dimensions was obtained. The reliability of PTS-A and PTS-B showed strong internal consistency with its subscales. Item correlations for the full scale and its subscales were within acceptable ranges for both forms. The validity of the PTS-A and PTS-B was strong, both concurrent and discriminant validity values The CFA results for both forms confirmed the two-factor structure of the scale, and the model fit indices (RMSEA, SRMR, IFI, TLI, and CFI) indicated an adequate fit. INTRODUCTION In recent years, digital technology tools have become an integral part of daily life, serving both facilitative and functional roles. The positive effects of digital technology tools such as smartphones, the internet, and social media on an individual's life cannot be denied. Still, they are likely to bring various difficulties in interpersonal relationships. These tools can also negatively affect the quality of parent-child relationships, critical for the child's healthy development process. According to the Mobile Communications Sector Report, while 98% of adults in Turkey use a mobile phone, 77% use a smartphone (MOBİSAD, 2020). The "Global Mobile User Research" conducted by the consultancy firm Deloitte with more than 53 thousand participants in 33 countries stated that mobile users in Turkey could not stop looking at their mobile phone screen an average of 78 times a day; in other words, every 13 minutes. Additionally, 66% of users admitted using their phones more than necessary (Deloitte, 2017). These results show how widespread digital technology is. Parents' use of technology Today, many people view digital technologies-such as mobile phones, the internet, and social media-as indispensable to everyday living. The mobility of these devices can often disrupt daily and family interactions as people can use them anytime and anywhere (McDaniel, 2015; McDaniel & Radesky, 2018b). There are studies on the use of mobile devices by parents and its effect on family relationships. As a result of a study conducted by Common Sense Media on parents' media use, it was determined that parents use a high amount of screen media and use most of their screen time (more than 7 hours/day on average) for personal reasons that are not related to work (Lauricella et al., 2016). Another study conducted with 553 children between the ages of 2 and 9 and their mothers revealed that mothers spend an average of 4.33 hours a day in front of a technological device (Poulain et al., 2019). In their study examining family communication around media and mobile devices using natural observational methodology, Domoff et al. (2018) stated that when parents focused on their mobile devices, they responded to their children's attempts to attract attention by talking less and observed more parental opposition. These findings highlight the pervasive presence of digital technology in daily life, and given the mobility of these devices, they frequently interrupt daily interactions. Definition of parental technoference and the effects on child development Technoference is defined as the interference and interruptions in interpersonal interactions or shared time caused by the use of digital and mobile technology devices (McDaniel, 2015). In other words, technoference refers to the disruption of social interactions and communication resulting from technology use (Akbağ & Sayıner, 2021). Parental technoference , which refers to the regular disruption of face-to-face communication, interactions, or shared time within the family due to parents' use of technology, can significantly hinder interactions with children of all ages by reducing the attention, responsiveness, and warmth that parents provide (Mackay et al., 2022). Technoference undermines feelings of interpersonal connection and cohesion in romantic relationships, friendships and parent-child interactions (Stockdale et al., 2018). Excessive use of mobil devices can negatively affect parenting quality or the coordination between parents, potentially leading to undesirable outcomes for children's development and well-being. Parents' overuse of technology can cause significant disruptions to family interactions (Mangan et al., 2018; McDaniel & Coyne, 2016b). It is estimated that parents spend an average of nine hours per day using digital media, with three of those hours specifically on smartphones. During family activities that are crucial for shaping children's social-emotional health such as mealtimes, playtimes, and bedtimes, parents frequently use mobile devices (McDaniel & Radesky, 2018a). The presence of digital media during these key family moments can distract parents, making them less attentive and responsive to their children's needs. These technology-induced interruptions can impair parents' ability to track their children's gaze, reduce shared attention, weaken parental responsiveness, and negatively affect language development (Knitter & Zemp, 2020; Morris et al., 2022). In a study by McDaniel & Coyne (2016b) 65% of mothers reported that their interactions were interrupted by technology during playtime, 36% during reading time, 26% during mealtimes and bedtime, and 22% during discipline and boundary-setting activities. Chamam et al. (2024) highlights the importance of the quality of parent-child interaction, noting that parental sensitivity-defined as the ability to detect, acknowledge, and respond to a child's behaviors and communicative cues-diminishes when a parent's focus shifts from the child to a digital device due to technoference, adversely affecting their responsiveness. In other words, when parents use screens during interactions with their children, the children tend to display more negative emotions and engage in more behaviors aimed at capturing their mothers' attention. According to Lemish et al. (2020), in their study observing parents and children in playgrounds, 79% of parents were observed using their mobile phones at least once, spending approximately one-third of their time in the playground engaged with their phones. The study found that children faced safety risks that went unnoticed or were detected late by their parents. When parents were preoccupied with their mobile devices, they often missed opportunities to reinforce their child's normative behaviors and overlooked non-normative behaviors. In contrast, parents who did not use mobile phones made significantly more eye contact with their children, praised them for overcoming challenges, supported their cooperative behavior with other children, appropriately responded to their bids for attention, intervened to stop dangerous or abnormal behaviors, and showed affection through talking, hugging, kissing, or caressing. In a similar study by Elias et al. (2021) parents of children aged 2 to 6 were observed in playgrounds and restaurants. This study found that technoference raised various concerns regarding children's safety and emotional well-being. Furthermore, the findings indicated that when parents used mobile devices in such public settings, they missed opportunities to help their children develop important social skills such as patience, cooperation, turn-taking, and kindness. The frequent and intensive use of mobile technology by parents, which diminishes their sensitivity and attentiveness during interactions with their children, can lead to reduced parental engagement, decreased responsiveness, and increased conflict with their children. This overuse also limits crucial verbal and non-verbal social exchanges necessary for fostering children's optimal growth and development (Gözün Kahraman & Özdemir, 2022; Kuzu Jafari, 2021). Over time, mobile device use may gradually replace parental sensitivity and interaction, reducing valuable opportunities for parents to guide their children's behavior and support their emotional regulation (Bauer, 2018; Elias et al., 2021; Lemish et al., 2020). When parents allow mobile devices to frequently disrupt family interactions, children may begin to perceive them as less available sources of emotional support, weakening the bonds of mutual closeness and trust (Meeus et al., 2021). Ultimately, the pervasive presence of digital technology in daily life can profoundly affect parent-child relationships, leading parents to miss critical opportunities to nurture their child's development and respond effectively to their needs. Measuring parental technoference The first measurement tools developed to assess technoference were designed to measure the level of technoference between couples. McDaniel & Coyne (2016a) created the Technology Device Interference Scale (TDIS) to evaluate how frequently technological devices interfere with interactions between partners, using a six-point scale (0: never, 5: all the time). In addition, the Technology Interference in Life Examples Scale (TILES) was developed to assess the frequency of technoference in specific situations, such as during meals, conversations, leisure time, and general time spent together. The TILES consists of five items (e.g., "1. During a typical mealtime that my partner and I spend together, my partner pulls out and checks their phone or mobile device. 2. My partner sends texts or emails to others during our face-to-face conversations...") and uses an eight-point scale (0:never, 7:ten or more times a day) to measure the frequency of technoference in these contexts. These measurement tools were later adapted for use in studies aimed at assessing parental technoference, with the scale items modified to reflect interactions between parents and their children. These measurement tools of McDaniel & Coyne (2016a) were used in studies examining the relationship between technoference and behavioural outcomes (McDaniel & Radesky, 2018a; 2018b; Stockdale et al., 2018), co-parenting (McDaniel et al., 2018), phone addiction (Liu et al., 2020; Qiao & Liu, 2020), executive functions (Yang et al., 2023), social anxiety (Ji et al., 2024) and problematic smartphone use (Shao et al., 2024). Additionally, Meeus et al. (2021) used a parent-adapted version of the Roberts & David (2016) partner phubbing scale to determine parental technoference. The current study This study aims to raise parents' awareness of parental technoference by helping them recognize its impact on their interactions with their children. While the literature shows that measurement tools designed to assess technoference between couples have been adapted for measuring parental technoference, a dedicated tool specifically for assessing parental technoference in parent-child interactions has yet to be developed. Therefore, this study seeks to create a valid and reliable instrument to fill this gap. The newly developed Parent Technoference Scale is expected to serve as a valuable tool for researchers investigating the levels of parental technoference and its developmental effects on children. METHODS Scale Development Process There is currently no standardized measurement tool available to assess technology-induced interruptions during interactions between parents and their children aged 4-9, or during time spent together. To address this gap, researchers have decided to develop a valid and reliable instrument to measure the level of parental technoference in parent-child interactions. Before initiating the scale development process, the researchers thoroughly reviewed the relevant scale development literature (DeVellis, 2017; Irwing & Hughes, 2018; Price, 2017). Following this review, they adhered to DeVellis (2017) eight-step process for developing a valid and reliable scale, as outlined in Figure 1. The steps of DeVellis (2017) scale development process, as outlined in Figure 1, have been applied in this study as follows: Step 1: The selection of this study's topic was guided by prior research on technoference, a thorough literature review, identified gaps in previous studies, and the researchers' expertise. To develop a tool for assessing parental technoference, the researchers examined literature on daily interactions, communication, and time spent between parents and children during early and middle childhood (Mackay et al., 2022; McDaniel, 2015, 2020; McDaniel & Coyne, 2016a; McDaniel & Radesky, 2018a, 2018b; McDaniel et al., 2018) (DeVellis, 2017). Theoretical explanations of technoference were analyzed to identify potential interruptions and distinguish between different types. Adopting a critical approach, the researchers reviewed existing measures of technoference in parent-child interactions, evaluating their applicability before establishing the specific objective of the study. The measurement tool was designed in two forms: one for parents to self-evaluate and another to assess their partner. This approach addresses potential bias in self-assessment and acknowledges variations in daily interactions between parents. Assessing both perspectives offers a more comprehensive understanding of parental technoference. Step 2: The researchers developed a comprehensive item pool to assess parental technoference, focusing on items that captured daily interactions, communication, and routines between parents and children, as well as the time they spent together. In constructing these items, the researchers adhered to Nardi’s (2018) recommendations, avoiding leading, vague, or double-barreled questions, as well as negative, repetitive, or abbreviated statements. Following these guidelines, parallel item pools were created, each consisting of 24 items. Step 3: Data were collected in three large Turkish cities during the scale development process. For the pilot study, 32 parents (17 mothers, 15 fathers) with children aged 4–9 were recruited as a representative sample. For the Exploratory Factor Analysis (EFA), 151 participants (Study-1) were included, meeting the recommended sample size of at least five times the number of items (Kass & Tinsley, 1979; Tabachnik & Fidell, 2013). To validate the factor structure, Confirmatory Factor Analysis (CFA) was conducted with 398 participants (Study-2), aligning with sample size guidelines (Cohen et al., 2002; Jackson, 2001). Demographic details for Study-1 and Study-2 are provided in Table 1. Step 4: Expert evaluation, which involves feedback from specialists in relevant fields, is essential for strengthening a scale's content and face validity. In this study, the researchers reviewed previous scale development studies (Dedeoğlu et al., 2020; Fehl-Seward, 2021; Williams et al., 2021), which typically involved consulting five to ten experts. For this study, feedback was collected from seven experts: four specializing in child development, one in measurement and evaluation, one in media, and one in language. These experts were asked to review the initial item pool (PTS-A: 24 items / PTS-B: 24 items) to ensure the appropriateness, clarity, and relevance of the items. Based on the experts' feedback, the following adjustments were made: three items were removed due to redundancy with other items, two items were excluded because they did not fit the daily routines of the target age group (children aged 4-9, focusing on activities such as eating, playing, reading, and sleeping), and one item was removed because it included the phrase "is with me," which could artificially inflate the measured level of technoference. Additionally, in response to suggestions from three experts, items containing the phrases "look and respond" were revised to "give a response" for better clarity. Following these revisions, both the PTS-A and PTS-B forms were reduced to 18 items each and resubmitted to the experts for final approval. Once approval was granted, data collection for the analysis of the PTS commenced. Step 5: At this stage of scale development, the researchers focused on identifying items that could potentially introduce bias into participants' responses, which could in turn affect the construct validity of the scale (DeVellis, 2017). To address this, they specifically assessed the validity of the items through concurrent and discriminant validity methods. Detailed information and findings from these analyses are presented in Study-1 and Study-2. Information on 6 step “Data Collection” and 7 step “Findings” are included. Step 8: The scale's length was reviewed and revised to balance reliability and brevity (DeVellis, 2017). Exploratory Factor Analysis (EFA) was used to identify items for removal, aiming for a Cronbach's Alpha between .80 and .90. A pilot study evaluated the scale’s clarity, completion time, and usability, leading to final revisions. The finalized PTS-A includes 12 items in two sub-dimensions, while the PTS-B consists of 11 items in two sub-dimensions, each with two reverse-coded items. Scoring ranges from 5 to 60 for PTS-A and 5 to 55 for PTS-B, with higher scores indicating greater parental technoference in parent-child interactions. Research Ethics In the planning, implementation, and reporting stages of this study, all the guidelines outlined in the Higher Education Institutions Scientific Research and Publication Ethics Directive were strictly followed. Additionally, before data collection began, ethical approval was obtained from the X University Social and Human Sciences Research Ethics Committee during a meeting held on 19.06.2023, under decision number 2023/5-7. Participants were thoroughly informed about the study before data collection, and those willing to participate were asked to complete and sign an informed consent form. In accordance with the Helsinki Declaration, consent was obtained from all participating parents. Participants were also informed that they could stop completing the scale or withdraw from the study at any time. The collected data were securely stored on a password-protected computer within the institution. No identifying information that could reveal participants' identities was included in the scale forms or the research report, ensuring confidentiality throughout the process. Participants Data were collected in three large Turkish cities during the scale development process. For the pilot study, 32 parents (17 mothers, 15 fathers) with children aged 4–9 were recruited as a representative sample. For the Exploratory Factor Analysis (EFA), 151 participants (Study-1) were included, meeting the recommended sample size of at least five times the number of items (Kass & Tinsley, 1979; Tabachnik & Fidell, 2013). To validate the factor structure, Confirmatory Factor Analysis (CFA) was conducted with 398 participants (Study-2), aligning with sample size guidelines (Cohen et al., 2002; Jackson, 2001). Demographic details for Study-1 and Study-2 are provided in Table 1. Table 1. Demographics of the Participants Study-1 (EFA; n=151) Study-2 (CFA; n=398) F % F % Parent Mother 122 80.8 312 78.4 Father 29 19.2 86 21.6 Child’s Age 4-5 64 41.4 161 40.5 6-7 43 28.5 109 27,4 8-9 44 29.1 128 32.2 Income Low 5 3.3 28 7 Middle 114 75.5 286 71.9 High 32 21.2 84 21.1 When examining Table 1, it is observed that in the first study, 80.8% of the participants were mothers and 19.2% were fathers. Of these parents, 41.4% had children aged 4-5, 28.5% had children aged 6-7, and 29.1% had children aged 8-9. Regarding income levels, 3.3% of the families were categorized as having low income, 75.5% as having medium income, and 21.2% as having high income. In the second study, 78.4% of the participants were mothers and 21.6% were fathers. Among these parents, 40.5% had children aged 4-5, 27.4% had children aged 6-7, and 32.2% had children aged 8-9. In terms of income, 7% of the families were classified as low-income, 71.9% as medium-income, and 21.1% as high-income. Data Collection For this study, after obtaining approval from the X University Ethics Committee, data were collected by the researchers in three phases through face-to-face interactions: the pilot study in July 2023 (n=32), the EFA in September 2023 (n=151), and the CFA in October 2023 (n=398). The researchers randomly selected parents with children aged 4-9 from three large cities in Turkey and informed them about the study’s purpose. After the parents completed the voluntary consent form, they were asked to fill out a personal information form, along with the PTS-A form (self-evaluation) and the PTS-B form (evaluation of their partner), while the researcher was present. Participants took approximately 10-15 minutes to complete the scales. RESULTS Study-1 The descriptive statistics, correlational analyses, and EFA were conducted using IBM's SPSS-26 program (IBM Corp., 2019). To confirm the validity of the factor structure for both the PTS-A and the PTS-B, CFA was performed using the AMOS-26 software program (Arbuckle, 2019). A significance value criterion of p < .05 was used in all analyses to establish statistical significance. To minimize the potential impact of outliers on the findings, the data underwent a process of cleansing and outlier management, following the guidelines of Tabachnick and Fidell (2013). After identifying and removing two univariate and two multivariate outliers, further analyses were conducted on the remaining sample, which included 151 cases. Parent Technoference Scale-A Form (PTS-A) Exploratory Factor Analysis The Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy and Bartlett's Test of Sphericity were conducted to evaluate the suitability of the sample for factor analysis. The KMO value was found to be .88, indicating commendable sample adequacy, while the Chi-Square value for Bartlett's Test of Sphericity was 858.44 (df = 66, p < .05), suggesting that the correlations between items were sufficient for factor analysis. A preliminary analysis using oblique rotation (direct oblimin) was conducted to identify the primary factors. The analysis revealed two factors with eigenvalues greater than 1, accounting for 59.18% of the total variance. This two-factor solution was deemed appropriate and approved for further analysis. The factor labeled Interaction of Simultaneous Time accounted for 45.56% of the variance, while the factor labeled Interaction of Discipline and Safeness accounted for 13.63% of the variance. The first five items loaded onto the first factor, while the subsequent seven items loaded onto the second factor (See Table 2). Table 2. Factor Analysis Findings of Parent Technoference Scale Parent Technoference Scale-A Form Parent Technoference Scale-B Form No Factor 1: Interaction of simultaneous time Factor 2: Interaction of discipline and safeness No Factor 1: Interaction of simultaneous time Factor 2: Interaction of discipline and safeness Item 1 .821 Item 1 .944 Item 2 .805 Item 2 .935 Item 3 .800 Item 3 .871 Item 4 .677 Item 4 .824 Item 5 -.582 Item 5 -.673 Item 6 .806 Item 6 .937 Item 7 .795 Item 7 .895 Item 8 .750 Item 8 .885 Item 9 -.740 Item 9 -.878 Item 10 .732 Item 10 .833 Item 11 .724 Item 11 .824 Item 12 .567 Internal Consistency Results The assessment of internal consistency was performed separately for the entire scale as well as for each of its two components. The internal consistency coefficient for the entire scale was .88, indicating strong reliability. For the subscales, Interaction of Simultaneous Time demonstrated a coefficient of .81, and Interaction of Discipline and Safeness showed a coefficient of .87, both indicating acceptable levels of internal consistency. Concurrent and Discriminant Validity To assess the concurrent and discriminant validity of the PTS-A, the researchers compared participants' scores on the scale with two constructs: one conceptually similar (control items-A form) and another conceptually dissimilar (control items-B form). As shown in Table 3, there was a significant correlation between PTS-A and control items-A (r = .41, p = .001), supporting the scale’s concurrent validity. In contrast, the correlation between PTS-B and control items-A was not statistically significant (r = .11, p = .163), providing evidence for the scale’s discriminant validity. Table 3. Correlation Between Control Items and Parent Technoference Scales Control Items-A Control Items-B PTS-A PTS-B Control Items-A Control Items-B .24 ** PTS-A .41 ** .24 ** PTS-B .11 .67 ** .45 ** **p <0.05 Parent Technoference Scale-B Form (PTS-B) Exploratory Factor Analysis The Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy and Bartlett's Test of Sphericity were first conducted to evaluate the suitability of the sample for factor analysis. The KMO value was found to be .93, indicating a high level of sampling adequacy, while the Chi-Square value for Bartlett's Test of Sphericity was 1725.47 (df = 66, p < .001), suggesting strong correlations between items and the appropriateness of the data for factor analysis. A preliminary analysis using oblique rotation (direct oblimin) was performed to identify the main factors. The analysis revealed two factors with eigenvalues greater than 1, explaining a significant portion of the variance, specifically 77.80%. However, Item-6 was excluded from the analysis due to cross-loading on both factors. After removing Item-6, a further component analysis using oblique rotation confirmed the two-factor solution, which explained 79.29% of the total variance. The two-factor solution was deemed optimal for further investigation. The first factor, Interaction of Simultaneous Time, accounted for 64.93% of the total variance, while the second factor, Interaction of Discipline and Safeness, accounted for 14.36% of the variance. The first five items loaded onto the first factor, while the next six items loaded onto the second factor. Internal Consistency Results The assessment of internal consistency was conducted separately for the entire scale as well as for each of its components. The internal consistency coefficient for the whole scale was found to be .94, indicating high reliability. For the subscales, Interaction of Simultaneous Time demonstrated a coefficient of .92, and Interaction of Discipline and Safeness showed a coefficient of .95, both reflecting strong internal consistency and reliability for the subscales. Concurrent and Discriminant Validity To evaluate the concurrent and discriminant validity of the PTS-B, the researchers performed a comparative analysis of participants' scores on two constructs: one conceptually similar (control items-B) and another conceptually dissimilar (control items-A). As shown in Table 3, there was a significant correlation between PTS-B and control items-B (r = .67, p = .001), supporting the scale’s concurrent validity. However, this correlation was not as strong as anticipated when compared to other findings. Additionally, a significant but weaker correlation was found between PTS-A and control items-B (r = .24, p = .003), providing partial evidence of discriminant validity. While the correlation was statistically significant, it was notably weaker than the correlation between PTS-B and control items-B, indicating some support for the scale's discriminant validity. Study-2 Parent Technoference Scale-A Form (PTS-A) Confirmatory Factor Analysis To assess the validity of the two-dimensional model (Interaction of Simultaneous Time and Interaction of Discipline and Safeness) of the PTS-A, confirmatory factor analyses were conducted using AMOS 26 software (Arbuckle, 2019). The initial two-factor solution demonstrated an adequate fit: χ² (53, N = 398) = 142.879, p = .001; RMSEA = .065, IFI = .949, TLI = .936, CFI = .949. The modification indices suggested that easing parameter restrictions between e10 (Item-10) and e12 (Item-12) could improve the model fit. Indeed, the model fit improved considerably when the revised analysis included the covariance between the error terms of these two items as a free parameter: χ² (52, N = 398) = 121.342, p = .001; RMSEA = .058, IFI = .961, TLI = .950, CFI = .961. The standard regression weights from this analysis are depicted in Figure 2. Internal Consistency Results The assessment of internal consistency was conducted separately for the entire scale as well as for each of its two components. The internal consistency coefficient for the whole scale was found to be .85, indicating good reliability. For the subscales, Interaction of Simultaneous Time exhibited a coefficient of .78, and Interaction of Discipline and Safeness showed a value of .84. Concurrent and Discriminant Validity To assess the concurrent and discriminant validity of the PTS-A, the researchers compared participants' scores on the scale with two constructs: one conceptually similar (control items-A) and another conceptually dissimilar (control items-B). The results shown in Table 4 indicate a significant correlation between PTS-A and control items-A (r = .47, p = .001), providing strong evidence for concurrent validity. Additionally, a significant but weaker correlation was found between PTS-A and control items-B (r = .29, p = .001), which was notably less robust than the correlation with control items-A. These findings offer partial support for discriminant validity. Table 4. Correlation Between Control Items and Parent Technoference Scale Control Items-A Control Items-B PTS-A PTS-B Control Items-A Control Items-B .31 ** PTS-A .47 ** .29 ** PTS-B .21 .71 ** .41 ** **p <0.05 Parent Technoference Scale-B Form (PTS-B) Confirmatory Factor Analysis To assess the validity of the two-dimensional model (Interaction of Simultaneous Time and Interaction of Discipline and Safeness) of the PTS-B, confirmatory factor analyses were conducted using AMOS 26 software (Arbuckle, 2019). The initial two-factor solution did not demonstrate an optimal fit: χ² (43, N = 398) = 164.685, p = .001; RMSEA = .084, IFI = .968, TLI = .959, CFI = .968. However, based on modification indices, easing parameter restrictions between e11 (Item-11) and e12 (Item-12) improved the model's fit. The model fit improved significantly when the revised analysis included the covariance between the error terms of these two items as a free parameter: χ² (42, N = 398) = 135.403, p = .001; RMSEA = .075, IFI = .975, TLI = .967, CFI = .975. This adjustment enhanced the model’s overall fit. The standard regression weights from this analysis are displayed in Figure 3. Internal Consistency Results The assessment of internal consistency was conducted separately for the entire scale as well as for each of its two components. The internal consistency coefficient for the whole scale was found to be .94, indicating excellent reliability. For the subscales, Interaction of Simultaneous Time demonstrated a coefficient of .91, and Interaction of Discipline and Safeness showed a coefficient of .94, both reflecting strong internal consistency and reliability for their respective components. Concurrent and Discriminant Validity To evaluate the concurrent and discriminant validity of the PTS-B, the researchers conducted a comparative analysis of participants' scores on the scale in relation to two constructs: one that is conceptually similar (control items-B) and another that lacks conceptual similarities (control items-A). The findings shown in Table 4 indicate a significant association between PTS-B and control items-B (r = .71, p = .001), providing strong support for the scale's concurrent validity. Additionally, a significant but weaker correlation was found between PTS-B and control items-A (r = .29, p = .001), which was notably less robust than the correlation with control items-B. These findings offer partial support for the scale's discriminant validity. DISCUSSION This study aimed to develop a measurement tool to assess the level of technology-induced interruptions in parent-child interactions during daily life. In this context, the PTS was created to determine the technoference levels of parents with children aged 4–9. The PTS consists of two forms: one where parents assess themselves (PTS-A) and another where they evaluate their partner (PTS-B). Both forms include two reverse-coded items, with PTS-A containing 12 items and PTS-B containing 11 items. Each form is structured around two factors: Factor 1: Interaction of Simultaneous Time and Factor 2: Interaction of Discipline and Safeness. The first factor, "Interaction of Simultaneous Time," measures behaviors related to family interactions during conversations, meals, sleep, and both indoor and outdoor activities. The second factor, "Interaction of Discipline and Safeness," evaluates parental responses to the child's demands, safety needs, and positive and negative behaviors throughout the day. To develop and validate the Parent Technoference Scale, two studies were conducted, with data collected in two phases. In the first study, data from 151 parents were used for EFA, and in the second study, data from 398 parents were used for CFA. As a result of the study, the Parent Technoference Scale proved to be highly effective in determining the level of technology-induced interruptions in parents' daily interactions with their children. In terms of reliability, both the A and B forms of the scale, along with their sub-dimensions, demonstrated strong internal consistency. The item correlations for the full scale and its sub-dimensions were within acceptable ranges for both forms. The internal consistency coefficients were also highly satisfactory. From a validity standpoint, both concurrent and discriminant validity values for the A and B forms were robust, confirming that the Parent Technoference Scale is a reliable and valid tool for measuring technoference levels in parents with children aged 4–9. Additionally, the results of the CFA for both forms confirmed the two-factor structure of the scale, with model fit indices (RMSEA, SRMR, IFI, TLI, and CFI) indicating an adequate fit across parents of children aged 4–9. Parents play a crucial role in supporting many aspects of their child's development through daily interactions. Routines such as mealtime, sleep, and playtime provide essential opportunities to nurture children's social and emotional growth. However, interruptions caused by technological devices during these critical moments can result in missed opportunities for meaningful engagement. Furthermore, important parenting responsibilities, such as implementing discipline and ensuring the child's safety, may be neglected when parents are excessively occupied with technological devices. Technoference -the interference of technology in family interactions-can create tension in both children and parents, disrupt family routines, and affect societal roles. To effectively support a child's social and emotional development, parents need to help their child manage emotions, solve problems, and understand their mental state and behavioral motivations. However, parents who frequently use mobile devices during parent-child activities are less likely to comprehend their child's mental state and intentions (Çakır & Köseliören, 2022 ; McDaniel et al., 2018 ; McDaniel & Radesky, 2018a ). Technological tools, which have seamlessly integrated into individuals' daily lives, have also introduced changes to parental interactions and practices. Many parents instinctively respond to messages and notifications on their smartphones without fully recognizing the potential for subsequent communication interruptions. The PTS can help raise awareness of these technology-induced disruptions and encourage mindfulness, allowing parents to avoid missing key opportunities for their children's development. The developed PTS can be employed in various experimental, descriptive, and correlational studies to assess the effects of technology on the parent-child relationship. The scale has been validated as a reliable and effective tool for researchers to investigate the impact of technology on child development, highlighting its value in understanding how digital interference shapes family dynamics. LIMITATIONS The participants consisted of parents of children aged 4–9 who were randomly selected from schools in three large cities in Turkey, which limits the generalizability of the findings to a broader population. The data collected were based on self-reported information from the parents regarding both themselves and their partners, which may introduce bias. Furthermore, the analysis results obtained through the developed measurement tool are limited to assessing technoference from the parents' perspectives. FUTURE RESEARCH Future large-scale and more diverse studies should continue investigating the relationship between parental technoference and parent-child interactions. Additionally, technoference scales should be developed for other age groups, such as middle childhood, late childhood, and adolescence. There is also a need for research focused on creating evidence-based intervention programs designed to reduce technoference in parent-child interactions. Declarations Compliance With Ethical Standards Conflict of Interest The authors declare no competing interests. Author Contribution Ö.G.K. and Z.S.D. wrote the introduction and discussion sections. All authors were involved in the data collection process.A.K. performed statistical analysis of the data and wrote up the results. All authors reviewed the manuscript. References Akbağ, M. & Sayıner, B. (2021). The reflections of digital technology: parental technoference and phubbing. Humanistic Perspective, 3 (3):753-778. https://doi.org/10.47793/hp.996220 Arbuckle, J. L. (2019). Amos 26.0 User's Guide . IBM SPSS. Bauer, N. S. (2018). Technoference over time and parenting. Pediatric Research, 84 (2): 157-158. https://doi.org/10.1038/s41390-018-0059-z Chamam, S., Forcella, A., Musio, N., Quinodoz, F., & Dimitrova, N. (2024). Effects of digital and non-digital parental distraction on parent-child ınteraction and communication. Frontiers in Child and Adolescent Psychiatry , 3 , 1330331. https://doi.org/10.3389/frcha.2024.1330331 Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2002). Applied multiple regression/correlation analysis for the behavioral sciences . 3rd ed. Routledge. https://doi.org/10.4324/9780203774441 Çakır, C., & Köseliören, M. (2022). Technoference as technology ınterference in the communication process: a study on married couples. Erciyes İletişim Dergisi, 9 (2): 609-626. https://doi.org/10.17680/erciyesiletisim.1091267 Dedeoğlu, B. B., Taheri, B., Okumus, F., & Gannon, M. (2020). Understanding the ımportance that consumers attach to social media sharing (ISMS): scale development and validation. Tourism Management, 76:103954. https://doi.org/10.1016/j.tourman.2019.103954 Deloitte (2017). Global mobil kullanıcı araştırma raporu . https://www2.deloitte.com/tr/tr/pages/technology-media-and-telecommunications/articles/global-mobile-consumer-survey-2017.html. DeVellis, R. F. (2017). Scale development: theory and applications . 4th ed. Thousand Oaks, CA: Sage. Devine, D. (2023). Antecedents and consequences of parent technology use in parents of young children. Doctoral dissertation, Virginia Polytechnic Institute and State University. Domoff, S. E., Radesky, J. S., Harrison, K., Riley, H., Lumeng, J. C., & Miller, A. L. (2019). A naturalistic study of child and family screen media and mobile device use. Journal of Child and Family Studies, 28:401-410. https://doi.org/10.1007/s10826-018-1275-1 Elias, N., Lemish, D., Dalyot, S., & Floegel, D. (2021). “Where are you?” an observational exploration of parental technoference in public places in the US and Israel. Journal of Children and Media, 15 (3):376-388. https://doi.org/10.1080/17482798.2020.1815228 Fehl-Seward, M. A. (2022). Development of an ınstrument to measure parent and caregiver perspectives of elementary online pedagogy, practices, and supports. Doctoral dissertation, Regent University. Floyd, F. J., & Widaman, K. F. (1995). Factor analysis in the development and refinement of clinical assessment ınstruments. Psychological Assessment, 7 (3): 286-299. https://psycnet.apa.org/doi/10.1037/1040-3590.7.3.286 Gözün Kahraman, Ö., & Özdemir, E.S. (2022). Dijital dünyanın çocuğun gelişimine etkileri. In Dijital Çağda Çocuk ve Ebeveyn Olmak , Ed. Ö. Gözün Kahraman, p.57-91. Nobel. IBM Corp. (2019). Statistical product and service solutions (IBM SPSS Version, 26) . http://www.ibm.com/products/spss-statistics. Irwing, P., & Hughes, D. J. (2018). Test development. In The Wiley handbook of psychometric testing: a multidisciplinary reference on survey, scale and test development , Eds. P. Irwing, T. Booth, & D. J. Hughes, p. 4-47. Hoboken, NJ: Wiley. Jackson, D. L. (2001). Sample size and number of parameter estimates in maximum likelihood confirmatory factor analysis: a monte carlo ınvestigation. Structural EquationModeling, 8 (2):205-223. https://doi.org/10.1207/S15328007SEM0802_3 Ji, X., Feng, N., & Cui, L. (2024). The serial mediation role of parent-child attachment and empathy in the relationship between parental technoference and social anxiety. Current Psychology : 1-11. https://doi.org/10.1007/s12144-024-06109-y Knitter, B., & Zemp, M. (2020). Digital family life: a systematic review of the ımpact of parental smartphone use on parent-child ınteractions. Digital Psychology, 1 (1): 29-43. https://doi.org/10.24989/dp.v1i1.1809 Kuzu Jafari, K. (2021). Investigation of the relationship between ıntelligence levels and social skills of early childhood children and parenting and digital parenting attitudes of families. Doctoral Thesis, Uludağ University. Lauricella, A. R., Cingel, D. P., Beaudoin-Ryan, L., Robb, M. B., Saphir, M., & Wartella, E. A. (2016). The Common Sense Census: plugged-in parents of tweens and teens . CA: Common Sense Media. Lemish, D., Elias, N., & Floegel, D. (2020). “Look at me!” Parental use of mobile phones at the playground. Mobile Media & Communication, 8 (2):170-187. https://doi.org/10.1177/2050157919846916 Liu, Q., Wu, J., Zhou, Z., & Wang, W. (2020). Parental technoference and smartphone addiction in chinese adolescents: the mediating role of social sensitivity and loneliness. Children and Youth Services Review, 118: 105434. https://doi.org/10.1016/j.childyouth.2020.105434 Mackay, L. J., Komanchuk, J., Hayden, K. A., & Letourneau, N. (2022). Impacts of parental technoference on parent-child relationships and child health and developmental outcomes: a scoping review protocol. Systematic Reviews, 11 (1): 1-7. https://doi.org/10.17605/OSF.IO/QNTS5 Mangan, E., Leavy, J. E., & Jancey, J. (2018). Mobile device use when caring for children 0‐5 years: a naturalistic playground study. Health Promotion Journal of Australia, 29 (3): 337-343. https://doi.org/10.1002/hpja.38 McDaniel, B. T. (2015). “Technoference”: Everyday intrusions and interruptions of technology in couple and family relationships. In C. J. Bruess (Ed.), Family communication in the age of digital and social media . Peter Lang Publishing. McDaniel, B. T. (2020). Technoference: parent mobile device use and ımplications for children and parent-child relationships. Zero To Three, 41 (2): 30-36. McDaniel, B. T., & Coyne, S. M. (2016a). Technoference: the ınterference of technology in couple relationships and ımplications for women's personal and relational well-being. Psychology of Popular Media Culture, 5 (1):85–98. https://psycnet.apa.org/doi/10.1037/ppm0000065 McDaniel, B. T., & Coyne, S. M. (2016b). Technology ınterference in the parenting of young children: ımplications for mothers’ perceptions of coparenting. The Social Science Journal, 53 (4): 435-443. https://doi.org/10.1016/j.soscij.2016.04.010 McDaniel, B. T., & Radesky, J. S. (2018a). Technoference: longitudinal associations between parent technology use, parenting stress, and child behavior problems. Pediatric Research, 84 (2): 210-218. https://doi.org/10.1038/s41390-018-0052-6 McDaniel, B. T., & Radesky, J. S. (2018b). Technoference: parent distraction with technology and associations with child behavior problems. Child Development, 89 (1): 100-109. https://doi.org/10.1111/cdev.12822 McDaniel, B. T., Galovan, A. M., Cravens, J. D., & Drouin, M. (2018). ‘Technoference’ and implications for mothers' and fathers' couple and coparenting relationship quality. Computers in Human Behavior, 80:303-313. https://doi.org/10.1016/j.chb.2017.11.019 Meeus, A., Coenen, L., Eggermont, S., & Beullens, K. (2021). Family technoference: exploring parent mobile device distraction from children’s perspectives. Mobile Media & Communication, 9 (3):584-604. https://doi.org/10.1177/2050157921991602 Mobil İletişim Araçları ve Bilgi Teknolojileri İş Adamları Derneği (MOBİSAD) (2020). Mobil iletişim sektör raporu . https://mobisad.org/dergi/mobisad-17/pdf/mobisad-17.pdf. Morris, A. J., Filippetti, M. L., & Rigato, S. (2022). The impact of parents’ smartphone use on language development in young children. Child Development Perspectives, 16 (2): 103-109. https://doi.org/10.1111/cdep.12449 Nardi, P. M. (2018). Doing survey research: a guide to quantitative methods (4th ed.) . Routledge. Poulain, T., Ludwig, J., Hiemisch, A., Hilbert, A., & Kiess, W. (2019). Media use of mothers, media use of children, and parent–child interaction are related to behavioral difficulties and strengths of children. International Journal of Environmental Research and Public Health, 16 (23): 4651. https://doi.org/10.3390/ijerph16234651 Price, L. R. (2017). Psychometric methods: theory into practice . The Guilford Press. Qiao, L., & Liu, Q. (2020). The effect of technoference in parent-child relationships on adolescent smartphone addiction: the role of cognitive factors. Children and Youth Services Review, 118: 105340. https://doi.org/10.1016/j.childyouth.2020.105340 Roberts, J. A., & David, M. E. (2016). My life has become a major distraction from my cell phone: partner phubbing and relationship satisfaction among romantic partners. Computers in Human Behavior, 54:134–141. https://doi.org/10.1016/j.chb.2015.07.058 Shao, T., Zhu, C., Lei, H., Jiang, Y., Wang, H., & Zhang, C. (2024). The relationship of parent-child technoference and child problematic smartphone use: the roles of parent-child relationship, negative parenting styles, and children’s gender. Psychology Research and Behavior Management, 17: 2067. https://doi.org/10.2147/PRBM.S456411 Stockdale, L. A., Coyne, S. M., & Padilla-Walker, L. M. (2018). Parent and child technoference and socioemotional behavioral outcomes: a nationally representative study of 10-to 20-year-old adolescents. Computers in Human Behavior, 88: 219-226. https://doi.org/10.1016/j.chb.2018.06.034 Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics. 6th ed. Pearson Education. Williams, J., Gazley, A., & Ashill, N. (2021). Children’s perceived value: conceptualization, scale development, and validation. Journal of Retailing, 97 (2): 301–315. https://doi.org/10.1016/j.jretai.2020.05.008 Yang, X., Jiang, P., & Zhu, L. (2023). Parental problematic smartphone use and children's executive function: The mediating role of technoference and the moderating role of children's age. Early Childhood Research Quarterly , 63 :219-227.https://doi.org/10.1016/j.ecresq.2022.12.017 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7155648","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511391379,"identity":"d7668d3c-76a1-4956-b5e3-ee0cb208d6ba","order_by":0,"name":"Özlem Gözün Kahraman","email":"","orcid":"","institution":"Karabük University","correspondingAuthor":false,"prefix":"","firstName":"Özlem","middleName":"Gözün","lastName":"Kahraman","suffix":""},{"id":511391380,"identity":"286a8d4d-c4b3-4da3-be63-b22fa15e1a61","order_by":1,"name":"Aysel Korkmaz","email":"","orcid":"","institution":"Bozok Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Aysel","middleName":"","lastName":"Korkmaz","suffix":""},{"id":511391381,"identity":"e5516e18-f9ac-4dc7-b7ff-bdee404a5d3e","order_by":2,"name":"Zeynep Sena Derdiyok","email":"data:image/png;base64,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","orcid":"","institution":"Duzce University","correspondingAuthor":true,"prefix":"","firstName":"Zeynep","middleName":"Sena","lastName":"Derdiyok","suffix":""}],"badges":[],"createdAt":"2025-07-18 08:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7155648/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7155648/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90899336,"identity":"8fe2f190-82cc-423e-9718-b9bc24d8986a","added_by":"auto","created_at":"2025-09-09 11:59:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104564,"visible":true,"origin":"","legend":"\u003cp\u003eEight Stages of Scale Development (DeVellis, 2017)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7155648/v1/624978e2ca19bc7a7715704c.png"},{"id":90899337,"identity":"bfc73295-8782-4452-9fca-659d5189b279","added_by":"auto","created_at":"2025-09-09 11:59:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":193632,"visible":true,"origin":"","legend":"\u003cp\u003eStandard Regression Coefficients for Parent Technoference Scale – A Form\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7155648/v1/7d73e06c2ff36c79887c937e.png"},{"id":90899339,"identity":"3ef2a925-9e36-4bcb-9506-191ec8f39d3c","added_by":"auto","created_at":"2025-09-09 11:59:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":164037,"visible":true,"origin":"","legend":"\u003cp\u003eStandard Regression Coefficients for Parent Technoference Scale-B Form\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7155648/v1/e4a6f1305c4f756902e8af26.png"},{"id":90901267,"identity":"1a054edc-b589-4f63-b97e-b271a4fd03e5","added_by":"auto","created_at":"2025-09-09 12:23:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1570506,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7155648/v1/81ccc43e-af76-4887-bf12-d870cd948488.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of a New Measurement Tool to Evaluate the Impact of Technology on Parents' Interactions with Their Children: The Parent Technoference Scale","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eThis is a study to develop a valid and reliable measurement tool to assess the impact of technology-related interruptions on interactions between parents and their children.\u003c/li\u003e\n \u003cli\u003eA measurement tool consisting of two forms, PTS-A with 12 items and PTS-B with 11 items, and each form having two sub-dimensions was obtained.\u003c/li\u003e\n \u003cli\u003eThe reliability of PTS-A and PTS-B showed strong internal consistency with its subscales. Item correlations for the full scale and its subscales were within acceptable ranges for both forms. The validity of the PTS-A and PTS-B was strong, both concurrent and discriminant validity values\u003c/li\u003e\n \u003cli\u003eThe CFA results for both forms confirmed the two-factor structure of the scale, and the model fit indices (RMSEA, SRMR, IFI, TLI, and CFI) indicated an adequate fit.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eIn recent years, digital technology tools have become an integral part of daily life, serving both facilitative and functional roles. The positive effects of digital technology tools such as smartphones, the internet, and social media on an individual\u0026apos;s life cannot be denied. Still, they are likely to bring various difficulties in interpersonal relationships. These tools can also negatively affect the quality of parent-child relationships, critical for the child\u0026apos;s healthy development process. According to the Mobile Communications Sector Report, while 98% of adults in Turkey use a mobile phone, 77% use a smartphone (MOBİSAD, 2020). The \u0026quot;Global Mobile User Research\u0026quot; conducted by the consultancy firm Deloitte with more than 53 thousand participants in 33 countries stated that mobile users in Turkey could not stop looking at their mobile phone screen an average of 78 times a day; in other words, every 13 minutes. Additionally, 66% of users admitted using their phones more than necessary (Deloitte, 2017). These results show how widespread digital technology is.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParents\u0026apos; use of technology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eToday, many people view digital technologies-such as mobile phones, the internet, and social media-as indispensable to everyday living. The mobility of these devices can often disrupt daily and family interactions as people can use them anytime and anywhere (McDaniel, 2015; McDaniel \u0026amp; Radesky, 2018b). There are studies on the use of mobile devices by parents and its effect on family relationships. As a result of a study conducted by Common Sense Media on parents\u0026apos; media use, it was determined that parents use a high amount of screen media and use most of their screen time (more than 7 hours/day on average) for personal reasons that are not related to work (Lauricella et al., 2016). Another study conducted with 553 children between the ages of 2 and 9 and their mothers revealed that mothers spend an average of 4.33 hours a day in front of a technological device (Poulain et al., 2019). In their study examining family communication around media and mobile devices using natural observational methodology, Domoff et al. (2018) stated that when parents focused on their mobile devices, they responded to their children\u0026apos;s attempts to attract attention by talking less and observed more parental opposition. These findings highlight the pervasive presence of digital technology in daily life, and given the mobility of these devices, they frequently interrupt daily interactions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinition of parental technoference and the effects on child development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTechnoference\u003c/strong\u003e is defined as the interference and interruptions in interpersonal interactions or shared time caused by the use of digital and mobile technology devices (McDaniel, 2015). In other words, technoference refers to the disruption of social interactions and communication resulting from technology use (Akbağ \u0026amp; Sayıner, 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParental technoference\u003c/strong\u003e, which refers to the regular disruption of face-to-face communication, interactions, or shared time within the family due to parents\u0026apos; use of technology, can significantly hinder interactions with children of all ages by reducing the attention, responsiveness, and warmth that parents provide (Mackay et al., 2022).\u003c/p\u003e\n\u003cp\u003eTechnoference undermines feelings of interpersonal connection and cohesion in romantic relationships, friendships and parent-child interactions (Stockdale et al., 2018). Excessive use of mobil devices can negatively affect parenting quality or the coordination between parents, potentially leading to undesirable outcomes for children\u0026apos;s development and well-being. Parents\u0026apos; overuse of technology can cause significant disruptions to family interactions (Mangan et al., 2018; McDaniel \u0026amp; Coyne, 2016b).\u003c/p\u003e\n\u003cp\u003eIt is estimated that parents spend an average of nine hours per day using digital media, with three of those hours specifically on smartphones. During family activities that are crucial for shaping children\u0026apos;s social-emotional health such as mealtimes, playtimes, and bedtimes, parents frequently use mobile devices (McDaniel \u0026amp; Radesky, 2018a). The presence of digital media during these key family moments can distract parents, making them less attentive and responsive to their children\u0026apos;s needs. These technology-induced interruptions can impair parents\u0026apos; ability to track their children\u0026apos;s gaze, reduce shared attention, weaken parental responsiveness, and negatively affect language development (Knitter \u0026amp; Zemp, 2020; Morris et al., 2022). In a study by McDaniel \u0026amp; Coyne (2016b) 65% of mothers reported that their interactions were interrupted by technology during playtime, 36% during reading time, 26% during mealtimes and bedtime, and 22% during discipline and boundary-setting activities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChamam et al. (2024) highlights the importance of the quality of parent-child interaction, noting that parental sensitivity-defined as the ability to detect, acknowledge, and respond to a child\u0026apos;s behaviors and communicative cues-diminishes when a parent\u0026apos;s focus shifts from the child to a digital device due to technoference, adversely affecting their responsiveness. In other words, when parents use screens during interactions with their children, the children tend to display more negative emotions and engage in more behaviors aimed at capturing their mothers\u0026apos; attention.\u003c/p\u003e\n\u003cp\u003eAccording to Lemish et al. (2020), in their study observing parents and children in playgrounds, 79% of parents were observed using their mobile phones at least once, spending approximately one-third of their time in the playground engaged with their phones. The study found that children faced safety risks that went unnoticed or were detected late by their parents. When parents were preoccupied with their mobile devices, they often missed opportunities to reinforce their child\u0026apos;s normative behaviors and overlooked non-normative behaviors. In contrast, parents who did not use mobile phones made significantly more eye contact with their children, praised them for overcoming challenges, supported their cooperative behavior with other children, appropriately responded to their bids for attention, intervened to stop dangerous or abnormal behaviors, and showed affection through talking, hugging, kissing, or caressing.\u003c/p\u003e\n\u003cp\u003eIn a similar study by Elias et al. (2021) parents of children aged 2 to 6 were observed in playgrounds and restaurants. This study found that technoference raised various concerns regarding children\u0026apos;s safety and emotional well-being. Furthermore, the findings indicated that when parents used mobile devices in such public settings, they missed opportunities to help their children develop important social skills such as patience, cooperation, turn-taking, and kindness. The frequent and intensive use of mobile technology by parents, which diminishes their sensitivity and attentiveness during interactions with their children, can lead to reduced parental engagement, decreased responsiveness, and increased conflict with their children. This overuse also limits crucial verbal and non-verbal social exchanges necessary for fostering children\u0026apos;s optimal growth and development (G\u0026ouml;z\u0026uuml;n Kahraman \u0026amp; \u0026Ouml;zdemir, 2022; Kuzu Jafari, 2021). Over time, mobile device use may gradually replace parental sensitivity and interaction, reducing valuable opportunities for parents to guide their children\u0026apos;s behavior and support their emotional regulation (Bauer, 2018; Elias et al., 2021; Lemish et al., 2020).\u003c/p\u003e\n\u003cp\u003eWhen parents allow mobile devices to frequently disrupt family interactions, children may begin to perceive them as less available sources of emotional support, weakening the bonds of mutual closeness and trust (Meeus et al., 2021). Ultimately, the pervasive presence of digital technology in daily life can profoundly affect parent-child relationships, leading parents to miss critical opportunities to nurture their child\u0026apos;s development and respond effectively to their needs.\u003c/p\u003e\n\u003ch2\u003eMeasuring parental technoference\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe first measurement tools developed to assess technoference were designed to measure the level of technoference between couples. McDaniel \u0026amp; Coyne (2016a) created the Technology Device Interference Scale (TDIS) to evaluate how frequently technological devices interfere with interactions between partners, using a six-point scale (0: never, 5: all the time). In addition, the Technology Interference in Life Examples Scale (TILES) was developed to assess the frequency of technoference in specific situations, such as during meals, conversations, leisure time, and general time spent together. The TILES consists of five items (e.g., \u0026quot;1. During a typical mealtime that my partner and I spend together, my partner pulls out and checks their phone or mobile device. 2. My partner sends texts or emails to others during our face-to-face conversations...\u0026quot;) and uses an eight-point scale (0:never, 7:ten or more times a day) to measure the frequency of technoference in these contexts. These measurement tools were later adapted for use in studies aimed at assessing parental technoference, with the scale items modified to reflect interactions between parents and their children. These measurement tools of McDaniel \u0026amp; Coyne (2016a) were used in studies examining the relationship between technoference and behavioural outcomes (McDaniel \u0026amp; Radesky, 2018a; 2018b; Stockdale et al., 2018), co-parenting (McDaniel et al., 2018), phone addiction (Liu et al., 2020; Qiao \u0026amp; Liu, 2020), executive functions (Yang et al., 2023), social anxiety (Ji et al., 2024) and problematic smartphone use (Shao et al., 2024). Additionally, Meeus et al. (2021) used a parent-adapted version of the Roberts \u0026amp; David (2016) partner phubbing scale to determine parental technoference.\u003c/p\u003e\n\u003ch2\u003eThe current study\u003c/h2\u003e\n\u003cp\u003eThis study aims to raise parents\u0026apos; awareness of parental technoference by helping them recognize its impact on their interactions with their children. While the literature shows that measurement tools designed to assess technoference between couples have been adapted for measuring parental technoference, a dedicated tool specifically for assessing parental technoference in parent-child interactions has yet to be developed. Therefore, this study seeks to create a valid and reliable instrument to fill this gap. The newly developed Parent Technoference Scale is expected to serve as a valuable tool for researchers investigating the levels of parental technoference and its developmental effects on children.\u003c/p\u003e"},{"header":"METHODS","content":"\u003ch2\u003eScale Development Process\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThere is currently no standardized measurement tool available to assess technology-induced interruptions during interactions between parents and their children aged 4-9, or during time spent together. To address this gap, researchers have decided to develop a valid and reliable instrument to measure the level of parental technoference in parent-child interactions. Before initiating the scale development process, the researchers thoroughly reviewed the relevant scale development literature (DeVellis, 2017; Irwing \u0026amp; Hughes, 2018; Price, 2017). Following this review, they adhered to DeVellis (2017) eight-step process for developing a valid and reliable scale, as outlined in Figure 1.\u003c/p\u003e\n\u003cp\u003eThe steps of DeVellis (2017) scale development process, as outlined in Figure 1, have been applied in this study as follows:\u003c/p\u003e\n\u003cp\u003eStep 1: The selection of this study\u0026apos;s topic was guided by prior research on technoference, a thorough literature review, identified gaps in previous studies, and the researchers\u0026apos; expertise. To develop a tool for assessing parental technoference, the researchers examined literature on daily interactions, communication, and time spent between parents and children during early and middle childhood (Mackay et al., 2022; McDaniel, 2015, 2020; McDaniel \u0026amp; Coyne, 2016a; McDaniel \u0026amp; Radesky, 2018a, 2018b; McDaniel et al., 2018) (DeVellis, 2017). Theoretical explanations of technoference were analyzed to identify potential interruptions and distinguish between different types. Adopting a critical approach, the researchers reviewed existing measures of technoference in parent-child interactions, evaluating their applicability before establishing the specific objective of the study.\u003c/p\u003e\n\u003cp\u003eThe measurement tool was designed in two forms: one for parents to self-evaluate and another to assess their partner. This approach addresses potential bias in self-assessment and acknowledges variations in daily interactions between parents. Assessing both perspectives offers a more comprehensive understanding of parental technoference.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStep 2:\u003c/em\u003e The researchers developed a comprehensive item pool to assess parental technoference, focusing on items that captured daily interactions, communication, and routines between parents and children, as well as the time they spent together. In constructing these items, the researchers adhered to Nardi\u0026rsquo;s (2018) recommendations, avoiding leading, vague, or double-barreled questions, as well as negative, repetitive, or abbreviated statements. Following these guidelines, parallel item pools were created, each consisting of 24 items.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStep 3:\u003c/em\u003e Data were collected in three large Turkish cities during the scale development process. For the pilot study, 32 parents (17 mothers, 15 fathers) with children aged 4\u0026ndash;9 were recruited as a representative sample.\u003c/p\u003e\n\u003cp\u003eFor the Exploratory Factor Analysis (EFA), 151 participants (Study-1) were included, meeting the recommended sample size of at least five times the number of items (Kass \u0026amp; Tinsley, 1979; Tabachnik \u0026amp; Fidell, 2013).\u003c/p\u003e\n\u003cp\u003eTo validate the factor structure, Confirmatory Factor Analysis (CFA) was conducted with 398 participants (Study-2), aligning with sample size guidelines (Cohen et al., 2002; Jackson, 2001). Demographic details for Study-1 and Study-2 are provided in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStep 4:\u003c/em\u003e Expert evaluation, which involves feedback from specialists in relevant fields, is essential for strengthening a scale\u0026apos;s content and face validity. In this study, the researchers reviewed previous scale development studies (Dedeoğlu et al., 2020; Fehl-Seward, 2021; Williams et al., 2021), which typically involved consulting five to ten experts. For this study, feedback was collected from seven experts: four specializing in child development, one in measurement and evaluation, one in media, and one in language. These experts were asked to review the initial item pool (PTS-A: 24 items / PTS-B: 24 items) to ensure the appropriateness, clarity, and relevance of the items.\u003c/p\u003e\n\u003cp\u003eBased on the experts\u0026apos; feedback, the following adjustments were made: three items were removed due to redundancy with other items, two items were excluded because they did not fit the daily routines of the target age group (children aged 4-9, focusing on activities such as eating, playing, reading, and sleeping), and one item was removed because it included the phrase \u0026quot;is with me,\u0026quot; which could artificially inflate the measured level of technoference. Additionally, in response to suggestions from three experts, items containing the phrases \u0026quot;look and respond\u0026quot; were revised to \u0026quot;give a response\u0026quot; for better clarity.\u003c/p\u003e\n\u003cp\u003eFollowing these revisions, both the PTS-A and PTS-B forms were reduced to 18 items each and resubmitted to the experts for final approval. Once approval was granted, data collection for the analysis of the PTS commenced.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStep 5:\u003c/em\u003e At this stage of scale development, the researchers focused on identifying items that could potentially introduce bias into participants\u0026apos; responses, which could in turn affect the construct validity of the scale (DeVellis, 2017). To address this, they specifically assessed the validity of the items through concurrent and discriminant validity methods. Detailed information and findings from these analyses are presented in Study-1 and Study-2.\u003c/p\u003e\n\u003cp\u003eInformation on \u003cem\u003e6 step\u0026nbsp;\u003c/em\u003e\u0026ldquo;Data Collection\u0026rdquo; and \u003cem\u003e7 step\u003c/em\u003e \u0026ldquo;Findings\u0026rdquo; are included.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStep 8:\u003c/em\u003e The scale\u0026apos;s length was reviewed and revised to balance reliability and brevity (DeVellis, 2017). Exploratory Factor Analysis (EFA) was used to identify items for removal, aiming for a Cronbach\u0026apos;s Alpha between .80 and .90. A pilot study evaluated the scale\u0026rsquo;s clarity, completion time, and usability, leading to final revisions.\u003c/p\u003e\n\u003cp\u003eThe finalized PTS-A includes 12 items in two sub-dimensions, while the PTS-B consists of 11 items in two sub-dimensions, each with two reverse-coded items. Scoring ranges from 5 to 60 for PTS-A and 5 to 55 for PTS-B, with higher scores indicating greater parental technoference in parent-child interactions.\u003c/p\u003e\n\u003ch2\u003eResearch Ethics\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eIn the planning, implementation, and reporting stages of this study, all the guidelines outlined in the Higher Education Institutions Scientific Research and Publication Ethics Directive were strictly followed. Additionally, before data collection began, ethical approval was obtained from the X University Social and Human Sciences Research Ethics Committee during a meeting held on 19.06.2023, under decision number 2023/5-7.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants were thoroughly informed about the study before data collection, and those willing to participate were asked to complete and sign an informed consent form. In accordance with the Helsinki Declaration, consent was obtained from all participating parents.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants were also informed that they could stop completing the scale or withdraw from the study at any time. The collected data were securely stored on a password-protected computer within the institution. No identifying information that could reveal participants\u0026apos; identities was included in the scale forms or the research report, ensuring confidentiality throughout the process.\u003c/p\u003e\n\u003ch2\u003eParticipants\u003c/h2\u003e\n\u003cp\u003eData were collected in three large Turkish cities during the scale development process. For the pilot study, 32 parents (17 mothers, 15 fathers) with children aged 4\u0026ndash;9 were recruited as a representative sample.\u003c/p\u003e\n\u003cp\u003eFor the Exploratory Factor Analysis (EFA), 151 participants (Study-1) were included, meeting the recommended sample size of at least five times the number of items (Kass \u0026amp; Tinsley, 1979; Tabachnik \u0026amp; Fidell, 2013).\u003c/p\u003e\n\u003cp\u003eTo validate the factor structure, Confirmatory Factor Analysis (CFA) was conducted with 398 participants (Study-2), aligning with sample size guidelines (Cohen et al., 2002; Jackson, 2001). Demographic details for Study-1 and Study-2 are provided in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1. Demographics of the Participants\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"572\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy-1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(EFA; n=151)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy-2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(CFA; n=398)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e80.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e78.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eFather\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild\u0026rsquo;s Age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e4-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e41.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e40.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e6-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e27,4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e8-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e29.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e32.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e75.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e71.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHigh\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e21.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWhen examining Table 1, it is observed that in the first study, 80.8% of the participants were mothers and 19.2% were fathers. Of these parents, 41.4% had children aged 4-5, 28.5% had children aged 6-7, and 29.1% had children aged 8-9. Regarding income levels, 3.3% of the families were categorized as having low income, 75.5% as having medium income, and 21.2% as having high income.\u003c/p\u003e\n\u003cp\u003eIn the second study, 78.4% of the participants were mothers and 21.6% were fathers. Among these parents, 40.5% had children aged 4-5, 27.4% had children aged 6-7, and 32.2% had children aged 8-9. In terms of income, 7% of the families were classified as low-income, 71.9% as medium-income, and 21.1% as high-income.\u003c/p\u003e\n\u003ch2\u003eData Collection\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eFor this study, after obtaining approval from the X University Ethics Committee, data were collected by the researchers in three phases through face-to-face interactions: the pilot study in July 2023 (n=32), the EFA in September 2023 (n=151), and the CFA in October 2023 (n=398). The researchers randomly selected parents with children aged 4-9 from three large cities in Turkey and informed them about the study\u0026rsquo;s purpose. After the parents completed the voluntary consent form, they were asked to fill out a personal information form, along with the PTS-A form (self-evaluation) and the PTS-B form (evaluation of their partner), while the researcher was present. Participants took approximately 10-15 minutes to complete the scales.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003ch2\u003eStudy-1\u003c/h2\u003e\n\u003cp\u003eThe descriptive statistics, correlational analyses, and EFA were conducted using IBM\u0026apos;s SPSS-26 program (IBM Corp., 2019). To confirm the validity of the factor structure for both the PTS-A and the PTS-B, CFA was performed using the AMOS-26 software program (Arbuckle, 2019).\u003c/p\u003e\n\u003cp\u003eA significance value criterion of p \u0026lt; .05 was used in all analyses to establish statistical significance. To minimize the potential impact of outliers on the findings, the data underwent a process of cleansing and outlier management, following the guidelines of Tabachnick and Fidell (2013). After identifying and removing two univariate and two multivariate outliers, further analyses were conducted on the remaining sample, which included 151 cases.\u003c/p\u003e\n\u003ch3\u003eParent Technoference Scale-A Form (PTS-A)\u003c/h3\u003e\n\u003ch4\u003eExploratory Factor Analysis\u003c/h4\u003e\n\u003cp\u003eThe Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy and Bartlett\u0026apos;s Test of Sphericity were conducted to evaluate the suitability of the sample for factor analysis. The KMO value was found to be .88, indicating commendable sample adequacy, while the Chi-Square value for Bartlett\u0026apos;s Test of Sphericity was 858.44 (df = 66, p \u0026lt; .05), suggesting that the correlations between items were sufficient for factor analysis.\u003c/p\u003e\n\u003cp\u003eA preliminary analysis using oblique rotation (direct oblimin) was conducted to identify the primary factors. The analysis revealed two factors with eigenvalues greater than 1, accounting for 59.18% of the total variance. This two-factor solution was deemed appropriate and approved for further analysis. The factor labeled Interaction of Simultaneous Time accounted for 45.56% of the variance, while the factor labeled Interaction of Discipline and Safeness accounted for 13.63% of the variance. The first five items loaded onto the first factor, while the subsequent seven items loaded onto the second factor (See Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2. Factor Analysis Findings of Parent Technoference Scale\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 286px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParent Technoference Scale-A Form\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParent Technoference Scale-B Form\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eFactor 1: Interaction of simultaneous time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eFactor 2:\u003c/p\u003e\n \u003cp\u003eInteraction of discipline and safeness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eFactor 1: Interaction of simultaneous time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eFactor 2: Interaction of discipline and safeness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eItem 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eItem 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eItem 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eItem 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eItem 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eItem 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eItem 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eItem 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eItem 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eItem 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eItem 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eItem 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e.937\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eItem 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eItem 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e.895\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eItem 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eItem 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e.885\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eItem 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e-.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eItem 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e-.878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eItem 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eItem 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eItem 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eItem 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e.824\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eItem 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch4\u003eInternal Consistency Results\u003c/h4\u003e\n\u003cp\u003eThe assessment of internal consistency was performed separately for the entire scale as well as for each of its two components. The internal consistency coefficient for the entire scale was .88, indicating strong reliability. For the subscales, Interaction of Simultaneous Time demonstrated a coefficient of .81, and Interaction of Discipline and Safeness showed a coefficient of .87, both indicating acceptable levels of internal consistency.\u003c/p\u003e\n\u003ch4\u003eConcurrent and Discriminant Validity\u003c/h4\u003e\n\u003cp\u003eTo assess the concurrent and discriminant validity of the PTS-A, the researchers compared participants\u0026apos; scores on the scale with two constructs: one conceptually similar (control items-A form) and another conceptually dissimilar (control items-B form). As shown in Table 3, there was a significant correlation between PTS-A and control items-A (r = .41, p = .001), supporting the scale\u0026rsquo;s concurrent validity. In contrast, the correlation between PTS-B and control items-A was not statistically significant (r = .11, p = .163), providing evidence for the scale\u0026rsquo;s discriminant validity.\u003c/p\u003e\n\u003cp\u003eTable 3. Correlation Between Control Items and Parent Technoference Scales\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl Items-A\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl Items-B\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePTS-A\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePTS-B\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eControl Items-A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eControl Items-B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e.24\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003ePTS-A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e.41\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e.24\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003ePTS-B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e.67\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.45\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e**p \u0026lt;0.05\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003eParent Technoference Scale-B Form (PTS-B)\u003c/h3\u003e\n\u003ch4\u003eExploratory Factor Analysis\u003c/h4\u003e\n\u003cp\u003eThe Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy and Bartlett\u0026apos;s Test of Sphericity were first conducted to evaluate the suitability of the sample for factor analysis. The KMO value was found to be .93, indicating a high level of sampling adequacy, while the Chi-Square value for Bartlett\u0026apos;s Test of Sphericity was 1725.47 (df = 66, p \u0026lt; .001), suggesting strong correlations between items and the appropriateness of the data for factor analysis.\u003c/p\u003e\n\u003cp\u003eA preliminary analysis using oblique rotation (direct oblimin) was performed to identify the main factors. The analysis revealed two factors with eigenvalues greater than 1, explaining a significant portion of the variance, specifically 77.80%. However, Item-6 was excluded from the analysis due to cross-loading on both factors. After removing Item-6, a further component analysis using oblique rotation confirmed the two-factor solution, which explained 79.29% of the total variance.\u003c/p\u003e\n\u003cp\u003eThe two-factor solution was deemed optimal for further investigation. The first factor, Interaction of Simultaneous Time, accounted for 64.93% of the total variance, while the second factor, Interaction of Discipline and Safeness, accounted for 14.36% of the variance. The first five items loaded onto the first factor, while the next six items loaded onto the second factor.\u003c/p\u003e\n\u003ch4\u003eInternal Consistency Results\u003c/h4\u003e\n\u003cp\u003eThe assessment of internal consistency was conducted separately for the entire scale as well as for each of its components. The internal consistency coefficient for the whole scale was found to be .94, indicating high reliability. For the subscales, Interaction of Simultaneous Time demonstrated a coefficient of .92, and Interaction of Discipline and Safeness showed a coefficient of .95, both reflecting strong internal consistency and reliability for the subscales.\u003c/p\u003e\n\u003ch4\u003eConcurrent and Discriminant Validity\u003c/h4\u003e\n\u003ch4\u003eTo evaluate the concurrent and discriminant validity of the PTS-B, the researchers performed a comparative analysis of participants\u0026apos; scores on two constructs: one conceptually similar (control items-B) and another conceptually dissimilar (control items-A). As shown in Table 3, there was a significant correlation between PTS-B and control items-B (r = .67, p = .001), supporting the scale\u0026rsquo;s concurrent validity. However, this correlation was not as strong as anticipated when compared to other findings.\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003eAdditionally, a significant but weaker correlation was found between PTS-A and control items-B (r = .24, p = .003), providing partial evidence of discriminant validity. While the correlation was statistically significant, it was notably weaker than the correlation between PTS-B and control items-B, indicating some support for the scale\u0026apos;s discriminant validity.\u003c/p\u003e\n\u003ch2\u003eStudy-2\u003c/h2\u003e\n\u003ch3\u003eParent Technoference Scale-A Form (PTS-A)\u003c/h3\u003e\n\u003ch4\u003eConfirmatory Factor Analysis\u003c/h4\u003e\n\u003cp\u003eTo assess the validity of the two-dimensional model (Interaction of Simultaneous Time and Interaction of Discipline and Safeness) of the PTS-A, confirmatory factor analyses were conducted using AMOS 26 software (Arbuckle, 2019). The initial two-factor solution demonstrated an adequate fit: \u0026chi;\u0026sup2; (53, N = 398) = 142.879, p = .001; RMSEA = .065, IFI = .949, TLI = .936, CFI = .949. The modification indices suggested that easing parameter restrictions between e10 (Item-10) and e12 (Item-12) could improve the model fit.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIndeed, the model fit improved considerably when the revised analysis included the covariance between the error terms of these two items as a free parameter: \u0026chi;\u0026sup2; (52, N = 398) = 121.342, p = .001; RMSEA = .058, IFI = .961, TLI = .950, CFI = .961. The standard regression weights from this analysis are depicted in Figure 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInternal Consistency Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe assessment of internal consistency was conducted separately for the entire scale as well as for each of its two components. The internal consistency coefficient for the whole scale was found to be .85, indicating good reliability. For the subscales, Interaction of Simultaneous Time exhibited a coefficient of .78, and Interaction of Discipline and Safeness showed a value of .84.\u003c/p\u003e\n\u003ch4\u003eConcurrent and Discriminant Validity\u003c/h4\u003e\n\u003cp\u003eTo assess the concurrent and discriminant validity of the PTS-A, the researchers compared participants\u0026apos; scores on the scale with two constructs: one conceptually similar (control items-A) and another conceptually dissimilar (control items-B). The results shown in Table 4 indicate a significant correlation between PTS-A and control items-A (r = .47, p = .001), providing strong evidence for concurrent validity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, a significant but weaker correlation was found between PTS-A and control items-B (r = .29, p = .001), which was notably less robust than the correlation with control items-A. These findings offer partial support for discriminant validity.\u003c/p\u003e\n\u003cp\u003eTable 4. Correlation Between Control Items and Parent Technoference Scale\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl Items-A\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl Items-B\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePTS-A\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePTS-B\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eControl Items-A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eControl Items-B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e.31\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePTS-A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e.47\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e.29\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePTS-B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e.71\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e.41\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e**p \u0026lt;0.05\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003eParent Technoference Scale-B Form (PTS-B)\u003c/h3\u003e\n\u003ch4\u003eConfirmatory Factor Analysis\u003c/h4\u003e\n\u003cp\u003eTo assess the validity of the two-dimensional model (Interaction of Simultaneous Time and Interaction of Discipline and Safeness) of the PTS-B, confirmatory factor analyses were conducted using AMOS 26 software (Arbuckle, 2019). The initial two-factor solution did not demonstrate an optimal fit: \u0026chi;\u0026sup2; (43, N = 398) = 164.685, p = .001; RMSEA = .084, IFI = .968, TLI = .959, CFI = .968.\u003c/p\u003e\n\u003cp\u003eHowever, based on modification indices, easing parameter restrictions between e11 (Item-11) and e12 (Item-12) improved the model\u0026apos;s fit. The model fit improved significantly when the revised analysis included the covariance between the error terms of these two items as a free parameter: \u0026chi;\u0026sup2; (42, N = 398) = 135.403, p = .001; RMSEA = .075, IFI = .975, TLI = .967, CFI = .975. This adjustment enhanced the model\u0026rsquo;s overall fit. The standard regression weights from this analysis are displayed in Figure 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInternal Consistency Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe assessment of internal consistency was conducted separately for the entire scale as well as for each of its two components. The internal consistency coefficient for the whole scale was found to be .94, indicating excellent reliability. For the subscales, Interaction of Simultaneous Time demonstrated a coefficient of .91, and Interaction of Discipline and Safeness showed a coefficient of .94, both reflecting strong internal consistency and reliability for their respective components.\u003c/p\u003e\n\u003ch4\u003eConcurrent and Discriminant Validity\u003c/h4\u003e\n\u003cp\u003eTo evaluate the concurrent and discriminant validity of the PTS-B, the researchers conducted a comparative analysis of participants\u0026apos; scores on the scale in relation to two constructs: one that is conceptually similar (control items-B) and another that lacks conceptual similarities (control items-A). The findings shown in Table 4 indicate a significant association between PTS-B and control items-B (r = .71, p = .001), providing strong support for the scale\u0026apos;s concurrent validity.\u003c/p\u003e\n\u003cp\u003eAdditionally, a significant but weaker correlation was found between PTS-B and control items-A (r = .29, p = .001), which was notably less robust than the correlation with control items-B. These findings offer partial support for the scale\u0026apos;s discriminant validity.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study aimed to develop a measurement tool to assess the level of technology-induced interruptions in parent-child interactions during daily life. In this context, the PTS was created to determine the technoference levels of parents with children aged 4\u0026ndash;9. The PTS consists of two forms: one where parents assess themselves (PTS-A) and another where they evaluate their partner (PTS-B). Both forms include two reverse-coded items, with PTS-A containing 12 items and PTS-B containing 11 items.\u003c/p\u003e\u003cp\u003eEach form is structured around two factors: Factor 1: Interaction of Simultaneous Time and Factor 2: Interaction of Discipline and Safeness. The first factor, \"Interaction of Simultaneous Time,\" measures behaviors related to family interactions during conversations, meals, sleep, and both indoor and outdoor activities. The second factor, \"Interaction of Discipline and Safeness,\" evaluates parental responses to the child's demands, safety needs, and positive and negative behaviors throughout the day.\u003c/p\u003e\u003cp\u003eTo develop and validate the Parent Technoference Scale, two studies were conducted, with data collected in two phases. In the first study, data from 151 parents were used for EFA, and in the second study, data from 398 parents were used for CFA.\u003c/p\u003e\u003cp\u003eAs a result of the study, the Parent Technoference Scale proved to be highly effective in determining the level of technology-induced interruptions in parents' daily interactions with their children. In terms of reliability, both the A and B forms of the scale, along with their sub-dimensions, demonstrated strong internal consistency. The item correlations for the full scale and its sub-dimensions were within acceptable ranges for both forms. The internal consistency coefficients were also highly satisfactory.\u003c/p\u003e\u003cp\u003eFrom a validity standpoint, both concurrent and discriminant validity values for the A and B forms were robust, confirming that the Parent Technoference Scale is a reliable and valid tool for measuring technoference levels in parents with children aged 4\u0026ndash;9. Additionally, the results of the CFA for both forms confirmed the two-factor structure of the scale, with model fit indices (RMSEA, SRMR, IFI, TLI, and CFI) indicating an adequate fit across parents of children aged 4\u0026ndash;9.\u003c/p\u003e\u003cp\u003eParents play a crucial role in supporting many aspects of their child's development through daily interactions. Routines such as mealtime, sleep, and playtime provide essential opportunities to nurture children's social and emotional growth. However, interruptions caused by technological devices during these critical moments can result in missed opportunities for meaningful engagement. Furthermore, important parenting responsibilities, such as implementing discipline and ensuring the child's safety, may be neglected when parents are excessively occupied with technological devices.\u003c/p\u003e\u003cp\u003eTechnoference -the interference of technology in family interactions-can create tension in both children and parents, disrupt family routines, and affect societal roles. To effectively support a child's social and emotional development, parents need to help their child manage emotions, solve problems, and understand their mental state and behavioral motivations. However, parents who frequently use mobile devices during parent-child activities are less likely to comprehend their child's mental state and intentions (\u0026Ccedil;akır \u0026amp; K\u0026ouml;seli\u0026ouml;ren, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; McDaniel et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; McDaniel \u0026amp; Radesky, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTechnological tools, which have seamlessly integrated into individuals' daily lives, have also introduced changes to parental interactions and practices. Many parents instinctively respond to messages and notifications on their smartphones without fully recognizing the potential for subsequent communication interruptions. The PTS can help raise awareness of these technology-induced disruptions and encourage mindfulness, allowing parents to avoid missing key opportunities for their children's development.\u003c/p\u003e\u003cp\u003eThe developed PTS can be employed in various experimental, descriptive, and correlational studies to assess the effects of technology on the parent-child relationship. The scale has been validated as a reliable and effective tool for researchers to investigate the impact of technology on child development, highlighting its value in understanding how digital interference shapes family dynamics.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLIMITATIONS\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe participants consisted of parents of children aged 4\u0026ndash;9 who were randomly selected from schools in three large cities in Turkey, which limits the generalizability of the findings to a broader population. The data collected were based on self-reported information from the parents regarding both themselves and their partners, which may introduce bias. Furthermore, the analysis results obtained through the developed measurement tool are limited to assessing technoference from the parents' perspectives.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFUTURE RESEARCH\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFuture large-scale and more diverse studies should continue investigating the relationship between parental technoference and parent-child interactions. Additionally, technoference scales should be developed for other age groups, such as middle childhood, late childhood, and adolescence. There is also a need for research focused on creating evidence-based intervention programs designed to reduce technoference in parent-child interactions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompliance With Ethical Standards\u003c/h2\u003e\u003cp\u003e\u003cb\u003eConflict of Interest\u003c/b\u003e The authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\u0026Ouml;.G.K. and Z.S.D. wrote the introduction and discussion sections. All authors were involved in the data collection process.A.K. performed statistical analysis of the data and wrote up the results. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkbağ, M. \u0026amp; Sayıner, B. (2021). The reflections of digital technology: parental technoference and phubbing. \u003cem\u003eHumanistic Perspective, 3\u003c/em\u003e(3):753-778. https://doi.org/10.47793/hp.996220 \u003c/li\u003e\n\u003cli\u003eArbuckle, J. L. (2019). \u003cem\u003eAmos 26.0 User\u0026apos;s Guide\u003c/em\u003e. IBM SPSS.\u003c/li\u003e\n\u003cli\u003eBauer, N. S. (2018). Technoference over time and parenting. \u003cem\u003ePediatric Research,\u003c/em\u003e \u003cem\u003e84 \u003c/em\u003e(2): 157-158. https://doi.org/10.1038/s41390-018-0059-z \u003c/li\u003e\n\u003cli\u003eChamam, S., Forcella, A., Musio, N., Quinodoz, F., \u0026amp; Dimitrova, N. (2024). Effects of digital and non-digital parental distraction on parent-child ınteraction and communication. \u003cem\u003eFrontiers in Child and Adolescent Psychiatry\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e, 1330331. https://doi.org/10.3389/frcha.2024.1330331\u003c/li\u003e\n\u003cli\u003eCohen, J., Cohen, P., West, S. G., \u0026amp; Aiken, L. S. (2002). \u003cem\u003eApplied multiple regression/correlation analysis for the behavioral sciences\u003c/em\u003e. 3rd ed. Routledge. https://doi.org/10.4324/9780203774441 \u003c/li\u003e\n\u003cli\u003e\u0026Ccedil;akır, C., \u0026amp; K\u0026ouml;seli\u0026ouml;ren, M. (2022). Technoference as technology ınterference in the communication process: a study on married couples. \u003cem\u003eErciyes İletişim Dergisi,\u003c/em\u003e \u003cem\u003e9\u003c/em\u003e(2): 609-626. https://doi.org/10.17680/erciyesiletisim.1091267 \u003c/li\u003e\n\u003cli\u003eDedeoğlu, B. B., Taheri, B., Okumus, F., \u0026amp; Gannon, M. (2020). Understanding the ımportance that consumers attach to social media sharing (ISMS): scale development and validation. \u003cem\u003eTourism Management,\u003c/em\u003e 76:103954. https://doi.org/10.1016/j.tourman.2019.103954 \u003c/li\u003e\n\u003cli\u003eDeloitte (2017). \u003cem\u003eGlobal mobil kullanıcı araştırma raporu\u003c/em\u003e. https://www2.deloitte.com/tr/tr/pages/technology-media-and-telecommunications/articles/global-mobile-consumer-survey-2017.html.\u003c/li\u003e\n\u003cli\u003eDeVellis, R. F. (2017). \u003cem\u003eScale development: theory and applications\u003c/em\u003e. 4th ed. Thousand Oaks, CA: Sage.\u003c/li\u003e\n\u003cli\u003eDevine, D. (2023). Antecedents and consequences of parent technology use in parents of young children. Doctoral dissertation, Virginia Polytechnic Institute and State University.\u003c/li\u003e\n\u003cli\u003eDomoff, S. E., Radesky, J. S., Harrison, K., Riley, H., Lumeng, J. C., \u0026amp; Miller, A. L. (2019). A naturalistic study of child and family screen media and mobile device use. \u003cem\u003eJournal of Child and Family Studies,\u003c/em\u003e 28:401-410. https://doi.org/10.1007/s10826-018-1275-1 \u003c/li\u003e\n\u003cli\u003eElias, N., Lemish, D., Dalyot, S., \u0026amp; Floegel, D. (2021). \u0026ldquo;Where are you?\u0026rdquo; an observational exploration of parental technoference in public places in the US and Israel. \u003cem\u003eJournal of Children and Media,\u003c/em\u003e \u003cem\u003e15\u003c/em\u003e(3):376-388. https://doi.org/10.1080/17482798.2020.1815228 \u003c/li\u003e\n\u003cli\u003eFehl-Seward, M. A. (2022). Development of an ınstrument to measure parent and caregiver perspectives of elementary online pedagogy, practices, and supports. Doctoral dissertation, Regent University.\u003c/li\u003e\n\u003cli\u003eFloyd, F. J., \u0026amp; Widaman, K. F. (1995). Factor analysis in the development and refinement of clinical assessment ınstruments. \u003cem\u003ePsychological Assessment,\u003c/em\u003e \u003cem\u003e7\u003c/em\u003e(3): 286-299. https://psycnet.apa.org/doi/10.1037/1040-3590.7.3.286 \u003c/li\u003e\n\u003cli\u003eG\u0026ouml;z\u0026uuml;n Kahraman, \u0026Ouml;., \u0026amp; \u0026Ouml;zdemir, E.S. (2022). Dijital d\u0026uuml;nyanın \u0026ccedil;ocuğun gelişimine etkileri. In \u003cem\u003eDijital \u0026Ccedil;ağda \u0026Ccedil;ocuk ve Ebeveyn Olmak\u003c/em\u003e, Ed. \u0026Ouml;. G\u0026ouml;z\u0026uuml;n Kahraman, p.57-91. Nobel.\u003c/li\u003e\n\u003cli\u003eIBM Corp. (2019). \u003cem\u003eStatistical product and service solutions (IBM SPSS Version, 26)\u003c/em\u003e. http://www.ibm.com/products/spss-statistics.\u003c/li\u003e\n\u003cli\u003eIrwing, P., \u0026amp; Hughes, D. J. (2018). Test development. In \u003cem\u003eThe Wiley handbook of psychometric testing: a multidisciplinary reference on survey, scale and test development\u003c/em\u003e, Eds. P. Irwing, T. Booth, \u0026amp; D. J. Hughes, p. 4-47. Hoboken, NJ: Wiley.\u003c/li\u003e\n\u003cli\u003eJackson, D. L. (2001). Sample size and number of parameter estimates in maximum likelihood confirmatory factor analysis: a monte carlo ınvestigation. \u003cem\u003eStructural EquationModeling, 8\u003c/em\u003e(2):205-223. https://doi.org/10.1207/S15328007SEM0802_3 \u003c/li\u003e\n\u003cli\u003eJi, X., Feng, N., \u0026amp; Cui, L. (2024). The serial mediation role of parent-child attachment and empathy in the relationship between parental technoference and social anxiety. \u003cem\u003eCurrent Psychology\u003c/em\u003e: 1-11. https://doi.org/10.1007/s12144-024-06109-y \u003c/li\u003e\n\u003cli\u003eKnitter, B., \u0026amp; Zemp, M. (2020). Digital family life: a systematic review of the ımpact of parental smartphone use on parent-child ınteractions. \u003cem\u003eDigital Psychology,\u003c/em\u003e \u003cem\u003e1\u003c/em\u003e(1): 29-43. https://doi.org/10.24989/dp.v1i1.1809 \u003c/li\u003e\n\u003cli\u003eKuzu Jafari, K. (2021). Investigation of the relationship between ıntelligence levels and social skills of early childhood children and parenting and digital parenting attitudes of families. Doctoral Thesis, Uludağ University.\u003c/li\u003e\n\u003cli\u003eLauricella, A. R., Cingel, D. P., Beaudoin-Ryan, L., Robb, M. B., Saphir, M., \u0026amp; Wartella, E. A. (2016). \u003cem\u003eThe Common Sense Census: plugged-in parents of tweens and teens\u003c/em\u003e. CA: Common Sense Media.\u003c/li\u003e\n\u003cli\u003eLemish, D., Elias, N., \u0026amp; Floegel, D. (2020). \u0026ldquo;Look at me!\u0026rdquo; Parental use of mobile phones at the playground. \u003cem\u003eMobile Media \u0026amp; Communication,\u003c/em\u003e \u003cem\u003e8\u003c/em\u003e(2):170-187. https://doi.org/10.1177/2050157919846916 \u003c/li\u003e\n\u003cli\u003eLiu, Q., Wu, J., Zhou, Z., \u0026amp; Wang, W. (2020). Parental technoference and smartphone addiction in chinese adolescents: the mediating role of social sensitivity and loneliness. \u003cem\u003eChildren and Youth Services Review,\u003c/em\u003e 118: 105434. https://doi.org/10.1016/j.childyouth.2020.105434 \u003c/li\u003e\n\u003cli\u003eMackay, L. J., Komanchuk, J., Hayden, K. A., \u0026amp; Letourneau, N. (2022). Impacts of parental technoference on parent-child relationships and child health and developmental outcomes: a scoping review protocol. \u003cem\u003eSystematic Reviews,\u003c/em\u003e \u003cem\u003e11\u003c/em\u003e(1): 1-7. https://doi.org/10.17605/OSF.IO/QNTS5 \u003c/li\u003e\n\u003cli\u003eMangan, E., Leavy, J. E., \u0026amp; Jancey, J. (2018). Mobile device use when caring for children 0‐5 years: a naturalistic playground study. \u003cem\u003eHealth Promotion Journal of Australia,\u003c/em\u003e \u003cem\u003e29\u003c/em\u003e(3): 337-343. https://doi.org/10.1002/hpja.38 \u003c/li\u003e\n\u003cli\u003eMcDaniel, B. T. (2015). \u0026ldquo;Technoference\u0026rdquo;: Everyday intrusions and interruptions of technology in couple and family relationships. In C. J. Bruess (Ed.), \u003cem\u003eFamily communication in the age of digital and social media\u003c/em\u003e. Peter Lang Publishing.\u003c/li\u003e\n\u003cli\u003eMcDaniel, B. T. (2020). Technoference: parent mobile device use and ımplications for children and parent-child relationships. \u003cem\u003eZero To Three,\u003c/em\u003e \u003cem\u003e41\u003c/em\u003e(2): 30-36.\u003c/li\u003e\n\u003cli\u003eMcDaniel, B. T., \u0026amp; Coyne, S. M. (2016a). Technoference: the ınterference of technology in couple relationships and ımplications for women\u0026apos;s personal and relational well-being. \u003cem\u003ePsychology of Popular Media Culture,\u003c/em\u003e \u003cem\u003e5\u003c/em\u003e(1):85\u0026ndash;98. https://psycnet.apa.org/doi/10.1037/ppm0000065 \u003c/li\u003e\n\u003cli\u003eMcDaniel, B. T., \u0026amp; Coyne, S. M. (2016b). Technology ınterference in the parenting of young children: ımplications for mothers\u0026rsquo; perceptions of coparenting. \u003cem\u003eThe Social Science Journal,\u003c/em\u003e \u003cem\u003e53\u003c/em\u003e(4): 435-443. https://doi.org/10.1016/j.soscij.2016.04.010 \u003c/li\u003e\n\u003cli\u003eMcDaniel, B. T., \u0026amp; Radesky, J. S. (2018a). Technoference: longitudinal associations between parent technology use, parenting stress, and child behavior problems. \u003cem\u003ePediatric Research,\u003c/em\u003e \u003cem\u003e84\u003c/em\u003e (2): 210-218. https://doi.org/10.1038/s41390-018-0052-6 \u003c/li\u003e\n\u003cli\u003eMcDaniel, B. T., \u0026amp; Radesky, J. S. (2018b). Technoference: parent distraction with technology and associations with child behavior problems. \u003cem\u003eChild Development,\u003c/em\u003e \u003cem\u003e89\u003c/em\u003e (1): 100-109. https://doi.org/10.1111/cdev.12822 \u003c/li\u003e\n\u003cli\u003eMcDaniel, B. T., Galovan, A. M., Cravens, J. D., \u0026amp; Drouin, M. (2018). \u0026lsquo;Technoference\u0026rsquo; and implications for mothers\u0026apos; and fathers\u0026apos; couple and coparenting relationship quality. \u003cem\u003eComputers in Human Behavior,\u003c/em\u003e 80:303-313. https://doi.org/10.1016/j.chb.2017.11.019 \u003c/li\u003e\n\u003cli\u003eMeeus, A., Coenen, L., Eggermont, S., \u0026amp; Beullens, K. (2021). Family technoference: exploring parent mobile device distraction from children\u0026rsquo;s perspectives. \u003cem\u003eMobile Media \u0026amp; Communication, 9\u003c/em\u003e(3):584-604. https://doi.org/10.1177/2050157921991602 \u003c/li\u003e\n\u003cli\u003eMobil İletişim Ara\u0026ccedil;ları ve Bilgi Teknolojileri İş Adamları Derneği (MOBİSAD) (2020). \u003cem\u003eMobil iletişim sekt\u0026ouml;r raporu\u003c/em\u003e. https://mobisad.org/dergi/mobisad-17/pdf/mobisad-17.pdf.\u003c/li\u003e\n\u003cli\u003eMorris, A. J., Filippetti, M. L., \u0026amp; Rigato, S. (2022). The impact of parents\u0026rsquo; smartphone use on language development in young children. \u003cem\u003eChild Development Perspectives,\u003c/em\u003e \u003cem\u003e16 \u003c/em\u003e(2): 103-109. https://doi.org/10.1111/cdep.12449 \u003c/li\u003e\n\u003cli\u003eNardi, P. M. (2018). \u003cem\u003eDoing survey research: a guide to quantitative methods\u003c/em\u003e (4th ed.)\u003cem\u003e.\u003c/em\u003e Routledge.\u003c/li\u003e\n\u003cli\u003ePoulain, T., Ludwig, J., Hiemisch, A., Hilbert, A., \u0026amp; Kiess, W. (2019). Media use of mothers, media use of children, and parent\u0026ndash;child interaction are related to behavioral difficulties and strengths of children. \u003cem\u003eInternational Journal of Environmental Research and Public Health,\u003c/em\u003e \u003cem\u003e16\u003c/em\u003e(23): 4651. https://doi.org/10.3390/ijerph16234651 \u003c/li\u003e\n\u003cli\u003ePrice, L. R. (2017). \u003cem\u003ePsychometric methods: theory into practice\u003c/em\u003e. The Guilford Press.\u003c/li\u003e\n\u003cli\u003eQiao, L., \u0026amp; Liu, Q. (2020). The effect of technoference in parent-child relationships on adolescent smartphone addiction: the role of cognitive factors. \u003cem\u003eChildren and Youth Services Review,\u003c/em\u003e 118: 105340. https://doi.org/10.1016/j.childyouth.2020.105340 \u003c/li\u003e\n\u003cli\u003eRoberts, J. A., \u0026amp; David, M. E. (2016). My life has become a major distraction from my cell phone: partner phubbing and relationship satisfaction among romantic partners. \u003cem\u003eComputers in Human Behavior,\u003c/em\u003e 54:134\u0026ndash;141. https://doi.org/10.1016/j.chb.2015.07.058 \u003c/li\u003e\n\u003cli\u003eShao, T., Zhu, C., Lei, H., Jiang, Y., Wang, H., \u0026amp; Zhang, C. (2024). The relationship of parent-child technoference and child problematic smartphone use: the roles of parent-child relationship, negative parenting styles, and children\u0026rsquo;s gender. \u003cem\u003ePsychology Research and Behavior Management,\u003c/em\u003e 17: 2067. https://doi.org/10.2147/PRBM.S456411 \u003c/li\u003e\n\u003cli\u003eStockdale, L. A., Coyne, S. M., \u0026amp; Padilla-Walker, L. M. (2018). Parent and child technoference and socioemotional behavioral outcomes: a nationally representative study of 10-to 20-year-old adolescents. \u003cem\u003eComputers in Human Behavior,\u003c/em\u003e 88: 219-226. https://doi.org/10.1016/j.chb.2018.06.034 \u003c/li\u003e\n\u003cli\u003eTabachnick, B. G., \u0026amp; Fidell, L. S. (2013). \u003cem\u003eUsing multivariate statistics.\u003c/em\u003e 6th ed. Pearson Education.\u003c/li\u003e\n\u003cli\u003eWilliams, J., Gazley, A., \u0026amp; Ashill, N. (2021). Children\u0026rsquo;s perceived value: conceptualization, scale development, and validation. \u003cem\u003eJournal of Retailing,\u003c/em\u003e \u003cem\u003e97\u003c/em\u003e (2): 301\u0026ndash;315. https://doi.org/10.1016/j.jretai.2020.05.008\u003c/li\u003e\n\u003cli\u003eYang, X., Jiang, P., \u0026amp; Zhu, L. (2023). Parental problematic smartphone use and children\u0026apos;s executive function: The mediating role of technoference and the moderating role of children\u0026apos;s age. \u003cem\u003eEarly Childhood Research Quarterly\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e:219-227.https://doi.org/10.1016/j.ecresq.2022.12.017\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"technology, technoference, parenting, parent-child relationship, scale","lastPublishedDoi":"10.21203/rs.3.rs-7155648/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7155648/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to develop a valid and reliable measurement tool to evaluate the impact of technology-induced interruptions (parental technoference) on interactions between parents and their children aged 4\u0026ndash;9. Parent Technoference Scale (PTS) items were rated on a five-point Likert scale (1 to 5), and both forms included three control items. A total of 549 parents with children aged 4\u0026ndash;9 participated in the study. Data were collected from 151 participants for exploratory factor analysis (Study-1) and from 398 participants for confirmatory factor analysis (Study-2). Following these analyses, PTS-A was refined to 12 items, and PTS-B to 11 items, with both forms including two reverse-scored items. Factor analysis revealed two distinct sub-dimensions in each form: Interaction of Simultaneous Time and Interaction of Discipline and Safeness. Higher scores on the scale indicate greater levels of parental technoference. The results demonstrated that the Parent Technoference Scale is a robust tool for assessing the degree of technology-induced disruptions in parent-child interactions. Both PTS-A and PTS-B showed high reliability, with Cronbach's alpha values exceeding 0.80 for PTS-A and 0.90 for PTS-B. The internal consistency of both forms and their sub-dimensions was strong, with acceptable item correlations. Furthermore, the scale showed excellent concurrent and discriminant validity.\u003c/p\u003e","manuscriptTitle":"Development of a New Measurement Tool to Evaluate the Impact of Technology on Parents' Interactions with Their Children: The Parent Technoference Scale","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 11:59:14","doi":"10.21203/rs.3.rs-7155648/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9d992a56-add4-4b01-be69-7aa3054f9ec9","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-17T17:20:00+00:00","index":68,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-09T11:59:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 11:59:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7155648","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7155648","identity":"rs-7155648","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}